Which integrations are available for this server?

Stores and queries code graph representations with vector indexing, enabling graph traversal, relationship analysis, and semantic search across parsed TypeScript/NestJS codebases. Provides deep understanding of NestJS architectural patterns including controllers, services, modules, dependency injection, HTTP endpoints, guards, pipes, and interceptors. Generates embeddings for semantic code search, powers natural language to Cypher query conversion, and enables vector-based similarity matching for code discovery. Parses TypeScript projects using AST analysis to extract code entities, relationships, and framework patterns for graph-based code understanding.

How do I use Code Graph Context?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Code Graph Context find all services that depend on the UserRepository" 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.

Code Graph Context

by andrew-hernandez-paragon

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

Code Graph Context

npm version MIT License TypeScript Neo4j NestJS OpenAI MCP

Give your AI coding assistant a photographic memory of your codebase.

Code Graph Context is an MCP server that builds a semantic graph of your TypeScript codebase, enabling Claude to understand not just individual files, but how your entire system fits together.

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.

                    ┌─────────────────────────────────────────────────────────────┐
                    │                     YOUR CODEBASE                           │
                    │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐    │
                    │  │ Service  │  │Controller│  │  Module  │  │  Entity  │    │
                    │  └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘    │
                    └───────┼─────────────┼─────────────┼─────────────┼──────────┘
                            │             │             │             │
                            ▼             ▼             ▼             ▼
                    ┌─────────────────────────────────────────────────────────────┐
                    │                   CODE GRAPH CONTEXT                        │
                    │                                                             │
                    │   AST Parser ──► Neo4j Graph ──► Vector Embeddings          │
                    │   (ts-morph)     (Relationships)  (Local or OpenAI)         │
                    │                                                             │
                    └─────────────────────────────────────────────────────────────┘
                                                │
                                                ▼
                    ┌─────────────────────────────────────────────────────────────┐
                    │                      CLAUDE CODE                            │
                    │                                                             │
                    │   "What services depend on UserService?"                    │
                    │   "What's the blast radius if I change this function?"      │
                    │   "Find all HTTP endpoints that accept a UserDTO"           │
                    │   "Refactor this across all 47 files that use it"           │
                    │                                                             │
                    └─────────────────────────────────────────────────────────────┘

Why Code Graph Context?

Without Code Graph	With Code Graph
Claude reads files one at a time	Claude understands the entire dependency tree
"What uses this?" requires manual searching	Instant impact analysis with risk scoring
Refactoring misses edge cases	Graph traversal finds every reference
Large codebases overwhelm context	Semantic search finds exactly what's relevant
Multi-file changes are error-prone	Swarm agents coordinate parallel changes

Related MCP server: Code Graph Knowledge System

Features

Multi-Project Support: Parse and query multiple projects in a single database with complete isolation
Semantic Search: Vector-based search using local or OpenAI embeddings to find relevant code
Natural Language Querying: Convert questions into Cypher queries
Framework-Aware: Built-in NestJS schema with ability to define custom framework patterns
Weighted Graph Traversal: Intelligent traversal scoring paths by importance and relevance
Workspace Support: Auto-detects Nx, Turborepo, pnpm, Yarn, and npm workspaces
Parallel & Async Parsing: Multi-threaded parsing with Worker threads for large codebases
Streaming Import: Chunked processing for projects with 100+ files
Incremental Parsing: Only reparse changed files
File Watching: Real-time graph updates on file changes
Impact Analysis: Assess refactoring risk (LOW/MEDIUM/HIGH/CRITICAL)
Dead Code Detection: Find unreferenced exports with confidence scoring
Duplicate Detection: Structural (AST hash) and semantic (embedding similarity) duplicates
Swarm Coordination: Multi-agent stigmergic coordination with pheromone decay

Architecture

TypeScript Source → AST Parser (ts-morph) → Neo4j Graph + Vector Embeddings → MCP Tools

Core Components:

src/core/parsers/typescript-parser.ts - AST parsing with ts-morph
src/storage/neo4j/neo4j.service.ts - Graph storage and queries
src/core/embeddings/embeddings.service.ts - Embedding service (local sidecar or OpenAI)
src/mcp/mcp.server.ts - MCP server and tool registration

Dual-Schema System:

Core Schema: AST-level nodes (ClassDeclaration, MethodDeclaration, ImportDeclaration, etc.)
Framework Schema: Semantic interpretation (NestController, NestService, HttpEndpoint, etc.)

