What can code-index-mcp do?

dual-strategy codebase indexing with shallow and deep modes, multi-strategy code search with regex, fuzzy matching, and semantic filtering, language-specific parsing strategy selection with fallback chains, context-aware symbol search with scope and type filtering, project statistics and code metrics generation, dependency graph extraction and relationship analysis, file-level code summarization and structural analysis, mcp protocol server with stdio transport and tool registration, project path management with context persistence, incremental index refresh with file change detection, tree-sitter ast parsing with language-specific symbol extraction, file discovery and listing with type-based filtering, configuration management and tool discovery, error handling and tool response normalization

code-index-mcp

MCP ServerFree

A Model Context Protocol (MCP) server that helps large language models index, search, and analyze code repositories with minimal setup

Open Source

/ 100

14 capabilities

Capabilities14 decomposed

dual-strategy codebase indexing with shallow and deep modes

Medium confidence

Implements a two-tier indexing strategy where shallow indexing rapidly builds file lists via filesystem traversal, while deep indexing extracts symbol-level structure (functions, classes, variables) using tree-sitter AST parsing for 50+ file types with fallback regex strategies. The indexing system uses SQLite for symbol storage and JSON for file metadata, enabling LLMs to understand codebase structure without full source transmission. Supports incremental updates and file watching for auto-refresh on changes.

Solves for

I need to give Claude access to my entire codebase structure without uploading gigabytes of source codeI want to index a large monorepo and search for specific functions or classes across multiple filesI need the index to stay fresh as my code changes without manual re-indexing

Best for

developers using Claude Desktop or MCP-compatible IDEs who want codebase-aware AI assistance

teams managing large monorepos (100+ files) needing efficient symbol extraction

solo developers building LLM agents that need to reason about code structure

Requires

Python 3.9+

tree-sitter library with language bindings for target languages

Local filesystem read access to codebase

Limitations

Tree-sitter parsing adds ~500ms-2s overhead per large file depending on language complexity

SQLite symbol storage requires local disk access; no cloud-based index sharing

Fallback regex strategies for unsupported languages may miss nested or complex symbol definitions

What makes it unique

Uses tree-sitter AST parsing for 50+ languages with intelligent fallback regex strategies, enabling structurally-aware symbol extraction without language-specific compiler dependencies. Dual-mode indexing (shallow for speed, deep for accuracy) allows LLMs to choose between fast file discovery and detailed symbol analysis.

vs alternatives

Faster and more accurate than regex-only indexing (e.g., ctags) because tree-sitter understands syntax trees; more practical than full-source RAG because it extracts only symbols, reducing context window usage by 80-90%.

multi-strategy code search with regex, fuzzy matching, and semantic filtering

Medium confidence

Exposes search_code_advanced tool that combines regex pattern matching, fuzzy string matching, and file type filtering to locate code across indexed repositories. Searches operate against both the symbol database (for function/class names) and file contents (for code patterns). Supports complex queries like 'find all async functions in TypeScript files' through composable filter chains. Results include file paths, line numbers, and context snippets.

Solves for

I need to find all usages of a deprecated function across my codebaseI want to search for code patterns matching a regex without loading the entire repo into contextI need to locate all test files or configuration files matching specific naming conventions

Best for

developers refactoring large codebases and needing precise code location

LLM agents performing code analysis tasks that require targeted symbol discovery

teams auditing code for security patterns or compliance violations

Requires

Completed deep index (build_deep_index must be run first)

Valid regex patterns or fuzzy search strings

File type filters must match indexed file extensions

Limitations

Regex search performance degrades on very large files (>10MB); no streaming results

Fuzzy matching may return false positives for short search terms (< 3 characters)

Search operates on indexed snapshot; real-time file changes require index refresh

What makes it unique

Combines three independent search strategies (regex, fuzzy, file filtering) into a single composable query interface, allowing LLMs to mix-and-match strategies without multiple tool calls. Searches both symbol database and file contents, enabling both structural and textual code discovery.

vs alternatives

More flexible than grep/ripgrep because it understands symbol boundaries and file types; faster than full-text search because it leverages pre-built symbol index for structural queries.

language-specific parsing strategy selection with fallback chains

Medium confidence

Implements an intelligent parser selection system that chooses the best parsing strategy for each language based on availability and accuracy. For languages with tree-sitter bindings (Python, JavaScript, TypeScript, Go, Rust, Java, C++, etc.), uses AST parsing. For unsupported languages, falls back to regex-based heuristics. Fallback strategies are language-specific (e.g., Bash uses different patterns than SQL). Parsing results are cached to avoid re-parsing identical files.

