CodeGraph MCP
Exports the indexed code graph (classes, methods, edges) to Neo4j for visual exploration, graph queries, and analyzing dependencies such as call chains and blast radius.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CodeGraph MCPFind all code that calls the function authenticateUser"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeGraph MCP - AI-Powered Code Intelligence for VS Code Copilot
A local, incrementally-updated codebase index combining graph database (dependency tracking) and vector embeddings (semantic search) in a single SQLite file, exposed to VS Code Copilot via MCP (Model Context Protocol).
Why CodeGraph MCP?
Semantic Code Search: Find code by concept, not just text matching
Dependency Analysis: Understand blast radius before making changes
Dead Code Detection: Find unused code through graph analysis
Incremental Updates: Only re-index changed files (git diff-based)
Local-First: No cloud dependencies, runs entirely on your machine
Multi-Language: C#, TypeScript/Angular, YAML pipelines out of the box
Related MCP server: code-memory-rs
Architecture
┌─────────────────────────────────────────────────────────────┐
│ VS Code Copilot │
│ (Asks questions about your codebase) │
└────────────────────────┬────────────────────────────────────┘
│ MCP Protocol
▼
┌─────────────────────────────────────────────────────────────┐
│ CodeGraph MCP Server │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
│ │ search_code │ │ get_call_ │ │ get_related_ │ │
│ │ (semantic) │ │ graph │ │ files │ │
│ └──────────────┘ └──────────────┘ └──────────────────┘ │
└────────────────────────┬────────────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ SQLite Database │
│ .repo-index/index.db │
├──────────────────────────────┤
│ Graph: Nodes + Edges │
│ - Classes, Methods │
│ - Calls, Implements │
│ - Injects, Registers │
├──────────────────────────────┤
│ Vectors: Code Chunks │
│ - Method-level embeddings │
│ - Semantic similarity │
└──────────────────────────────┘
│
(Optional)
▼
┌──────────────────────────────┐
│ Neo4j (Visualization) │
│ Graph queries & exploration│
└──────────────────────────────┘Quick Start
Prerequisites
Node.js 18+
Ollama (for local embeddings) OR Azure OpenAI
Optional: Docker (for Neo4j graph visualization)
Optional: Neo4j Desktop (alternative to Docker)
Optional: Beekeeper Studio (for SQLite database browser)
1. Install Dependencies
cd /path/to/codegraph-mcp
npm install2. Choose Your Embedding Provider
Option A: Ollama (Recommended - Free & Local)
# Install Ollama from https://ollama.ai
# Pull embedding model
ollama pull qwen3-embedding
# Set environment variables
export EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings"
export EMBEDDING_MODEL="qwen3-embedding"Option B: Azure OpenAI
export EMBEDDING_ENDPOINT="https://<your-instance>.openai.azure.com/openai/deployments/<model>/embeddings?api-version=2024-02-01"
export EMBEDDING_API_KEY="your-api-key"
export EMBEDDING_MODEL="text-embedding-ada-002" # or text-embedding-3-small3. Index Your Repository
Full Index (Graph + Embeddings)
cd /path/to/your/repo
EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings" \
EMBEDDING_MODEL="qwen3-embedding" \
node /path/to/codegraph-mcp/src/indexer.jsPerformance:
Graph extraction: ~3 minutes (9,611 nodes, 46,693 edges for a ~500-file repo)
Embeddings: ~2-2.5 hours with parallel processing (10 concurrent requests)
Database size: ~100-110 MB
Graph-Only Index (Fast, No Embeddings)
For immediate graph visualization without waiting for embeddings:
Comment out embedding code in
src/indexer.js(lines 159-199)Run indexer (completes in ~3 minutes)
Later, uncomment and re-run to add embeddings
4. Configure VS Code Copilot
Copy the MCP config to your repository:
cd /path/to/your/repo
mkdir -p .vscode
cp /path/to/codegraph-mcp/.vscode-mcp-example.json .vscode/mcp.jsonEdit .vscode/mcp.json and update the path:
{
"servers": {
"repo-index": {
"command": "node",
"args": ["/absolute/path/to/codegraph-mcp/src/mcpServer.js"],
"env": {
"EMBEDDING_ENDPOINT": "http://localhost:11434/api/embeddings",
"EMBEDDING_MODEL": "qwen3-embedding"
}
}
}
}5. Reload VS Code
Cmd+Shift+P → "Developer: Reload Window"Usage
MCP Tools Available to Copilot
1. search_code - Semantic Search
Find code by natural language, not just keywords.
Example queries:
Find JWT token validation logic
Show me retry mechanisms
Where is error handling implemented?
