MCP server
by er77
Code Graph RAG enables advanced code analysis with graph traversal, semantic search, and multi-language support for smar
Analyzes codebases across 11 programming languages using Tree-sitter parsing and creates a semantic knowledge graph stored in SQLite. Enables natural language queries about code structure, relationships, and functionality with hybrid search combining graph traversal and vector similarity.
Code Graph RAG is a community-built MCP server published by er77 that provides AI assistants with tools and capabilities via the Model Context Protocol. Code Graph RAG enables advanced code analysis with graph traversal, semantic search, and multi-language support for smar It is categorized under developer tools.
You can install Code Graph RAG in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Code Graph RAG is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated Code Graph RAG against two servers with overlapping tools; this profile had the clearer scope statement.
Code Graph RAG has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Code Graph RAG is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Code Graph RAG is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Code Graph RAG against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend Code Graph RAG for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We wired Code Graph RAG into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Code Graph RAG benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Code Graph RAG has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated Code Graph RAG against two servers with overlapping tools; this profile had the clearer scope statement.
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Sponsor https://accelerator.slider-ai.ru/
Advanced Multi-Language Code Analysis with Semantic Intelligence
A powerful Model Context Protocol server that creates intelligent graph representations of your codebase with comprehensive semantic analysis capabilities.
🌟 11 Languages Supported | ⚡ 5.5x Faster | 🔍 Semantic Search | 📊 26 MCP Methods
# Install globally
npm install -g ./er77-code-graph-rag-mcp-2.7.12.tgz
code-graph-rag-mcp --version
# Quick setup (recommended)
npx @modelcontextprotocol/inspector add code-graph-rag \
--command "npx" \
--args "@er77/code-graph-rag-mcp /path/to/your/codebase"
or
#
claude mcp add-json code-graph-rag ' {
"command": "npx",
"args": ["@er77/code-graph-rag-mcp", "/_work_fodler"],
"env": {
"MCP_TIMEOUT": "80000"
}
}
Manual setup: Add to Claude Desktop config → See detailed instructions
# Example
gemini mcp add-json code-graph-rag '{
"command": "npx",
"args": ["@er77/code-graph-rag-mcp", "/path/to/your/codebase"]
}'
# Recommended: add a *global* MCP server entry (works from any project folder)
codex mcp remove code-graph-rag # optional cleanup
codex mcp add code-graph-rag -- code-graph-rag-mcp
# Or point Codex directly at a local dev build (no npm/npx required)
codex mcp remove code-graph-rag # optional cleanup
codex mcp add code-graph-rag -- node /absolute/path/to/code-graph-rag-mcp/dist/index.js
Multi-codebase support: Analyze multiple projects simultaneously → Multi-Codebase Setup Guide
npm install -g @er77/code-graph-rag-mcpcode-graph-rag-mcp [directory]gemini mcp add-json ... (above)codex mcp add ... (above)5.5x faster than Native Claude tools with comprehensive testing results:
| Metric | Native Claude | MCP CodeGraph | Improvement |
|---|---|---|---|
| Execution Time | 55.84s | <10s | 5.5x faster |
| Memory Usage | Process-heavy | 65MB | Optimized |
| Features | Basic patterns | 26 methods | Comprehensive |
| Accuracy | Pattern-based | Semantic | Superior |
| Feature | Description | Use Case |
|---|---|---|
| Semantic Search | Natural language code search | "Find authentication functions" |
| Code Similarity | Duplicate & clone detection | Identify refactoring opportunities |
| JSCPD Clone Scan | JSCPD-based copy/paste detection without embeddings | Targeted duplicate sweeps |
| Impact Analysis | Change impact prediction | Assess modification risks |
| AI Refactoring | Intelligent code suggestions | Improve code quality |
| Hotspot Analysis | Complexity & coupling metrics | Find problem areas |
| Cross-Language | Multi-language relationships | Polyglot codebases |
| Graph Health | Database diagnostics | get_graph_health |
| Version Info | Server version & runtime details | get_version |
| Safe Reset | Clean reindexing | reset_graph, clean_index |
| Batched Indexing | Resumable indexing with progress (Codex-safe for big repos) | batch_index |
| Agent Telemetry | Runtime metrics across agents | get_agent_metrics |
| Bus Diagnostics | Inspect/clear knowledge bus topics | get_bus_stats, clear_bus_topic |
| Lerna Project Graph | Workspace dependency DAG export, optional ingest, cached refresh control | lerna_project_graph (requires Lerna config) |