Solves for

I have a polyglot codebase with 10+ languages and need consistent symbol extractionI want to index languages that don't have tree-sitter support using best-effort regexI need fast parsing without installing language-specific compilers or tools

Best for

teams managing polyglot monorepos (microservices, multi-language projects)

developers who want to index code without language-specific tooling

LLM agents that need to handle arbitrary code files

Requires

tree-sitter library for supported languages

Python regex engine for fallback strategies

Language identification (file extension or content-based)

Limitations

Regex fallback is less accurate than AST parsing; may miss nested or complex definitions

Fallback strategies are heuristic-based and may produce false positives/negatives

Adding new language support requires implementing regex patterns or tree-sitter bindings

What makes it unique

Implements fallback chain that gracefully degrades from AST parsing to regex heuristics, enabling symbol extraction for any language without external dependencies. Caches parsing results to avoid re-parsing identical files across multiple queries.

vs alternatives

More practical than requiring language-specific tools because it works with Python bindings only; more accurate than pure regex because it uses AST when available.

context-aware symbol search with scope and type filtering

Medium confidence

Extends basic search with semantic awareness by filtering results by symbol type (function, class, variable, import) and scope (global, module-level, nested). Allows queries like 'find all async functions' or 'find all class methods named init'. Leverages symbol metadata extracted during indexing (type, scope, decorators) to filter results without post-processing. Results include full symbol context (definition location, signature, scope chain).

Solves for

I need to find all async functions in my codebase to understand concurrency patternsI want to locate all class constructors (__init__, constructor) across multiple languagesI need to find all global variables to identify potential state management issues

Best for

code auditors looking for specific patterns (async functions, global state, etc.)

LLM agents performing code analysis tasks that require semantic understanding

developers refactoring code and needing to understand symbol relationships

Requires

Completed deep index with symbol metadata

Valid symbol type and scope filters

Limitations

Type and scope filtering depends on accurate parsing; regex fallback may miss metadata

Scope detection is limited to syntactic scope; does not understand runtime scope changes

Decorator and annotation filtering is language-specific; not all languages supported equally

What makes it unique

Combines pattern matching with semantic filtering based on symbol metadata extracted during indexing. Enables high-precision searches without post-processing or AST traversal at query time.

vs alternatives

More precise than grep because it understands symbol types and scopes; faster than runtime analysis because it uses pre-computed metadata.

project statistics and code metrics generation

Medium confidence

Provides get_project_stats tool that analyzes the indexed codebase to generate aggregate metrics: total files, lines of code per language, symbol counts (functions, classes, variables), file size distribution, and complexity estimates. Metrics are computed from the index without re-parsing. Supports filtering by language, file type, or directory. Useful for understanding codebase scale and composition.

Solves for

I need to understand the size and composition of my codebase (how many lines of code, how many functions)I want to compare code metrics across languages in my monorepoI need to identify which parts of my codebase are largest or most complex

Best for

engineering managers assessing codebase health and scale

developers planning refactoring efforts and understanding scope

LLM agents gathering context about project size before analysis

Requires

Completed index (shallow or deep)

Valid language or file type filters

Limitations

Metrics are based on indexed snapshot; real-time changes require index refresh

Complexity estimates are heuristic-based (e.g., function count, nesting depth) not true cyclomatic complexity

Line-of-code counts may include comments and blank lines depending on parser

What makes it unique

Generates metrics from pre-computed index without re-parsing, enabling fast statistics generation even for large codebases. Supports filtering by language, file type, and directory for granular analysis.

vs alternatives

Faster than tools like cloc because it uses indexed data; more accurate than line-counting tools because it understands symbol structure.

dependency graph extraction and relationship analysis

Medium confidence

Analyzes import statements and symbol references to build a dependency graph showing relationships between files and modules. Extracts import/require statements from indexed code to identify direct dependencies. Supports language-specific import syntax (Python import/from, JavaScript import/require, Go import, etc.). Can compute transitive dependencies and identify circular dependencies. Results are returned as graph data structure suitable for visualization or further analysis.

