{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-codegraphcontext-codegraphcontext","slug":"mcp-codegraphcontext-codegraphcontext","name":"CodeGraphContext","type":"mcp","url":"https://github.com/CodeGraphContext/CodeGraphContext","page_url":"https://unfragile.ai/mcp-codegraphcontext-codegraphcontext","categories":["mcp-servers"],"tags":["hacktoberfest2025","social-winter-of-code"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_0","uri":"capability://data.processing.analysis.multi.language.code.parsing.with.tree.sitter.ast.extraction","name":"multi-language code parsing with tree-sitter ast extraction","description":"Parses source code from 14 programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, Ruby, PHP, C#, Swift, Kotlin, Scala, Lua) using Tree-sitter's incremental parsing engine to build abstract syntax trees. Extracts semantic entities (functions, classes, variables, imports) and their relationships with structural awareness, enabling precise code graph construction rather than regex-based pattern matching. The parser layer feeds directly into the GraphBuilder service, which normalizes language-specific syntax into a unified graph schema.","intents":["I need to understand function definitions and their call chains across multiple languages in my codebase","I want to extract class hierarchies and inheritance relationships from typed languages","I need to track import dependencies and module relationships across my project","I want to identify all references to a specific function or variable across the codebase"],"best_for":["polyglot teams with codebases spanning multiple languages","developers building language-agnostic code analysis tools","teams migrating legacy code and needing dependency mapping"],"limitations":["Tree-sitter grammar support limited to 14 languages — no support for Haskell, Elixir, Clojure, or domain-specific languages","Incremental parsing adds ~50-100ms per file on first index; subsequent updates faster","Complex macro systems (C++ templates, Rust macros) may be partially resolved or skipped","Language-specific semantic analysis (type inference, scope resolution) limited to syntactic extraction"],"requires":["Python 3.9+","Tree-sitter library (bundled with package)","Source files in supported language format"],"input_types":["source code files (.py, .js, .ts, .java, .cpp, .go, .rs, .rb, .php, .cs, .swift, .kt, .scala, .lua)"],"output_types":["AST node objects","normalized entity records (functions, classes, variables)","relationship tuples (calls, inherits, imports)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_1","uri":"capability://data.processing.analysis.graph.based.code.relationship.indexing.with.pluggable.database.backends","name":"graph-based code relationship indexing with pluggable database backends","description":"Builds a queryable property graph of code entities and relationships, storing nodes (functions, classes, modules) and edges (calls, inherits, imports, references) in a graph database. Supports four database backends via a DatabaseManager singleton pattern: KùzuDB (default, zero-config, in-process), FalkorDB Lite (Unix only), FalkorDB Remote (networked), and Neo4j (all platforms). The GraphBuilder service constructs the graph incrementally, and the database abstraction layer enables backend switching without changing query logic, allowing teams to scale from local development (KùzuDB) to production deployments (Neo4j).","intents":["I want to query all functions that call a specific function across my entire codebase","I need to find all classes that inherit from a base class and trace the inheritance chain","I want to identify circular dependencies or unused code paths in my project","I need to understand the impact of changing a function signature on all callers"],"best_for":["teams with large codebases (10k+ files) needing efficient relationship queries","developers building code refactoring or impact analysis tools","organizations deploying code intelligence as a shared service (Neo4j backend)"],"limitations":["KùzuDB backend limited to single-process access — no concurrent writes from multiple indexing jobs","FalkorDB Lite only available on Unix/Linux; Windows users must use KùzuDB or remote FalkorDB","Graph construction time scales linearly with codebase size; 100k+ files may require 10+ minutes on first index","No built-in graph versioning — updating an indexed codebase requires full re-indexing or manual node updates","Query performance depends on database backend; KùzuDB slower than Neo4j for complex multi-hop queries"],"requires":["Python 3.9+","KùzuDB (bundled, zero-config) OR FalkorDB (requires separate installation) OR Neo4j (requires server setup)","Sufficient disk space for graph database (typically 2-5x source code size)"],"input_types":["parsed code entities from Tree-sitter (functions, classes, variables, imports)","relationship tuples (call edges, inheritance edges, reference edges)"],"output_types":["graph nodes with properties (name, type, file, line number, signature)","graph edges with relationship types and metadata","query results (paths, subgraphs, relationship chains)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_10","uri":"capability://automation.workflow.background.job.management.and.async.operation.tracking","name":"background job management and async operation tracking","description":"Manages long-running operations (code indexing, bundle downloads, graph updates) as background jobs tracked by a JobManager service. Each job has a unique ID, status (pending, in-progress, completed, failed), and progress metadata. Jobs are stored in-memory and exposed through both CLI and MCP interfaces, allowing clients to poll job status without blocking. The job system prevents MCP client timeouts by returning immediately with a job ID, then allowing clients to check progress asynchronously. Enables responsive UX for operations that take seconds or minutes.","intents":["I want to start a long-running indexing operation and check its progress without blocking","I need to know the status of a background bundle download while continuing to work","I want to cancel a long-running operation if it's taking too long","I need to see progress updates (percentage complete, estimated time remaining) for large operations"],"best_for":["developers using AI assistants that need responsive MCP interactions","teams with large codebases where indexing takes minutes","CI/CD pipelines that need to monitor indexing progress"],"limitations":["Job state stored in-memory — lost if server restarts","No persistent job history — completed jobs not retained after server restart","No job cancellation API — running jobs must complete or timeout","Progress tracking limited to high-level status — no granular per-file progress","No job queuing — concurrent jobs may compete for database resources"],"requires":["Python 3.9+","Active graph database connection","MCP client or CLI interface to poll job status"],"input_types":["operation parameters (codebase path, bundle names, etc.)","job IDs for status polling"],"output_types":["job objects with ID, status, progress, and metadata","status updates (pending, in-progress, completed, failed)","error messages if job fails"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_11","uri":"capability://automation.workflow.configuration.management.with.multi.environment.support","name":"configuration management with multi-environment support","description":"Provides configuration management that supports multiple deployment environments (local development, Docker, production) with environment-specific database backends, logging levels, and bundle registry URLs. Configuration is loaded from environment variables, config files, and command-line arguments with a clear precedence order. Enables teams to use KùzuDB locally, FalkorDB in staging, and Neo4j in production without code changes. The configuration layer also handles database connection pooling, retry logic, and fallback strategies (e.g., falling back from FalkorDB to KùzuDB if connection fails).","intents":["I want to use KùzuDB locally and Neo4j in production without changing code","I need to configure different bundle registries for internal and public libraries","I want to set logging levels and debug output for troubleshooting","I need to configure database connection timeouts and retry logic for production stability"],"best_for":["teams deploying CodeGraphContext across multiple environments","DevOps engineers configuring production deployments","developers switching between local and cloud-based databases"],"limitations":["Configuration precedence can be confusing with multiple sources (env vars, config files, CLI args)","No built-in configuration validation — invalid configs may fail at runtime","Database connection pooling not implemented — each process creates new connections","No hot-reload of configuration — changes require server restart","Limited documentation on environment-specific best practices"],"requires":["Python 3.9+","Environment variables or config file (optional, defaults provided)","Database connection credentials (for remote databases)"],"input_types":["environment variables (CGC_DATABASE_TYPE, CGC_DATABASE_URL, etc.)","YAML/JSON config files","command-line arguments"],"output_types":["configuration objects with resolved values","database connection strings","logging configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_12","uri":"capability://data.processing.analysis.incremental.indexing.with.change.detection.and.delta.updates","name":"incremental indexing with change detection and delta updates","description":"Implements incremental indexing that detects changed files and updates only affected graph nodes and edges rather than re-indexing the entire codebase. The GraphBuilder service tracks file modification times and checksums to identify changes, re-parses only modified files, and updates the graph with new/modified/deleted entities. Enables fast re-indexing of large codebases where only a few files change between updates. Integrates with the CodeWatcher to automatically trigger incremental updates when files change, keeping the graph synchronized with the codebase.","intents":["I