Find code that calculates profit margins2. get_call_graph - Dependency Analysis
Understand what calls what, and blast radius analysis.
Example queries:
What's the blast radius for AuthenticationHelper?
Show me what depends on MarginController
What does UserService call?
Find all callers of ValidateToken method3. get_related_files - Discover Connected Code
Find files related through call edges.
Example queries:
What files are related to Program.cs?
Show me files connected to this serviceBest Practices for Copilot Queries
Good (Uses MCP tools):
What's the impact of changing the authorization middleware?
Find all implementations of IRepository pattern
Show me error handling across the codebaseBetter (Explicit tool usage):
Use get_call_graph to analyze the blast radius of AuthHelper with depth 3
Use search_code to find margin calculation patternsGraph Visualization with Neo4j
Export your SQLite graph to Neo4j for visual exploration:
Setup Neo4j
Option A: Docker (Recommended - Quick & Easy)
# Pull and run Neo4j
docker run -d \
--name neo4j-codegraph \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/password \
neo4j:latest
# Check status
docker ps | grep neo4j-codegraphTo stop/start later:
docker stop neo4j-codegraph
docker start neo4j-codegraphOption B: Neo4j Desktop
Install Neo4j Desktop
Create a local database
Set password
Export to Neo4j
cd /path/to/your/repo
NEO4J_PASSWORD="password" \
node /path/to/codegraph-mcp/export-to-neo4j.js .Explore in Neo4j Browser
Open http://localhost:7474 and try these queries:
// View all classes
MATCH (n:Class) RETURN n LIMIT 50
// Find call chains
MATCH path=(a:Method)-[:CALLS*1..3]->(b:Method)
RETURN path LIMIT 25
// DI registrations (interface → implementation)
MATCH (i:Interface)<-[:REGISTERS]-(impl)
RETURN i, impl
// Dead code (no incoming references)
MATCH (n:Method)
WHERE NOT ()-[:CALLS]->(n)
RETURN n.name, n.path
// Blast radius from a specific method
MATCH path=(caller)-[:CALLS*1..3]->(target:Method {name: 'Authenticate'})
RETURN pathDatabase Access with Beekeeper Studio
To browse the SQLite database directly:
Install Beekeeper Studio
File → New Connection
Connection Type: SQLite
Database File:
/path/to/your/repo/.repo-index/index.dbConnect
Useful Queries
-- Count nodes by type
SELECT kind, COUNT(*)
FROM nodes
GROUP BY kind;
-- Count edges by type
SELECT kind, COUNT(*)
FROM edges
GROUP BY kind;
-- Find methods with most callers
SELECT n.name, n.path, COUNT(e.src_id) as caller_count
FROM nodes n
JOIN edges e ON e.dst_id = n.id AND e.kind = 'calls'
WHERE n.kind = 'method'
GROUP BY n.id
ORDER BY caller_count DESC
LIMIT 20;
-- Find classes with no callers (potential dead code)
SELECT n.name, n.path
FROM nodes n
WHERE n.kind = 'class'
AND NOT EXISTS (
SELECT 1 FROM edges e WHERE e.dst_id = n.id
);
-- View embedding statistics
SELECT
COUNT(*) as total_chunks,
AVG(LENGTH(text)) as avg_chunk_size,
SUM(LENGTH(embedding)) / COUNT(*) as avg_embedding_size
FROM chunks;Nightly Refresh (Incremental Updates)
Set up automated indexing to keep your index fresh:
Manual Refresh
cd /path/to/your/repo
/path/to/codegraph-mcp/nightly-refresh.shAutomated (Cron - macOS/Linux)
crontab -e
# Add line (runs daily at 2 AM):
0 2 * * * /path/to/your/repo/nightly-refresh-index.sh >> /path/to/your/repo/.repo-index/refresh.log 2>&1The indexer automatically:
Fetches latest git changes
Identifies modified files since last index
Only re-indexes changed files
Preserves existing embeddings for unchanged code
Project Layout
codegraph-mcp/
├── schema.sql # Database schema (graph + vectors)
├── SCHEMA.md # Design rationale
├── src/
│ ├── db.js # SQLite connection
│ ├── indexer.js # Main indexing engine
│ ├── graph.js # Call graph queries
│ ├── search.js # Vector similarity search
│ ├── embeddings.js # Embedding provider abstraction
│ ├── mcpServer.js # MCP protocol server
│ └── extractors/
│ ├── csharp.js # C# symbol extraction
│ ├── angular.js # TypeScript/Angular extraction
│ └── yaml.js # YAML pipeline extraction
├── export-to-neo4j.js # Neo4j export utility
├── nightly-refresh.sh # Incremental update script
└── .vscode-mcp-example.json # MCP config templateLanguage Support