Solves for

I need to understand the dependency structure of my codebase to plan refactoringI want to detect circular dependencies that might indicate architectural issuesI need to trace how changes in one module affect other modules

Best for

architects analyzing codebase structure and dependencies

developers planning modularization or microservices migration

LLM agents understanding code relationships before refactoring

Requires

Completed deep index with import statements extracted

Language support for import statement parsing

Limitations

Dependency extraction is limited to static imports; misses dynamic requires or lazy loading

External dependencies (npm packages, PyPI modules) are identified but not resolved

Transitive dependency computation can be expensive for large graphs (O(n²) in worst case)

What makes it unique

Extracts dependency relationships from indexed import statements without executing code or resolving external packages. Supports language-specific import syntax and can compute transitive dependencies efficiently.

vs alternatives

More practical than runtime dependency analysis because it works without executing code; more accurate than static analysis tools because it uses parsed AST instead of regex.

file-level code summarization and structural analysis

Medium confidence

The get_file_summary tool generates concise summaries of individual source files by analyzing their AST structure to extract top-level definitions (functions, classes, imports, exports). Summaries include symbol lists with signatures, dependency information, and file-level documentation. Uses tree-sitter parsing to understand code structure without executing or compiling, producing machine-readable output suitable for LLM context windows.

Solves for

I need a quick overview of what functions and classes are defined in a file without reading the entire sourceI want to understand a file's dependencies and public API surfaceI need to generate context for an LLM to understand a file's purpose before asking it to modify the code

Best for

LLM agents preparing context before code generation or refactoring tasks

developers navigating unfamiliar codebases quickly

documentation generation pipelines that need automated file summaries

Requires

File must be in indexed codebase

Language must have tree-sitter parser or fallback regex strategy

File must be readable (not binary)

Limitations

Summaries are structural only; do not include docstring content or inline comments

Complex nested structures (closures, decorators) may be simplified or omitted

Performance scales with file size; very large files (>50KB) may timeout

What makes it unique

Generates summaries by parsing AST rather than regex or heuristics, ensuring accurate symbol extraction even in complex nested code. Output is optimized for LLM consumption (JSON-structured, concise) rather than human reading.

vs alternatives

More accurate than comment-based summaries because it extracts actual code structure; more efficient than sending full file content because summaries are 5-20% of original size while retaining 90% of structural information.

mcp protocol server with stdio transport and tool registration

Medium confidence

Implements a FastMCP server that exposes 15+ code intelligence tools through the Model Context Protocol, communicating with MCP clients (Claude Desktop, Codex CLI) via stdio transport. All tools are decorated with @mcp.tool() and wrapped with @handle_mcp_tool_errors for consistent error handling. The server manages a CodeIndexerContext object that provides shared state (index managers, services, configuration) across all tool invocations, enabling stateful operations like maintaining an active project path.

Solves for

I want to integrate code intelligence into Claude Desktop without building a custom APII need to expose my indexing and search tools to any MCP-compatible clientI want consistent error handling and tool discovery across all code analysis operations

Best for

developers using Claude Desktop or other MCP-compatible IDEs

teams building custom MCP clients that need code intelligence

organizations standardizing on MCP for LLM tool integration

Requires

Python 3.9+

FastMCP library

MCP-compatible client application

Limitations

Stdio transport is single-threaded; concurrent tool calls are serialized

No built-in authentication; relies on OS-level process isolation

Tool discovery requires MCP client to parse tool definitions; no dynamic capability negotiation

What makes it unique

Uses FastMCP framework with decorator-based tool registration (@mcp.tool()), reducing boilerplate compared to manual JSON-RPC handling. Centralized error handling via @handle_mcp_tool_errors decorator ensures all tools return consistent error responses without per-tool try-catch blocks.

vs alternatives

Simpler than building a custom REST API because MCP handles protocol negotiation and transport; more reliable than direct LLM API calls because MCP enforces schema validation and error handling.

project path management with context persistence

Medium confidence

Provides set_project_path and get_project_path tools that manage the active codebase context across tool invocations. The project path is stored in CodeIndexerContext and persists for the lifetime of the MCP server process, allowing subsequent search and indexing operations to operate on the correct codebase without re-specifying the path. Validates paths exist and are readable before accepting them.

Solves for

I want to set a working directory once and have all subsequent tools operate on that codebaseI need to switch between multiple projects in a single MCP sessionI want to verify that my project path is correctly configured before running indexing operations

Best for

LLM agents managing multi-step code analysis workflows

developers switching between projects in Claude Desktop

automation scripts that need to operate on different codebases sequentially

Requires

Valid filesystem path

Read permissions on target directory

MCP server running and accessible

Limitations

Project path is session-scoped; lost when MCP server restarts

No project configuration file support; path must be set programmatically

Changing project path does not automatically invalidate existing indexes; must call refresh_index

What makes it unique

Maintains project context in CodeIndexerContext object shared across all tool invocations, eliminating need to pass project path to every tool call. Enables stateful workflows where LLM agents can set context once and reuse it across multiple operations.

vs alternatives

More convenient than REST APIs that require path parameter on every request; more reliable than environment variables because context is explicitly managed and validated.

incremental index refresh with file change detection

Medium confidence

The refresh_index tool rebuilds the symbol index for changed files only, using file modification timestamps to detect changes since last index. Implements file watching via watchdog library (or polling fallback) to automatically trigger refreshes when files change. Supports both manual refresh (on-demand) and automatic refresh (background monitoring). Preserves unchanged file entries in the index, reducing reprocessing overhead.