want to re-index my codebase quickly after editing a few files without re-parsing everything","I need to update the graph with only the changes from my recent edits","I want to avoid the overhead of full re-indexing for small code changes","I need to keep the graph synchronized with my codebase as I work"],"best_for":["developers with large codebases (10k+ files) who edit frequently","teams using live code watching and real-time graph synchronization","CI/CD pipelines that need fast incremental indexing"],"limitations":["Change detection relies on file modification times — may miss changes if timestamps are unreliable","Incremental updates may leave stale nodes if entities are moved between files","Checksum computation adds overhead — slower than modification time alone","No built-in garbage collection for deleted entities — may accumulate orphaned nodes","Incremental updates may be slower than full re-indexing for small codebases due to change detection overhead"],"requires":["Python 3.9+","Indexed code graph in database","File system with reliable modification times"],"input_types":["changed file paths","file modification times or checksums"],"output_types":["updated graph nodes and edges","change summaries (entities added/modified/deleted)","indexing statistics (files processed, entities changed)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_13","uri":"capability://automation.workflow.docker.containerization.with.production.ready.deployment","name":"docker containerization with production-ready deployment","description":"Provides Docker images and docker-compose configurations for deploying CodeGraphContext with Neo4j or other production databases. The Docker setup includes the MCP server, CLI tools, and optional visualization server, with environment-based configuration for different deployment scenarios. Enables teams to deploy code intelligence as a containerized service with persistent database storage, making it suitable for production environments and CI/CD integration.","intents":["I want to deploy CodeGraphContext as a containerized service in production","I need to run CodeGraphContext with a persistent Neo4j database for my team","I want to integrate code indexing into my CI/CD pipeline as a Docker container","I need to scale CodeGraphContext across multiple servers with a shared database"],"best_for":["DevOps engineers deploying CodeGraphContext in production","teams running code intelligence as a shared service","organizations integrating code indexing into CI/CD pipelines","teams deploying on Kubernetes or Docker Swarm"],"limitations":["Docker image size may be large (500MB+) due to Python dependencies and Tree-sitter grammars","No built-in Kubernetes manifests — teams must write their own YAML","Database persistence requires external volume management — not suitable for ephemeral containers","No built-in load balancing or horizontal scaling — single MCP server instance","Docker networking may complicate local development workflows"],"requires":["Docker 20.10+ or Docker Desktop","docker-compose 1.29+ (optional, for multi-container setup)","Neo4j or other supported database (can be containerized)","Sufficient disk space for database volumes (10GB+ for large codebases)"],"input_types":["Dockerfile and docker-compose.yml configurations","environment variables for database connection","codebase volumes mounted into container"],"output_types":["running MCP server container","persistent database storage","visualization server (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_2","uri":"capability://automation.workflow.real.time.code.graph.synchronization.with.file.watching","name":"real-time code graph synchronization with file watching","description":"Monitors local file system changes using a CodeWatcher service and automatically updates the indexed graph database when source files are modified, created, or deleted. Implements debouncing to batch rapid file changes and avoid thrashing the database with individual updates. The watcher integrates with the JobManager to track synchronization status and expose progress through both CLI and MCP interfaces, enabling AI assistants to work with current code context without manual re-indexing.","intents":["I want my AI assistant to see code changes immediately as I edit files without manual re-indexing","I need to track which files have changed since the last graph update","I want to pause watching during bulk file operations and resume afterward","I need to know the status of background synchronization while working"],"best_for":["developers using AI assistants (Cursor, Claude) with live code context","teams with continuous development workflows where code changes frequently","developers who want to avoid manual re-indexing after file edits"],"limitations":["File watching adds ~5-10% CPU overhead during active development","Debouncing introduces 1-2 second latency between file save and graph update","Symlinks and network file systems may not trigger watch events reliably","Large file deletions (e.g., removing entire directories) may take 30+ seconds to process","Watch events