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Code Graph Context MCP Server

MIT License TypeScript Neo4j NestJS OpenAI MCP

A Model Context Protocol (MCP) server that builds rich code graphs to provide deep contextual understanding of TypeScript codebases to Large Language Models. This server parses your codebase using AST analysis, constructs a comprehensive graph representation in Neo4j, and provides intelligent querying capabilities through semantic search and graph traversal.

Config-Driven & Extensible: Define custom framework schemas to capture domain-specific patterns beyond the included NestJS support. The parser is fully configurable to recognize your architectural patterns, decorators, and relationships.

Features

  • Rich Code Graph Generation: Parses TypeScript projects and creates detailed graph representations with AST-level precision
  • Semantic Search: Vector-based semantic search using OpenAI embeddings to find relevant code patterns and implementations
  • Natural Language Querying: Convert natural language questions into Cypher queries using OpenAI assistants API
  • Framework-Aware & Customizable: Built-in NestJS schema with ability to define custom framework patterns via configuration
  • Graph Traversal: Explore code relationships and dependencies through intelligent graph traversal
  • High Performance: Optimized Neo4j storage with vector indexing for fast retrieval
  • MCP Integration: Seamless integration with Claude Code and other MCP-compatible tools

Architecture

The MCP server consists of several key components:

Core Components

  1. TypeScript Parser (src/core/parsers/typescript-parser-v2.ts): Uses ts-morph to parse TypeScript AST and extract code entities
  2. Graph Storage (src/storage/neo4j/neo4j.service.ts): Neo4j integration for storing and querying the code graph
  3. Embeddings Service (src/core/embeddings/embeddings.service.ts): OpenAI integration for semantic search capabilities
  4. MCP Server (src/mcp/mcp.server.ts): Main MCP server providing tools for code analysis

Graph Schema

The system uses a dual-schema approach:

  • Core Schema: AST-level nodes (Classes, Methods, Properties, Imports, etc.)
  • Framework Schema: Semantic interpretations (NestJS Controllers, Services, HTTP Endpoints, etc.)

Getting Started

Prerequisites

  • Node.js >= 18
  • Neo4j >= 5.0 with APOC plugin
  • OpenAI API Key (for embeddings and natural language processing)
  • Docker (recommended for Neo4j setup)

Installation

  1. Clone the repository:
git clone https://github.com/drewdrewH/code-graph-context.git
cd code-graph-context
  1. Install dependencies:
npm install
  1. Set up Neo4j using Docker:
docker-compose up -d

This will start Neo4j with:

  • Web interface: http://localhost:7474
  • Bolt connection: bolt://localhost:7687
  • Username: neo4j, Password: PASSWORD
  1. Configure environment variables:
# Create .env file
cp .env.example .env

# Edit .env with your configuration:
OPENAI_API_KEY=your_openai_api_key_here
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=PASSWORD
  1. Build the project:
npm run build

Adding to Claude Code

Method 1: Direct (Recommended)

claude mcp add code-graph-context node /absolute/path/to/code-graph-context/dist/mcp/mcp.server.js

Method 2: With Debug Shell (useful for troubleshooting)

claude mcp add code-graph-context /absolute/path/to/code-graph-context/debug-mcp.sh

Method 3: Manual Configuration

Add to your Claude Code MCP configuration file:

{
  "mcpServers": {
    "code-graph-context": {
      "command": "node",
      "args": ["/path/to/code-graph-context/dist/mcp/mcp.server.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "PASSWORD"
      }
    }
  }
}

Tool Usage Guide & Sequential Workflows

Sequential Tool Usage Patterns

The MCP tools are designed to work together in powerful workflows. Here are the most effective patterns:

Pattern 1: Discovery → Focus → Deep Dive

graph LR
    A[search_codebase] --> B[traverse_from_node] --> C[traverse_from_node with skip]
    A --> D[traverse_from_node] --> E[traverse_from_node deeper]
Loading

Pattern 2: Broad Search → Targeted Analysis

  1. Start Broad: Use search_codebase to find relevant starting points
  2. Focus: Use traverse_from_node to explore specific relationships
  3. Paginate: Use skip parameter to explore different sections of the graph

Tool Deep Dive

1. search_codebase - Your Starting Point

Purpose: Semantic search using vector embeddings to find the most relevant code nodes.