Solves for

I want my code index to stay current as I edit files without manually re-indexingI need to refresh the index after pulling changes from gitI want to detect which files have changed since the last index build

Best for

developers using code-index-mcp in long-running IDE sessions

CI/CD pipelines that need to index code after checkout or merge

LLM agents that need to maintain accurate codebase state across multiple interactions

Requires

Existing index (build_deep_index must be run first)

Filesystem that supports modification timestamps

watchdog library for file monitoring (optional; falls back to polling)

Limitations

File watching uses polling on some systems (macOS, Windows), consuming CPU on large repos

Timestamp-based detection misses changes that don't update mtime (e.g., git checkout with --no-touch)

Refresh is blocking; large file changes may pause tool responses for seconds

What makes it unique

Uses timestamp-based change detection combined with optional file watching to minimize reprocessing. Incremental refresh preserves unchanged entries, reducing index rebuild time from O(n) to O(changes) for large repos.

vs alternatives

More efficient than full re-indexing because it only reprocesses changed files; more reliable than git-based change detection because it works with uncommitted changes and non-git directories.

tree-sitter ast parsing with language-specific symbol extraction

Medium confidence

Implements a pluggable parsing system using tree-sitter library to extract symbols (functions, classes, variables, imports) from source code by traversing the Abstract Syntax Tree. Supports 50+ languages with language-specific parsers (Python, JavaScript, TypeScript, Go, Rust, Java, C++, etc.). For unsupported languages, falls back to regex-based heuristics. Parsing results are cached and stored in SQLite for fast retrieval. Each parser extracts language-specific metadata (e.g., async/await markers, decorators, type annotations).

Solves for

I need to extract all function definitions from a Python file with their signatures and decoratorsI want to find all class definitions across multiple languages in a monorepoI need to understand import dependencies to trace code relationships

Best for

developers working with polyglot codebases (multiple languages)

LLM agents that need accurate symbol extraction without language-specific compilers

code analysis tools that require structural understanding of source code

Requires

tree-sitter Python library

Language-specific tree-sitter bindings (e.g., tree-sitter-python, tree-sitter-javascript)

Source files must be readable text (not binary)

Limitations

Tree-sitter parsing adds 500ms-2s latency per large file depending on language complexity

Language bindings must be installed separately; missing bindings fall back to regex (less accurate)

Regex fallback misses nested or complex definitions (e.g., closures, decorators in Python)

What makes it unique

Uses tree-sitter for structural parsing across 50+ languages with intelligent fallback to regex heuristics for unsupported languages. Caches parsed results in SQLite, enabling fast symbol lookups without re-parsing on every query.

vs alternatives

More accurate than regex-only parsing because tree-sitter understands syntax trees; more practical than language-specific compilers because it requires no build tools or dependencies beyond Python bindings.

file discovery and listing with type-based filtering

Medium confidence

The find_files tool performs filesystem traversal to locate files matching specified criteria (name patterns, file types, size constraints). Supports glob patterns, regex matching, and file type filters (e.g., 'all Python files', 'all test files'). Results include file paths, sizes, and modification times. Operates on the indexed file list for fast retrieval without filesystem I/O. Supports exclusion patterns to skip directories like node_modules, .git, __pycache__.

Solves for

I need to find all test files in my codebase to understand test coverageI want to locate all configuration files (package.json, pyproject.toml, etc.) across a monorepoI need to find large files that might be performance bottlenecks

Best for

developers auditing codebase structure and file organization

LLM agents gathering context about project layout before analysis

build systems and CI/CD pipelines that need to discover files matching patterns

Requires

Completed shallow index (set_project_path must be called first)

Valid glob or regex pattern

Limitations

File discovery operates on indexed snapshot; real-time filesystem changes require index refresh

Glob patterns are limited to standard syntax; no advanced regex features

No support for symlinks or hard links; treats them as regular files

What makes it unique

Combines glob pattern matching with file type filtering and exclusion patterns in a single query, enabling complex file discovery without multiple tool calls. Operates on indexed file list for O(1) lookup performance instead of filesystem traversal.

vs alternatives

Faster than shell find/locate because it uses pre-indexed file list; more flexible than simple glob matching because it supports type-based and size-based filtering.

configuration management and tool discovery

Medium confidence

Manages MCP server configuration through JSON files (.well-known/mcp.json, fastmcp.json) that define tool schemas, transport settings, and discovery metadata. Supports environment variable substitution for API keys and paths. Exposes get_config and set_config tools for runtime configuration updates. Configuration is validated against JSON schema to ensure tool definitions are correct. Enables MCP clients to discover available tools and their parameters without hardcoding.

