@er77/code-graph-rag-mcp
Advanced tools
Multi-agent LiteRAG MCP server for advanced code graph analysis
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.
π 10 Languages Supported | β‘ 5.5x Faster | π Semantic Search | π 22 MCP Methods
# Install globally
npm install -g @er77/code-graph-rag-mcp
# Or use with npx (no installation)
npx @er77/code-graph-rag-mcp /path/to/your/project
# Quick setup (recommended)
npx @modelcontextprotocol/inspector add code-graph-rag \
--command "npx" \
--args "@er77/code-graph-rag-mcp /path/to/your/codebase"
or
# ΠΡΡΡΡΠ°Ρ Π½Π°ΡΡΡΠΎΠΉΠΊΠ° (ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΠ΅ΡΡΡ)
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 β See detailed instructions
# Using helper script (prints the exact gemini CLI command to run)
./scripts/GEMINI-CORRECT-CONFIG.sh
# Or manually (example)
gemini mcp add-json code-graph-rag '{
"command": "npx",
"args": ["@er77/code-graph-rag-mcp", "/path/to/your/codebase"]
}'
# Using helper script (prints how to update ~/.codex/config.toml)
./scripts/CODEX-CORRECT-CONFIG.sh
# Project-scoped MCP server in ~/.codex/config.toml
[projects."/path/to/your/project".mcp_servers.code_graph_rag]
command = "npx"
args = ["@er77/code-graph-rag-mcp", "/path/to/your/codebase"]
transport = "stdio"
Multi-codebase support: Analyze multiple projects simultaneously β Multi-Codebase Setup Guide
npm install -g @er77/code-graph-rag-mcpcode-graph-rag-mcp /path/to/your/project./scripts/GEMINI-CORRECT-CONFIG.sh and follow the printed command./scripts/CODEX-CORRECT-CONFIG.sh and update ~/.codex/config.toml5.5x 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 | 22 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 |
| 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 |
| Agent Telemetry | Runtime metrics across agents | get_agent_metrics |
| Bus Diagnostics | Inspect/clear knowledge bus topics | get_bus_stats, clear_bus_topic |
| 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 | β Advanced (95%) |
| TypeScript/JavaScript | Full ES6+, JSX, TSX, React patterns | β Complete (100%) |
| C/C++ | Functions, structs/unions/enums, classes, namespaces, templates | β Advanced (90%) |
| C# | Classes, interfaces, enums, properties, LINQ, async/await | β Advanced (90%) |
| Rust | Functions, structs, enums, traits, impls, modules, use | β Advanced (90%) |
| Go | Packages, functions, structs, interfaces, goroutines, channels | β Advanced (90%) |
| Java | Classes, interfaces, enums, records (Java 14+), generics, lambdas | β Advanced (90%) |
| VBA | Modules, subs, functions, properties, user-defined types | β Regex-based (80%) |
# Single project analysis
code-graph-rag-mcp /path/to/your/project
# 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
# Agent telemetry snapshot
get_agent_metrics
# Knowledge bus diagnostics
get_bus_stats
clear_bus_topic --args '{"topic": "semantic:search"}'
# 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:
Breaking Changes & Major Improvements β‘
get_agent_metrics, get_bus_stats, and clear_bus_topic expose live telemetry and knowledge-bus controls for Codex automationagent_busy responses with retry guidance when capacity is saturateddoc_embeddings, vec_doc_embeddings) with improved sqlite-vec integrationTechnical Details:
Testing & Validation (2025-10-21):
get_agent_metrics, get_bus_stats, clear_bus_topicsqliteVecEnabled propertycase "get_graph" in src/index.ts:1668 & 1707 (non-critical, line 1707 unreachable)v2.5.7 - Semantic analysis improvements, lowered thresholds, clone detection
v2.5.6 - Fixed DoraAgent type collision (+16% success rate)
v2.5.5 - WASM path resolution fix, get_version tool, restart script
v2.5.4 - Architecture Decision Records (ADRs)
v2.5.3 - Deprecated dependency warning suppression
v2.5.2 - Enhanced README documentation
v2.5.1 - Python magic methods, import analysis enhancements
v2.5.0 - 8 new languages (C#, Rust, C, C++, Go, Java, VBA), Research Trinity
v2.4.1 - Rust AST parsing, system architecture docs
v2.4.0 - Health check tools, AST hotspots, semantic routing
v2.3.3 - Entity extraction fix (0 β 4,467 entities)
Minimum: Node.js 18+, 2GB RAM, Dual-core CPU Recommended: Node.js 18+, 8GB RAM, Quad-core CPU with SSD
boolean@3.2.0 warning: This is a transitive dependency from the optional onnxruntime-node package (used for ML embeddings). The package is deprecated but functional. The warning can be safely ignored as it doesn't affect core functionality.Contributing Guide β’ Issue Tracker
MIT License - see LICENSE file for details.
Links: GitHub β’ NPM β’ MCP Protocol
FAQs
Multi-agent LiteRAG MCP server for advanced code graph analysis
The npm package @er77/code-graph-rag-mcp receives a total of 69 weekly downloads. As such, @er77/code-graph-rag-mcp popularity was classified as not popular.
We found that @er77/code-graph-rag-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago.Β It has 1 open source maintainer collaborating on the project.
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