Response Structure: Returns normalized JSON using JSON:API pattern to eliminate duplication:

  • nodes: Map of unique nodes (stored once, referenced by ID)
  • depths: Array of depth levels with relationship chains
  • Source Code: Included by default (truncated to 1000 chars: first 500 + last 500)
  • Statistics: Total connections, unique files, max depth

Real Response Example:

// Query: "JWT token validation"
// Returns:
{
  "totalConnections": 22,
  "uniqueFiles": 2,
  "maxDepth": 3,
  "startNodeId": "MethodDeclaration:697d2c96-1f91-4894-985d-1eece117b72b",
  "nodes": {
    "MethodDeclaration:697d2c96-1f91-4894-985d-1eece117b72b": {
      "id": "MethodDeclaration:697d2c96-1f91-4894-985d-1eece117b72b",
      "type": "MethodDeclaration",
      "filePath": "/packages/jwt-validation/src/lib/jwt.strategy.ts",
      "name": "validate",
      "sourceCode": "validate(payload: EmJwtPayload): EmJwtPayload {\n  ...\n\n... [truncated] ...\n\n  return payload;\n}",
      "hasMore": true,
      "truncated": 1250
    },
    "ClassDeclaration:abc-123": {
      "id": "ClassDeclaration:abc-123",
      "type": "Service",
      "filePath": "/packages/jwt-validation/src/lib/jwt.strategy.ts",
      "name": "JwtStrategy"
    }
  },
  "depths": [
    {
      "depth": 1,
      "count": 8,
      "chains": [
        {
          "via": "HAS_MEMBER",
          "direction": "INCOMING",
          "count": 1,
          "nodeIds": ["ClassDeclaration:abc-123"]
        },
        {
          "via": "HAS_PARAMETER",
          "direction": "OUTGOING",
          "count": 2,
          "nodeIds": ["Parameter:xyz-456", "Parameter:def-789"]
        }
      ]
    },
    {
      "depth": 2,
      "count": 14,
      "chains": [
        {
          "via": "HAS_MEMBER → INJECTS",
          "direction": "INCOMING",
          "count": 3,
          "nodeIds": ["Service:auth-service", "Service:user-service", "Repository:user-repo"],
          "hasMore": 2
        }
      ]
    }
  ]
}

Key Features:

  • JSON:API Normalization: Nodes stored once in nodes map, referenced by ID to eliminate duplication
  • Source Code Truncation: Max 1000 chars per snippet (first 500 + last 500 chars)
  • Relationship Chains: Shows full path like "HAS_MEMBER → INJECTS → USES_REPOSITORY"
  • Direction Indicators: OUTGOING (what this calls), INCOMING (who calls this)

Pro Tips:

  • Use specific domain terms: "JWT token validation" vs "authentication"
  • Start with limit=1-3 for initial exploration to avoid token limits
  • Look for node IDs in nodes map to use with traverse_from_node
  • Check truncated field to see how many bytes were hidden from source code

2. traverse_from_node - Deep Relationship Exploration

Purpose: Explore all connections from a specific node with precise control over depth and pagination.