Solves for

I need to configure which languages to parse and which file types to indexI want to set up environment-specific settings (dev vs. production indexing strategies)I need to discover what tools are available in the MCP server and their parameters

Best for

MCP client developers integrating code-index-mcp into their applications

DevOps teams deploying code-index-mcp with environment-specific settings

developers customizing indexing behavior for their specific codebase

Requires

JSON configuration files in project root or .well-known directory

Write permissions to configuration files

Valid JSON syntax

Limitations

Configuration changes require server restart to take effect (no hot-reload)

Environment variable substitution is limited to simple ${VAR} syntax

No validation of configuration values; invalid settings may cause silent failures

What makes it unique

Centralizes tool definitions in JSON schema files that serve dual purposes: MCP client discovery and runtime validation. Environment variable substitution enables deployment-time configuration without code changes.

vs alternatives

More flexible than hardcoded tool definitions because configuration is externalized; more reliable than environment-only configuration because schema validation catches errors early.

error handling and tool response normalization

Medium confidence

Implements @handle_mcp_tool_errors decorator that wraps all MCP tool functions to catch exceptions and convert them to MCPToolError responses with consistent formatting. Provides detailed error messages including stack traces (in debug mode) and user-friendly summaries. Normalizes error responses across all tools so MCP clients receive consistent error format regardless of which tool fails. Logs errors for debugging and monitoring.

Solves for

I need consistent error handling across all code intelligence toolsI want detailed error messages when tools fail so I can debug issuesI need to ensure MCP clients receive properly formatted error responses

Best for

MCP client developers building robust integrations

DevOps teams monitoring code-index-mcp in production

developers debugging tool failures and understanding root causes

Requires

Python logging configuration

MCP client that can handle MCPToolError responses

Limitations

Error details are limited to exception messages; no structured error codes

Stack traces are only included in debug mode; production errors are summarized

No error recovery or retry logic; failed tools return errors immediately

What makes it unique

Centralized error handling via decorator pattern eliminates per-tool try-catch blocks and ensures all tools return consistent error format. Supports debug mode for detailed diagnostics while keeping production errors concise.

vs alternatives

More maintainable than per-tool error handling because logic is centralized; more user-friendly than raw exception messages because errors are formatted for MCP protocol.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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multi-language-code-indexing

1 shared capability

Best For

✓developers using Claude Desktop or MCP-compatible IDEs who want codebase-aware AI assistance
✓teams managing large monorepos (100+ files) needing efficient symbol extraction
✓solo developers building LLM agents that need to reason about code structure
✓developers refactoring large codebases and needing precise code location
✓LLM agents performing code analysis tasks that require targeted symbol discovery
✓teams auditing code for security patterns or compliance violations
✓teams managing polyglot monorepos (microservices, multi-language projects)
✓developers who want to index code without language-specific tooling

Known Limitations

⚠Tree-sitter parsing adds ~500ms-2s overhead per large file depending on language complexity
⚠SQLite symbol storage requires local disk access; no cloud-based index sharing
⚠Fallback regex strategies for unsupported languages may miss nested or complex symbol definitions
⚠File watching uses polling on some systems, consuming CPU on very large repos (10k+ files)
⚠Regex search performance degrades on very large files (>10MB); no streaming results
⚠Fuzzy matching may return false positives for short search terms (< 3 characters)

Requirements

Python 3.9+tree-sitter library with language bindings for target languagesLocal filesystem read access to codebaseMCP client compatible with stdio transport (Claude Desktop, Codex CLI, or custom)Completed deep index (build_deep_index must be run first)Valid regex patterns or fuzzy search stringsFile type filters must match indexed file extensionstree-sitter library for supported languages

