dual-strategy codebase indexing with shallow and deep modes
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.
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
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.
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
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.
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
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).
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
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.
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
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.
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
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.
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
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.
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.
+6 more capabilities