Response Structure: Identical JSON:API format to search_codebase:

  • Focused Traversal: Starts from your specified node
  • Depth Control: Configurable max depth (1-10, default 3)
  • Pagination: Skip parameter for exploring large graphs in chunks
  • Source Code Included by Default: Set includeCode: false for structure-only view

Real Response Example:

// Starting from a Service class
// maxDepth: 2, skip: 0, includeCode: true
{
  "totalConnections": 15,
  "uniqueFiles": 4,
  "maxDepth": 2,
  "startNodeId": "ClassDeclaration:credit-check-service",
  "nodes": {
    "ClassDeclaration:credit-check-service": {
      "id": "ClassDeclaration:credit-check-service",
      "type": "Service",
      "filePath": "/src/modules/credit/credit-check.service.ts",
      "name": "CreditCheckService",
      "sourceCode": "@Injectable([CreditCheckRepository, OscilarClient])\nexport class CreditCheckService {\n  ...\n\n... [truncated] ...\n\n}",
      "truncated": 3200
    },
    "Repository:credit-check-repo": {
      "id": "Repository:credit-check-repo",
      "type": "Repository",
      "filePath": "/src/modules/credit/credit-check.repository.ts",
      "name": "CreditCheckRepository"
    }
  },
  "depths": [
    {
      "depth": 1,
      "count": 5,
      "chains": [
        {
          "via": "INJECTS",
          "direction": "OUTGOING",
          "count": 2,
          "nodeIds": ["Repository:credit-check-repo", "VendorClient:oscilar"]
        },
        {
          "via": "HAS_MEMBER",
          "direction": "OUTGOING",
          "count": 3,
          "nodeIds": ["Method:processCheck", "Method:getResult", "Method:rerun"]
        }
      ]
    },
    {
      "depth": 2,
      "count": 10,
      "chains": [
        {
          "via": "INJECTS → USES_DAL",
          "direction": "OUTGOING",
          "count": 1,
          "nodeIds": ["DAL:application-dal"]
        }
      ]
    }
  ]
}

Parameters:

  • nodeId (required): Node ID from search_codebase results
  • maxDepth (default: 3): Traversal depth (1-10)
  • skip (default: 0): Pagination offset
  • includeCode (default: true): Include source code snippets
  • summaryOnly (default: false): Just file paths and statistics
  • direction (default: BOTH): Filter by OUTGOING/INCOMING/BOTH
  • relationshipTypes (optional): Filter by specific relationships like ["INJECTS", "USES_REPOSITORY"]

Pagination Strategy:

// Note: Pagination removed in recent commits - all results returned
// Use depth and relationship filtering instead
traverse_from_node({
  nodeId,
  maxDepth: 2,
  relationshipTypes: ["INJECTS"]  // Focus on dependency injection only
})

3. parse_typescript_project - Graph Generation

Purpose: Parse a TypeScript/NestJS project and build the initial graph database.

// Full project parsing
await mcp.call('parse_typescript_project', {
  projectPath: '/path/to/your/nestjs/project',
  tsconfigPath: '/path/to/your/nestjs/project/tsconfig.json',
  clearExisting: true // Recommended: clear previous data
});

// Response: Success confirmation with node/edge counts
"✅ SUCCESS: Parsed 2,445 nodes and 4,892 edges. Graph imported to Neo4j."

Performance Notes:

  • Large projects (>1000 files) may take several minutes
  • Embedding generation adds significant time but enables semantic search
  • Use clearExisting: true to avoid duplicate data

4. test_neo4j_connection - Health Check

Purpose: Verify database connectivity and APOC plugin availability.

// Simple health check
await mcp.call('test_neo4j_connection');

// Response indicates database status
"Neo4j connected: Connected! at 2025-07-25T19:48:42.676Z
APOC plugin available with 438 functions"

Workflow Examples

Example 1: Understanding Authentication Flow

// Step 1: Find authentication-related code
const searchResult = await mcp.call('search_codebase', {
  query: 'JWT token validation authentication',
  limit: 2
});

// Step 2: Extract node ID from most relevant result
const nodeId = "MethodDeclaration:697d2c96-1f91-4894-985d-1eece117b72b";

// Step 3: Explore immediate relationships
const immediateConnections = await mcp.call('traverse_from_node', {
  nodeId,
  maxDepth: 2,
  skip: 0
});