Input / Output

Accepts: local filesystem paths, file type filters (e.g., '.py', '.js', '.ts'), configuration JSON for indexing strategies, regex pattern strings, fuzzy search keywords, file type filters (e.g., '.py', '.js'), scope filters (function, class, variable), source code text, language identifier (auto-detected from file extension), search pattern (regex or fuzzy), symbol type filter (function, class, variable, import, etc.), scope filter (global, module, nested, etc.), optional: language filter, optional: file type filter, optional: directory filter, optional: starting file or module for dependency trace, optional: depth limit for transitive dependencies, file path (relative to project root), optional: summary detail level (basic, detailed), MCP tool call requests (JSON-RPC format), tool arguments as JSON objects, absolute or relative filesystem path, optional: validation flags, optional: file paths to refresh (if not provided, scans all indexed files), optional: refresh mode (manual, auto, background), language identifier (e.g., 'python', 'javascript'), optional: parsing options (include comments, include docstrings), glob pattern (e.g., '*.py', 'src/**/*.ts'), file type filter (e.g., 'python', 'javascript'), optional: size constraints (min/max bytes), optional: exclusion patterns, JSON configuration objects, environment variable names, configuration keys and values, exceptions raised by tool functions, debug mode flag

Produces: SQLite database with symbol tables, JSON file lists with metadata, structured symbol objects (name, type, line number, scope), list of matched file paths with line numbers, code context snippets (surrounding lines), symbol metadata (type, scope, definition location), list of extracted symbols with type and location, parsing strategy used (tree-sitter or regex), confidence score for regex-based results, filtered list of matching symbols, symbol metadata (type, scope, signature, location), context snippets showing symbol definition, JSON object with aggregate metrics, per-language breakdowns, file size and complexity distributions, symbol count statistics, graph data structure (nodes=files, edges=imports), list of circular dependencies, dependency statistics (average degree, depth), JSON representation suitable for visualization, JSON object with file metadata, list of top-level symbols with types and signatures, import/export statements, file-level statistics (lines of code, complexity estimate), MCP tool responses (JSON-RPC format), structured tool results or error objects, MCPToolError for exceptions, confirmation of set path, current project path string, path validation status, list of refreshed files, count of added/modified/deleted files, index statistics (total symbols, file count), list of symbol objects with type, name, line number, scope, AST node references for further analysis, language-specific metadata (decorators, type annotations, async markers), list of matching file paths, file metadata (size, modification time, type), match count and statistics, current configuration as JSON, tool schema definitions, validation status and errors, MCPToolError response with message and optional stack trace, log entries for error tracking

UnfragileRank

Adoption23%(30% weight)

Quality53%(25% weight)

Ecosystem50%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

14 capabilities

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Repository Details

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License

Last commit: Apr 8, 2026

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A Model Context Protocol (MCP) server that helps large language models index, search, and analyze code repositories with minimal setup

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Capabilities14 decomposed

dual-strategy codebase indexing with shallow and deep modes

Medium confidence

Solves for

Best for

developers using Claude Desktop or MCP-compatible IDEs who want codebase-aware AI assistance

teams managing large monorepos (100+ files) needing efficient symbol extraction

solo developers building LLM agents that need to reason about code structure

Requires

Python 3.9+

tree-sitter library with language bindings for target languages

Local filesystem read access to codebase

Limitations

Tree-sitter parsing adds ~500ms-2s overhead per large file depending on language complexity

SQLite symbol storage requires local disk access; no cloud-based index sharing

Fallback regex strategies for unsupported languages may miss nested or complex symbol definitions

What makes it unique

vs alternatives

multi-strategy code search with regex, fuzzy matching, and semantic filtering

Medium confidence

Solves for

Best for

developers refactoring large codebases and needing precise code location

LLM agents performing code analysis tasks that require targeted symbol discovery

teams auditing code for security patterns or compliance violations

Requires

Completed deep index (build_deep_index must be run first)

Valid regex patterns or fuzzy search strings

File type filters must match indexed file extensions

Limitations

Regex search performance degrades on very large files (>10MB); no streaming results

Fuzzy matching may return false positives for short search terms (< 3 characters)

Search operates on indexed snapshot; real-time file changes require index refresh

What makes it unique

vs alternatives

More flexible than grep/ripgrep because it understands symbol boundaries and file types; faster than full-text search because it leverages pre-built symbol index for structural queries.

language-specific parsing strategy selection with fallback chains

Medium confidence

Solves for

Best for

teams managing polyglot monorepos (microservices, multi-language projects)

developers who want to index code without language-specific tooling

LLM agents that need to handle arbitrary code files

Requires

tree-sitter library for supported languages

Python regex engine for fallback strategies

Language identification (file extension or content-based)

Limitations

Regex fallback is less accurate than AST parsing; may miss nested or complex definitions

Fallback strategies are heuristic-based and may produce false positives/negatives

Adding new language support requires implementing regex patterns or tree-sitter bindings

What makes it unique

vs alternatives

More practical than requiring language-specific tools because it works with Python bindings only; more accurate than pure regex because it uses AST when available.

context-aware symbol search with scope and type filtering

Medium confidence

Solves for

Best for

code auditors looking for specific patterns (async functions, global state, etc.)