can be missed if the process is suspended or system is under heavy I/O load"],"requires":["Python 3.9+","File system that supports inotify (Linux), FSEvents (macOS), or ReadDirectoryChangesW (Windows)","Active graph database connection","Write permissions to the indexed codebase directory"],"input_types":["file system events (create, modify, delete)","file paths and change types"],"output_types":["updated graph nodes and edges","job status updates (pending, in-progress, completed)","change summaries (files added/modified/deleted)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_3","uri":"capability://tool.use.integration.mcp.server.interface.for.ai.assistant.integration","name":"mcp server interface for ai assistant integration","description":"Exposes the code graph as a Model Context Protocol (MCP) server using JSON-RPC 2.0 over stdio, enabling AI assistants (Claude, Cursor, VS Code) to query code relationships, search entities, and analyze dependencies without direct database access. The MCP server wraps core services (GraphBuilder, CodeFinder, CodeWatcher) and translates MCP tool calls into service method invocations, returning structured results. Implements background job tracking so long-running operations (indexing, bundle downloads) can be polled asynchronously, preventing MCP client timeouts.","intents":["I want Claude or Cursor to understand my codebase structure and relationships when answering questions","I need to ask my AI assistant about function dependencies and call chains","I want my AI assistant to search for specific code patterns or entities across my project","I need to provide my AI assistant with up-to-date code context as I edit files"],"best_for":["developers using Claude, Cursor, or VS Code with AI features","teams integrating code intelligence into custom AI workflows","organizations building AI-powered code analysis tools"],"limitations":["MCP server runs as a single process — no built-in load balancing for multiple concurrent clients","JSON-RPC over stdio adds ~50-100ms latency per request compared to direct API calls","Large query results (e.g., all references to a common function) may exceed MCP message size limits","No authentication or authorization — assumes trusted local environment","Background jobs are stored in-memory — job status lost if server restarts"],"requires":["Python 3.9+","MCP-compatible client (Claude, Cursor, VS Code with MCP extension)","Active graph database connection","stdio communication channel between client and server"],"input_types":["MCP tool call requests with parameters (entity names, search queries, file paths)","JSON-RPC 2.0 formatted messages"],"output_types":["MCP tool results with code entities, relationships, and metadata","job status objects (pending, in-progress, completed, failed)","structured search results and dependency graphs"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_4","uri":"capability://data.processing.analysis.cli.toolkit.for.local.code.analysis.and.visualization","name":"cli toolkit for local code analysis and visualization","description":"Provides command-line interface for developers to index codebases, query relationships, analyze dependencies, and visualize graphs locally without AI assistance. The CLI invokes the same core services as the MCP server (GraphBuilder, CodeFinder, CodeWatcher), ensuring feature parity. Includes commands for indexing, searching entities, finding relationships, watching files, and serving a web-based graph visualization. The CLI toolkit enables developers to explore code structure interactively and understand their codebase before sharing context with AI assistants.","intents":["I want to index my codebase and explore its structure from the command line","I need to find all functions that call a specific function without using an IDE","I want to visualize my code graph as an interactive web interface","I need to watch my codebase for changes and see real-time updates in the graph"],"best_for":["developers who prefer command-line tools over GUI-based IDEs","DevOps engineers building code analysis into CI/CD pipelines","teams analyzing codebases in remote/headless environments","developers exploring unfamiliar codebases before refactoring"],"limitations":["CLI output limited to terminal width and height — large graphs difficult to visualize in text form","Web visualization server requires browser access — not suitable for headless-only environments","No built-in diff visualization — comparing graphs across versions requires manual inspection","Command-line argument parsing may be verbose for complex queries","No persistent command history or saved queries — each invocation is stateless"],"requires":["Python 3.9+","Terminal with ANSI color support (optional, for better output formatting)","Web browser (optional, for graph visualization)","Network access to localhost:8000 (optional, for visualization server)"],"input_types":["command-line arguments and flags","file paths and directory paths","entity names and search queries"],"output_types":["formatted text output (tables, lists, trees)","JSON output (for piping to