// Step 4: Go deeper to understand full authentication chain
const deepConnections = await mcp.call('traverse_from_node', {
  nodeId,
  maxDepth: 4,
  skip: 0
});

// Step 5: Explore different connection branches
const alternateConnections = await mcp.call('traverse_from_node', {
  nodeId,
  maxDepth: 3,
  skip: 10  // Skip first 10 to see different connections
});

Example 2: API Endpoint Analysis

// Step 1: Search for controller endpoints
const controllerSearch = await mcp.call('search_codebase', {
  query: 'HTTP controller endpoints routes POST GET',
  limit: 1
});

// Step 2: Find a controller node ID from results
const controllerNodeId = "ClassDeclaration:controller-uuid";

// Step 3: Explore what endpoints this controller exposes
const endpoints = await mcp.call('traverse_from_node', {
  nodeId: controllerNodeId,
  maxDepth: 2,
  skip: 0
});

// Step 4: For each endpoint found, explore its dependencies
const endpointNodeId = "MethodDeclaration:endpoint-uuid";
const endpointDeps = await mcp.call('traverse_from_node', {
  nodeId: endpointNodeId,
  maxDepth: 3,
  skip: 0
});

Example 3: Service Dependency Mapping

// Step 1: Find a specific service
const serviceSearch = await mcp.call('search_codebase', {
  query: 'UserService injectable dependency injection',
  limit: 1
});

// Step 2: Map all its dependencies (what it injects)
const serviceDeps = await mcp.call('traverse_from_node', {
  nodeId: "ClassDeclaration:user-service-uuid",
  maxDepth: 2,
  skip: 0
});

// Step 3: Find what depends on this service (reverse relationships)
const serviceDependents = await mcp.call('search_codebase', {
  query: 'UserService injection constructor parameter',
  limit: 5
});

Advanced Usage Tips

Understanding Response Format (JSON:API Normalization)

Key Insight: All responses use JSON:API pattern to eliminate duplication by storing each node once and referencing by ID.

How to Read Responses:

  1. Start with nodes map: All unique nodes are stored here once
  2. Look at depths array: Shows how nodes are connected at each depth level
  3. Follow nodeIds references: Use IDs to look up full node data in nodes map
  4. Check truncated field: Indicates how many bytes of source code were hidden

Example Reading Pattern:

const response = await search_codebase({ query: "authentication" });

// 1. Get overview statistics
console.log(`Found ${response.totalConnections} connections across ${response.uniqueFiles} files`);

// 2. Examine the starting node
const startNode = response.nodes[response.startNodeId];
console.log(`Starting from: ${startNode.name} in ${startNode.filePath}`);

// 3. Explore first depth level
const firstDepth = response.depths[0];
firstDepth.chains.forEach(chain => {
  console.log(`Via ${chain.via}: ${chain.count} connections (${chain.direction})`);

  // 4. Look up actual node details
  chain.nodeIds.forEach(nodeId => {
    const node = response.nodes[nodeId];
    console.log(`  - ${node.name} (${node.type})`);
  });
});

Managing Large Responses

  • Start Small: Use limit: 1-3 for initial searches
  • Relationship Filtering: Use relationshipTypes to focus on specific connections
  • Structure-Only View: Set includeCode: false to exclude source code snippets
  • Summary Mode: Use summaryOnly: true for just file paths and statistics

Efficient Graph Exploration

  • Breadth First: Start with low maxDepth (1-2) to get overview
  • Depth Second: Increase maxDepth (3-5) for detailed analysis
  • Direction Filtering: Use direction: "OUTGOING" or "INCOMING" to focus exploration
  • Source Code on Demand: Source code included by default but truncated to 1000 chars