LLM agents performing code analysis tasks that require semantic understanding

developers refactoring code and needing to understand symbol relationships

Requires

Completed deep index with symbol metadata

Valid symbol type and scope filters

Limitations

Type and scope filtering depends on accurate parsing; regex fallback may miss metadata

Scope detection is limited to syntactic scope; does not understand runtime scope changes

Decorator and annotation filtering is language-specific; not all languages supported equally

What makes it unique

Combines pattern matching with semantic filtering based on symbol metadata extracted during indexing. Enables high-precision searches without post-processing or AST traversal at query time.

vs alternatives

More precise than grep because it understands symbol types and scopes; faster than runtime analysis because it uses pre-computed metadata.

project statistics and code metrics generation

Medium confidence

Solves for

Best for

engineering managers assessing codebase health and scale

developers planning refactoring efforts and understanding scope

LLM agents gathering context about project size before analysis

Requires

Completed index (shallow or deep)

Valid language or file type filters

Limitations

Metrics are based on indexed snapshot; real-time changes require index refresh

Complexity estimates are heuristic-based (e.g., function count, nesting depth) not true cyclomatic complexity

Line-of-code counts may include comments and blank lines depending on parser

What makes it unique

vs alternatives

Faster than tools like cloc because it uses indexed data; more accurate than line-counting tools because it understands symbol structure.

dependency graph extraction and relationship analysis

Medium confidence

Solves for

Best for

architects analyzing codebase structure and dependencies

developers planning modularization or microservices migration

LLM agents understanding code relationships before refactoring

Requires

Completed deep index with import statements extracted

Language support for import statement parsing

Limitations

Dependency extraction is limited to static imports; misses dynamic requires or lazy loading

External dependencies (npm packages, PyPI modules) are identified but not resolved

Transitive dependency computation can be expensive for large graphs (O(n²) in worst case)

What makes it unique

vs alternatives

More practical than runtime dependency analysis because it works without executing code; more accurate than static analysis tools because it uses parsed AST instead of regex.

file-level code summarization and structural analysis

Medium confidence

Solves for

Best for

LLM agents preparing context before code generation or refactoring tasks

developers navigating unfamiliar codebases quickly

documentation generation pipelines that need automated file summaries

Requires

File must be in indexed codebase

Language must have tree-sitter parser or fallback regex strategy

File must be readable (not binary)

Limitations

Summaries are structural only; do not include docstring content or inline comments

Complex nested structures (closures, decorators) may be simplified or omitted

Performance scales with file size; very large files (>50KB) may timeout

What makes it unique

vs alternatives

mcp protocol server with stdio transport and tool registration

Medium confidence

Solves for

Best for

developers using Claude Desktop or other MCP-compatible IDEs

teams building custom MCP clients that need code intelligence

organizations standardizing on MCP for LLM tool integration

Requires

Python 3.9+

FastMCP library

MCP-compatible client application

Limitations

Stdio transport is single-threaded; concurrent tool calls are serialized

No built-in authentication; relies on OS-level process isolation

Tool discovery requires MCP client to parse tool definitions; no dynamic capability negotiation

What makes it unique

vs alternatives

Simpler than building a custom REST API because MCP handles protocol negotiation and transport; more reliable than direct LLM API calls because MCP enforces schema validation and error handling.

project path management with context persistence

Medium confidence

Solves for

Best for

LLM agents managing multi-step code analysis workflows

developers switching between projects in Claude Desktop

automation scripts that need to operate on different codebases sequentially

Requires

Valid filesystem path

Read permissions on target directory

MCP server running and accessible

Limitations

Project path is session-scoped; lost when MCP server restarts

No project configuration file support; path must be set programmatically

Changing project path does not automatically invalidate existing indexes; must call refresh_index

What makes it unique

vs alternatives

More convenient than REST APIs that require path parameter on every request; more reliable than environment variables because context is explicitly managed and validated.

incremental index refresh with file change detection

Medium confidence

Solves for

Best for

developers using code-index-mcp in long-running IDE sessions

CI/CD pipelines that need to index code after checkout or merge

LLM agents that need to maintain accurate codebase state across multiple interactions

Requires

Existing index (build_deep_index must be run first)

Filesystem that supports modification timestamps

watchdog library for file monitoring (optional; falls back to polling)

Limitations

File watching uses polling on some systems (macOS, Windows), consuming CPU on large repos

Timestamp-based detection misses changes that don't update mtime (e.g., git checkout with --no-touch)