| Semantic Warmup | Configurable cache priming for embeddings | mcp.semantic.cacheWarmupLimit |
| Metric | Capability | Details |
|---|---|---|
| Parsing Speed | 100+ files/second | Tree-sitter based |
| Query Response | <100ms | Optimized SQLite + vector search |
| Agent System | Multi-agent coordination | Resource-managed execution |
| Vector Search | Hardware-accelerated (optional) | Automatic embedding ingestion |
| AST Analysis | Precise code snippets | Semantic context extraction |
| Language | Features | Support Level |
|---|---|---|
| Python | Async/await, decorators, magic methods (40+), dataclasses | ✅ Advanced (95%) |
| TypeScript/JavaScript | Full ES6+, JSX, TSX, React patterns | ✅ Complete (100%) |
| C/C++ | Functions, structs/unions/enums, classes, namespaces, templates | ✅ Advanced (90%) |
| C# | Classes, interfaces, enums, properties, LINQ, async/await | ✅ Advanced (90%) |
| Rust | Functions, structs, enums, traits, impls, modules, use | ✅ Advanced (90%) |
| Go | Packages, functions, structs, interfaces, goroutines, channels | ✅ Advanced (90%) |
| Java | Classes, interfaces, enums, records (Java 14+), generics, lambdas | ✅ Advanced (90%) |
| Kotlin | Packages/imports, classes/objects, functions/properties, relationships | ✅ Implemented |
| VBA | Modules, subs, functions, properties, user-defined types | ✅ Regex-based (80%) |
# Single project analysis
code-graph-rag-mcp
code-graph-rag-mcp /path/to/your/project
# CLI helpers
code-graph-rag-mcp --help
code-graph-rag-mcp --version
# Multi-project setup (see Multi-Codebase Setup Guide)
# Configure multiple projects in Claude Desktop config
# Check installation
code-graph-rag-mcp --help
# Health & maintenance
# Health check (totals + sample)
get_graph_health
# Reset graph data safely
reset_graph
# Clean reindex (reset + full index)
clean_index
# Batched index with progress (recommended for strict clients/timeouts)
batch_index
# Lerna workspace graph (ingest into storage)
lerna_project_graph --args '{"ingest": true}'
# Force refresh graph and re-ingest (bypass cache)
lerna_project_graph --args '{"ingest": true, "force": true}'
# Cached runs return `cached: true`; use `force` to break the 30s debounce when configs change.
# Agent telemetry snapshot
get_agent_metrics
# Knowledge bus diagnostics
get_bus_stats
clear_bus_topic --args '{"topic": "semantic:search"}'
# One-shot index from the CLI (debug mode)
node dist/index.js /home/er77/tt '{"jsonrpc":"2.0","id":"index-1","method":"tools/call","params":{"name":"index","arguments":{"directory":"/home/er77/tt","incremental":false,"fullScan":true,"reset":true}}}'
# Relationships for an entity name
list_entity_relationships (entityName: "YourEntity", relationshipTypes: ["imports"])
# Adjust semantic warmup (optional)
export MCP_SEMANTIC_WARMUP_LIMIT=25
# Note: when an agent is saturated, `AgentBusyError` responses include `retryAfterMs` hints.
With Claude Desktop:
Multi-Project Queries:
Codex/VSCode MCP stdio fails to start
Codex is strict about stdio: stdout must be JSON-RPC only. As of v2.7.12, console stdout logs are redirected to stderr during MCP runs, and heavy initialization is deferred until after handshake / first tool call.
Recommended Codex config: omit the directory argument and let the server use the workspace root via roots/list:
[mcp_servers.code-graph-rag]
command = "code-graph-rag-mcp"
args = []
If index / clean_index time out on large repos and the transport closes, prefer batch_index with a small maxFilesPerBatch and keep calling it with the returned sessionId until done:true.
If you must see logs on stdout for local debugging, set MCP_STDIO_ALLOW_STDOUT_LOGS=1 (not recommended for strict clients).
If startup still fails, check the global tmp log mirror: /tmp/code-graph-rag-mcp/mcp-server-YYYY-MM-DD.log (Linux/macOS; uses os.tmpdir()).
batch_index fails with agent_busy / memory_limit
Increase the coordinator/conductor limits (these gate task routing in-process): set COORDINATOR_MEMORY_LIMIT / CONDUCTOR_MEMORY_LIMIT and COORDINATOR_MAX_MEMORY_MB / CONDUCTOR_MAX_MEMORY_MB, or edit config/default.yaml.
If you see a real Node.js OOM, also start the server with a larger heap, e.g. NODE_OPTIONS="--max-old-space-size=4096" code-graph-rag-mcp.
Database location / multi-repo isolation
By default, the server stores its SQLite DB under ./.code-graph-rag/vectors.db (per repo). Add /.code-graph-rag/ to your project’s .gitignore.
Native module mismatch (better-sqlite3)
Since v2.6.4 the server automatically rebuilds the native binary when it detects a NODE_MODULE_VERSION mismatch. If the automatic rebuild fails (for example due to file permissions), run:
npm rebuild better-sqlite3
in the installation directory (globally this is commonly /usr/lib/node_modules/@er77/code-graph-rag-mcp).
Legacy database missing new columns
Older installations might lack the latest `embe
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.