Performance Optimization

  • Token Efficiency: JSON:API normalization eliminates duplicate nodes in responses
  • Code Truncation: Source code limited to 1000 chars (first 500 + last 500) to prevent token overflow
  • Memory: Large traversals may hit Neo4j memory limits (increase heap size if needed)
  • Caching: Node IDs are persistent; save interesting ones for later exploration

Available MCP Tools

Core Tools

Tool Description Parameters Use Case
hello Test tool that says hello None Verify MCP connection
test_neo4j_connection Test Neo4j connection and APOC plugin None Health check before operations

Parsing Tools

Tool Description Parameters Use Case
parse_typescript_project Parse TypeScript/NestJS project into graph projectPath, tsconfigPath, clearExisting? Initial setup: build the graph database

Search & Exploration Tools

Tool Description Parameters Best For
search_codebase Vector-based semantic search - Find most relevant code using OpenAI embeddings query (string), limit? (default: 10) Starting point for code exploration. Returns comprehensive multi-depth graph starting from best semantic match
traverse_from_node Focused graph traversal - Explore specific relationships from a known node nodeId (string), maxDepth? (1-10, default: 3), skip? (default: 0) Deep diving into specific code relationships. Pagination for large graphs
natural_language_to_cypher AI-powered query generation - Convert natural language to Cypher queries using GPT-4 query (string) Advanced queries - currently requires OpenAI assistant setup

Tool Selection Guide

Start Here: search_codebase

  • Use when you don't know specific node IDs
  • Best for exploring new codebases
  • Returns rich context with code snippets

Go Deeper: traverse_from_node

  • Use when you have specific node IDs from search results
  • Perfect for understanding relationships and dependencies
  • Use skip parameter for pagination through large result sets

Advanced: natural_language_to_cypher

  • Requires additional OpenAI assistant configuration
  • Best for complex queries beyond simple search/traversal
  • Currently in development - may require setup

Framework Support

NestJS Framework Schema

The server provides deep understanding of NestJS patterns:

Node Types

  • Controllers: HTTP endpoint handlers with route analysis
  • Services: Business logic providers with dependency injection mapping
  • Modules: Application structure with import/export relationships
  • Guards: Authentication and authorization components
  • Pipes: Request validation and transformation
  • Interceptors: Request/response processing middleware
  • DTOs: Data transfer objects with validation decorators
  • Entities: Database models with relationship mapping

Relationship Types

  • Module System: MODULE_IMPORTS, MODULE_PROVIDES, MODULE_EXPORTS
  • Dependency Injection: INJECTS, PROVIDED_BY
  • HTTP API: EXPOSES, ACCEPTS, RESPONDS_WITH
  • Security: GUARDED_BY, TRANSFORMED_BY, INTERCEPTED_BY

Example Graph Structure

┌─────────────────┐    EXPOSES     ┌──────────────────┐
│   UserController│──────────────→│  POST /users     │
│   @Controller   │                │  @Post()         │
└─────────────────┘                └──────────────────┘
         │                                   │
      INJECTS                           ACCEPTS
         ↓                                   ↓
┌─────────────────┐                ┌──────────────────┐
│   UserService   │                │   CreateUserDto  │
│   @Injectable   │                │   @IsString()    │
└─────────────────┘                └──────────────────┘
         │
      MANAGES
         ↓
┌─────────────────┐
│   User Entity   │
│   @Entity()     │
└─────────────────┘

Configuration

Environment Variables

Variable Description Default
OPENAI_API_KEY OpenAI API key for embeddings and LLM Required
OPENAI_ASSISTANT_ID Reuse existing OpenAI assistant Optional
NEO4J_URI Neo4j database URI bolt://localhost:7687
NEO4J_USER Neo4j username neo4j
NEO4J_PASSWORD Neo4j password PASSWORD

Parse Options

Customize parsing behavior:

const parseOptions = {
  includePatterns: ['**/*.ts', '**/*.tsx'],
  excludePatterns: [
    'node_modules/',
    'dist/',
    'coverage/',
    '.d.ts',
    '.spec.ts',
    '.test.ts'
  ],
  maxFiles: 1000,
  frameworkSchemas: [NESTJS_FRAMEWORK_SCHEMA]
};