other tools)","HTML/JavaScript visualization (served via embedded web server)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_5","uri":"capability://memory.knowledge.pre.indexed.bundle.system.for.instant.library.loading","name":"pre-indexed bundle system for instant library loading","description":"Distributes pre-indexed code graphs as .cgc bundle files for popular open-source libraries (React, Django, FastAPI, etc.), enabling instant loading without parsing and indexing. Bundles are generated offline and published to a registry, reducing initial setup time from minutes to seconds. The bundle system uses a BundleManager service that downloads bundles on-demand, validates checksums, and merges them into the local graph database. Bundles achieve 30-60x speedup by eliminating parsing and graph construction overhead, making it practical to include large dependencies in AI context.","intents":["I want to instantly load the code structure of popular libraries without waiting for indexing","I need to understand how a library's functions and classes are organized without parsing it myself","I want to reduce initial setup time when starting a new project with common dependencies","I need to share pre-indexed library graphs across my team without duplicating work"],"best_for":["developers working with large popular libraries (React, Django, FastAPI, NumPy)","teams that want to reduce onboarding time for new projects","organizations sharing code intelligence across multiple developers","CI/CD pipelines that need fast code analysis without indexing overhead"],"limitations":["Bundle registry limited to popular open-source libraries — custom/proprietary code cannot be bundled","Bundles are version-specific — updating a library requires downloading a new bundle","Bundle downloads add network latency (typically 10-100MB per bundle) on first use","Merging bundles with local code graphs may cause conflicts if entities have the same names","Bundle validation requires checksum verification — corrupted bundles will be rejected"],"requires":["Python 3.9+","Network access to bundle registry (https://registry.codegraphcontext.com or custom registry)","Sufficient disk space for bundle files (typically 10-100MB per library)","Active graph database connection"],"input_types":["library names and versions","bundle registry URLs","local graph database connection"],"output_types":["merged graph nodes and edges from bundle","bundle metadata (version, checksum, size)","job status updates (downloading, validating, merging)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_6","uri":"capability://search.retrieval.entity.search.and.code.pattern.discovery","name":"entity search and code pattern discovery","description":"Provides a CodeFinder service that searches the indexed graph for code entities (functions, classes, variables, modules) by name, type, or pattern. Supports substring matching, regex patterns, and type-based filtering (e.g., 'find all functions that return a specific type'). Search results include entity metadata (file location, line number, signature) and can be filtered by language, file path, or scope. The search layer integrates with both CLI and MCP interfaces, enabling developers and AI assistants to locate relevant code quickly without manual file browsing.","intents":["I want to find all functions named 'process' or matching a pattern across my codebase","I need to locate all classes that inherit from a specific base class","I want to find all variables of a specific type or with a specific name pattern","I need to search for code entities in a specific file or directory"],"best_for":["developers refactoring code and needing to find all references to a symbol","teams analyzing large codebases and searching for specific patterns","AI assistants providing code recommendations based on entity searches"],"limitations":["Search performance depends on graph database backend — KùzuDB slower than Neo4j for large result sets","Regex patterns may be slow on large graphs with millions of entities","Type-based filtering limited to syntactic type information — no semantic type inference","Search results may include false positives if entity names are ambiguous","No full-text search on docstrings or comments — only entity names and signatures"],"requires":["Python 3.9+","Indexed code graph in database","Search query (entity name, pattern, or type filter)"],"input_types":["search query strings (substring, regex, or type filter)","scope filters (file path, language, entity type)"],"output_types":["entity records with metadata (name, type, file, line, signature)","entity counts and match statistics","formatted search results (JSON, table, or text)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_7","uri":"capability://data.processing.analysis.dependency.analysis.and.relationship.traversal","name":"dependency analysis and relationship traversal","description":"Analyzes code relationships stored in the graph to identify dependencies, call chains, inheritance hierarchies, and circular references. Provides traversal algorithms that follow edges (calls, imports, inherits) to compute transitive relationships and impact analysis. For example, finding all functions that transitively call a specific function, or identifying all classes that depend on a specific module. The relationship analysis layer supports both breadth-first and depth-first traversal, with configurable depth limits to prevent infinite loops in circular dependencies.","intents":["I want to find all functions that transitively call a specific function (direct and indirect)","I need to identify circular dependencies in my codebase","I want to understand the impact of changing a function on all callers (direct and indirect)","I need to trace the inheritance chain from a class to its base classes and subclasses"],"best_for":["developers performing impact analysis before refactoring","teams identifying and breaking circular dependencies","architects understanding system dependencies and coupling","developers optimizing code by identifying unused dependencies"],"limitations":["Traversal performance degrades with graph size — large codebases may require 10+ seconds for deep traversals","Circular dependencies require cycle detection to prevent infinite loops — adds computational overhead","Transitive relationship computation is not cached — repeated queries require re-traversal","Depth limits may truncate results if relationships are deeper than configured limit","No semantic analysis of dependency strength (e.g., required vs optional dependencies)"],"requires":["Python 3.9+","Indexed code graph with relationship edges","Starting entity (function, class, or module)"],"input_types":["entity identifiers (function name, class name, module path)","relationship types to follow (calls, imports, inherits)","traversal parameters (depth limit, direction)"],"output_types":["relationship paths (sequences of edges)","transitive relationship sets (all reachable entities)","cycle detection results (circular dependency chains)","impact analysis reports (affected entities and counts)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_8","uri":"capability://image.visual.web.based.interactive.graph.visualization","name":"web-based interactive graph visualization","description":"Serves an embedded web application that visualizes the code graph as an interactive node-link diagram, allowing developers to explore relationships visually. The visualization server (viz/server.py) renders nodes as code entities and edges as relationships, with interactive features like zooming, panning, filtering by entity type, and clicking to view entity details. The visualization is generated from the indexed graph database and updates in real-time as the graph changes. Developers can use the visualization to understand system architecture, identify bottlenecks, and communicate code structure to team members.","intents":["I want to visualize my codebase structure as an interactive graph to understand system architecture","I need to show my team the relationships between modules and classes in a visual format","I want to explore the call graph of a specific function to understand its dependencies","I need to identify clusters of tightly-coupled code that should be refactored"],"best_for":["architects and tech leads communicating system design to teams","developers exploring unfamiliar codebases visually","teams performing code reviews and discussing architecture","developers identifying refactoring opportunities through visual inspection"],"limitations":["Large graphs (10k+ nodes) may be slow to render or cause browser performance issues","Node-link visualization becomes cluttered with dense graphs — filtering/clustering needed","No built-in layout algorithms for hierarchical or force-directed layouts — uses basic grid layout","Visualization updates require browser refresh if graph changes — no real-time push updates","No export to image/PDF formats — screenshots required for documentation"],"requires":["Python 3.9+","Web browser with JavaScript support (Chrome, Firefox, Safari, Edge)","Network access to localhost:8000 (default visualization server port)","Indexed code graph in database"],"input_types":["graph nodes and edges from database","entity metadata (name, type, file, line)","relationship types and weights"],"output_types":["HTML/CSS/JavaScript visualization","interactive node-link diagram","entity detail views (metadata, relationships)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-codegraphcontext-codegraphcontext__cap_9","uri":"capability://data.processing.analysis.language.agnostic.entity.normalization.and.schema.mapping","name":"language-agnostic entity normalization and schema mapping","description":"Normalizes heterogeneous code entities from 14 different languages into a unified graph schema with consistent entity types (Function, Class, Variable, Module, Import) and relationship types (Calls, Inherits, Imports, References). The normalization layer maps language-specific syntax to canonical entity representations, enabling queries and analysis to work across languages without language-specific logic. 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