C# (.cs)
Nodes: Classes, interfaces, records, structs, methods, functions
Edges:
calls: Method invocationsimplements: Interface/base class relationshipsinjects: Constructor/primary constructor DIregisters: DI container registrations (AddScoped<I, Impl>())
Features: C# 12 primary constructors, expression-bodied members
TypeScript/Angular (.ts, .tsx, .js, .jsx)
Nodes: Classes (by decorator:
@Component,@Injectable,@NgModule,@Directive,@Pipe)Edges:
calls: Method invocationsinjects: Constructor parameter DIimports: Relative import statements
Limitations: Template files (
.html) not yet indexed
YAML (.yml, .yaml)
Nodes: Azure DevOps stages/jobs, GitHub Actions, k8s resources, docker-compose services
Edges: None (structural only)
Features: Templated YAML skipped structurally but embedded for semantic search
Performance Optimizations
Parallel Embedding Processing
The indexer processes embeddings in batches of 10 concurrent requests:
Sequential: ~1,100 chunks/hour
Parallel (10x): ~2,800 chunks/hour
Speedup: 2.5x faster
Content Hashing
Only generates new embeddings for changed code:
const hash = sha256(chunk.text);
const existing = db.prepare(
`SELECT id FROM chunks WHERE node_id = ? AND content_hash = ?`
).get(chunk.nodeId, hash);
if (!existing) {
chunksToEmbed.push({ ...chunk, hash });
}Incremental Git Diff
const diff = getChangedFiles(lastCommit);
targets = diff.filter(d => EXT_LANG[path.extname(d.filePath)]);Known Limitations & Future Improvements
Current Simplifications
Regex-based extraction: For production, replace with:
C#: Roslyn analyzer (
Microsoft.CodeAnalysis.CSharp)TypeScript: ts-morph or tree-sitter
Benefit: 100% accuracy vs ~95% with regex
Brute-force vector search: Fine for <50K chunks. For larger repos:
Use
sqlite-vec, Qdrant, or pgvectorImplement ANN (Approximate Nearest Neighbor) index
Angular templates: HTML templates not indexed
Missing:
(click)="handler()"→ call edges
Non-generic DI: Only
AddScoped<I, Impl>()supportedMissing:
AddScoped(typeof(I), typeof(Impl))
Planned Features
Multi-repo indexing (monorepo support)
Python extractor
Java/Kotlin extractor
Workspace-wide refactoring suggestions
Code smell detection via graph patterns
Historical analysis (code churn, hotspots)
Troubleshooting
MCP Server Not Connecting
Check
.vscode/mcp.jsonpath is absoluteReload VS Code:
Cmd+Shift+P→ "Developer: Reload Window"Check Output panel:
View→Output→ Select "MCP: repo-index"
Ollama Embedding Errors
# Check Ollama is running
curl http://localhost:11434/api/tags
# Verify model is installed
ollama list
# Re-pull if needed
ollama pull qwen3-embeddingSlow Indexing
Graph only: Disable embeddings temporarily (comment lines 159-199 in indexer.js)
Parallel batching: Increase
PARALLEL_BATCH_SIZEin indexer.js (default: 10)Smaller model: Use faster embedding models (lower dimensions)
Database Locked Errors
SQLite doesn't support concurrent writes. If running multiple indexers:
Wait for current indexing to complete
Use file locking wrapper
Run indexers sequentially
Neo4j Docker Issues
# Check if Neo4j container is running
docker ps | grep neo4j
# View Neo4j logs
docker logs neo4j-codegraph
# Restart Neo4j
docker restart neo4j-codegraph
# Remove and recreate (clears data!)
docker rm -f neo4j-codegraph
docker run -d \
--name neo4j-codegraph \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/password \
neo4j:latestContributing
Contributions welcome! Priority areas:
Language extractors (Python, Java, Go, Rust)
ANN vector index integration
Roslyn/ts-morph replacement for regex parsers
Performance optimizations
License
MIT License - See LICENSE file
Name Suggestions
CodeGraph MCP - Current working name, emphasizes the graph + MCP integration
Alternative names to consider:
RepoMind MCP - AI-powered repository knowledge
CodeIndex Pro - Professional code intelligence
GraphCode MCP - Graph-based code understanding
CodeAtlas MCP - Maps your entire codebase
DevGraph - Developer-focused graph database
RepoGraph AI - AI-powered repository graphing
Built for developers who want to understand their codebase deeply, not just search it superficially.
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