Refresh is blocking; large file changes may pause tool responses for seconds

What makes it unique

vs alternatives

More efficient than full re-indexing because it only reprocesses changed files; more reliable than git-based change detection because it works with uncommitted changes and non-git directories.

tree-sitter ast parsing with language-specific symbol extraction

Medium confidence

Solves for

Best for

developers working with polyglot codebases (multiple languages)

LLM agents that need accurate symbol extraction without language-specific compilers

code analysis tools that require structural understanding of source code

Requires

tree-sitter Python library

Language-specific tree-sitter bindings (e.g., tree-sitter-python, tree-sitter-javascript)

Source files must be readable text (not binary)

Limitations

Tree-sitter parsing adds 500ms-2s latency per large file depending on language complexity

Language bindings must be installed separately; missing bindings fall back to regex (less accurate)

Regex fallback misses nested or complex definitions (e.g., closures, decorators in Python)

What makes it unique

vs alternatives

file discovery and listing with type-based filtering

Medium confidence

Solves for

Best for

developers auditing codebase structure and file organization

LLM agents gathering context about project layout before analysis

build systems and CI/CD pipelines that need to discover files matching patterns

Requires

Completed shallow index (set_project_path must be called first)

Valid glob or regex pattern

Limitations

File discovery operates on indexed snapshot; real-time filesystem changes require index refresh

Glob patterns are limited to standard syntax; no advanced regex features

No support for symlinks or hard links; treats them as regular files

What makes it unique

vs alternatives

Faster than shell find/locate because it uses pre-indexed file list; more flexible than simple glob matching because it supports type-based and size-based filtering.

configuration management and tool discovery

Medium confidence

Solves for

Best for

MCP client developers integrating code-index-mcp into their applications

DevOps teams deploying code-index-mcp with environment-specific settings

developers customizing indexing behavior for their specific codebase

Requires

JSON configuration files in project root or .well-known directory

Write permissions to configuration files

Valid JSON syntax

Limitations

Configuration changes require server restart to take effect (no hot-reload)

Environment variable substitution is limited to simple ${VAR} syntax

No validation of configuration values; invalid settings may cause silent failures

What makes it unique

vs alternatives

More flexible than hardcoded tool definitions because configuration is externalized; more reliable than environment-only configuration because schema validation catches errors early.

error handling and tool response normalization

Medium confidence

Solves for

Best for

MCP client developers building robust integrations

DevOps teams monitoring code-index-mcp in production

developers debugging tool failures and understanding root causes

Requires

Python logging configuration

MCP client that can handle MCPToolError responses

Limitations

Error details are limited to exception messages; no structured error codes

Stack traces are only included in debug mode; production errors are summarized

No error recovery or retry logic; failed tools return errors immediately

What makes it unique

vs alternatives

More maintainable than per-tool error handling because logic is centralized; more user-friendly than raw exception messages because errors are formatted for MCP protocol.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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code-index-mcp

Capabilities14 decomposed

dual-strategy codebase indexing with shallow and deep modes

multi-strategy code search with regex, fuzzy matching, and semantic filtering

language-specific parsing strategy selection with fallback chains

context-aware symbol search with scope and type filtering

project statistics and code metrics generation

dependency graph extraction and relationship analysis

file-level code summarization and structural analysis

mcp protocol server with stdio transport and tool registration

project path management with context persistence

incremental index refresh with file change detection

tree-sitter ast parsing with language-specific symbol extraction

file discovery and listing with type-based filtering

configuration management and tool discovery

error handling and tool response normalization

Related Artifactssharing capabilities

system-prompts-and-models-of-ai-tools

PageIndex

token-savior

llm-code-highlighter

Video - testing Maige

grepmax

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to code-index-mcp

Are you the builder of code-index-mcp?

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code-index-mcp

Capabilities14 decomposed

dual-strategy codebase indexing with shallow and deep modes

multi-strategy code search with regex, fuzzy matching, and semantic filtering

language-specific parsing strategy selection with fallback chains

context-aware symbol search with scope and type filtering

project statistics and code metrics generation

dependency graph extraction and relationship analysis

file-level code summarization and structural analysis

mcp protocol server with stdio transport and tool registration

project path management with context persistence

incremental index refresh with file change detection

tree-sitter ast parsing with language-specific symbol extraction

file discovery and listing with type-based filtering

configuration management and tool discovery

error handling and tool response normalization

Related Artifactssharing capabilities

system-prompts-and-models-of-ai-tools

PageIndex

token-savior

llm-code-highlighter

Video - testing Maige

grepmax

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to code-index-mcp

Are you the builder of code-index-mcp?

Get the weekly brief

Data Sources