Limitations

Current Limitations

  1. Language Support: Currently supports TypeScript/NestJS only
  2. Framework Support: Primary focus on NestJS patterns
  3. File Size: Large files (>10MB) may cause parsing performance issues
  4. Memory Usage: Graph generation is memory-intensive for very large projects
  5. Vector Search: Requires OpenAI API for semantic search functionality
  6. Real-time Updates: No file watching - requires manual re-parsing for code changes
  7. Response Size: Large graph traversals can exceed token limits (25,000 tokens max)
  8. Neo4j Memory: Database memory limits can cause query failures on large graphs

Performance Considerations

  • Large Projects: Projects with >10,000 files may require increased memory allocation
  • Graph Traversal: Deep traversals (>5 levels) may be slow for highly connected graphs
  • Embedding Generation: Initial parsing with embeddings can take several minutes for large codebases
  • Neo4j Memory: Recommend at least 4GB RAM allocation for Neo4j with large graphs

Known Issues

  1. Complex Type Inference: Advanced TypeScript type gymnastics may not be fully captured
  2. Dynamic Imports: Runtime module loading not tracked in static analysis
  3. Decorator Arguments: Complex decorator argument patterns may not be fully parsed
  4. Monorepo Support: Limited support for complex monorepo structures

Troubleshooting

Common Issues

Neo4j Connection Failed

# Check if Neo4j is running
docker ps | grep neo4j

# Check Neo4j logs
docker logs codebase-neo4j

# Verify APOC plugin
curl -u neo4j:PASSWORD http://localhost:7474/db/neo4j/tx/commit \
  -H "Content-Type: application/json" \
  -d '{"statements":[{"statement":"CALL apoc.help(\"apoc\") YIELD name RETURN count(name) as count"}]}'

Neo4j Memory Issues

If you encounter errors like "allocation of an extra X MiB would use more than the limit":

# Increase Neo4j memory limits in docker-compose.yml
NEO4J_server_memory_heap_max__size=8G
NEO4J_server_memory_pagecache_size=4G
NEO4J_dbms_memory_transaction_total_max=8G

# Restart Neo4j
docker-compose restart neo4j

Token Limit Exceeded

If responses exceed 25,000 tokens:

// Reduce limit parameter
search_codebase({ query: "...", limit: 1 })

// Use pagination with skip
traverse_from_node({ nodeId: "...", maxDepth: 2, skip: 0 })
traverse_from_node({ nodeId: "...", maxDepth: 2, skip: 20 })

OpenAI API Issues

# Test API key
curl https://api.openai.com/v1/models \
  -H "Authorization: Bearer $OPENAI_API_KEY"

# Check embedding model availability
curl https://api.openai.com/v1/models/text-embedding-3-large \
  -H "Authorization: Bearer $OPENAI_API_KEY"

Parsing Failures

# Check TypeScript configuration
npx tsc --noEmit --project /path/to/tsconfig.json

# Verify file permissions
ls -la /path/to/project

# Check memory usage during parsing
node --max-old-space-size=8192 dist/mcp/mcp.server.js

Debug Mode

Enable detailed logging:

export DEBUG=mcp:*
export NODE_ENV=development

Contributing

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Commit your changes: git commit -m 'Add amazing feature'
  4. Push to the branch: git push origin feature/amazing-feature
  5. Open a Pull Request

Development Setup

# Install dependencies
npm install

# Run in development mode
npm run dev

# Run tests
npm test

# Lint code
npm run lint

# Format code
npm run format

License

This project is proprietary software. All rights reserved - see the LICENSE file for details.

Acknowledgments

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Topics

typescript neo4j openai developer-tools graph-database nestjs graphrag mcp-server claude-code ai-code-analysis code-graph-rag

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