Nodes have both coreType (AST) and semanticType (framework meaning), enabling queries like "find all controllers" while maintaining AST precision.

Quick Start

Prerequisites

Node.js >= 18
Python >= 3.10 (for local embeddings)
Docker (for Neo4j)

No API keys required. Local embeddings work out of the box using a Python sidecar.

1. Install

npm install -g code-graph-context
code-graph-context init  # Sets up Neo4j + Python sidecar + downloads embedding model

The init command handles everything:

Starts a Neo4j container via Docker
Creates a Python virtual environment
Installs embedding dependencies (PyTorch, sentence-transformers)
Downloads the default BGE-base embedding model

2. Configure Claude Code

claude mcp add --scope user code-graph-context -- code-graph-context

That's it. No API keys needed. Restart Claude Code and you're ready to go.

Want to use OpenAI instead? See Embedding Configuration below.

3. Parse Your Project

In Claude Code, say:

"Parse this project and build the code graph"

Claude will run parse_typescript_project and index your codebase.

Configuration Files

Claude Code stores MCP server configs in JSON files. The location depends on scope:

Scope	File	Use Case
User (global)	`~/.claude.json`	Available in all projects
Project	`.claude.json` in project root	Project-specific config
Local	`.mcp.json` in project root	Git-ignored local overrides

Manual Configuration

If you prefer to edit the config files directly:

~/.claude.json (user scope - recommended):

{
  "mcpServers": {
    "code-graph-context": {
      "command": "code-graph-context"
    }
  }
}

With OpenAI (optional):

{
  "mcpServers": {
    "code-graph-context": {
      "command": "code-graph-context",
      "env": {
        "OPENAI_EMBEDDINGS_ENABLED": "true",
        "OPENAI_API_KEY": "sk-your-key-here"
      }
    }
  }
}

From source installation:

{
  "mcpServers": {
    "code-graph-context": {
      "command": "node",
      "args": ["/absolute/path/to/code-graph-context/dist/cli/cli.js"]
    }
  }
}

Environment Variables

Variable	Required	Default	Description
`NEO4J_URI`	No	`bolt://localhost:7687`	Neo4j connection URI
`NEO4J_USER`	No	`neo4j`	Neo4j username
`NEO4J_PASSWORD`	No	`PASSWORD`	Neo4j password
`EMBEDDING_MODEL`	No	`BAAI/bge-base-en-v1.5`	Local embedding model (see Embedding Configuration)
`EMBEDDING_BATCH_SIZE`	No	`8`	Texts per embedding batch (lower = less memory, higher = faster)
`EMBEDDING_SIDECAR_PORT`	No	`8787`	Port for local embedding server
`EMBEDDING_DEVICE`	No	auto (`mps`/`cpu`)	Device for embeddings. Auto-detects MPS on Apple Silicon
`EMBEDDING_HALF_PRECISION`	No	`false`	Set `true` for float16 (uses ~0.5x memory)
`OPENAI_EMBEDDINGS_ENABLED`	No	`false`	Use OpenAI only when `true` and a nonempty API key is set
`OPENAI_API_KEY`	No*	-	Required when `OPENAI_EMBEDDINGS_ENABLED=true`; also enables `natural_language_to_cypher`

Core Capabilities

Semantic Code Search

Find code by describing what you need, not by memorizing file paths:

"Find where user authentication tokens are validated"
"Show me the database connection pooling logic"
"What handles webhook signature verification?"

Impact Analysis

Before you refactor, understand the blast radius:

┌─────────────────────────────────────────────────────────────┐
│ Impact Analysis: UserService.findById()                     │
├─────────────────────────────────────────────────────────────┤
│ Risk Level: HIGH                                            │
│                                                             │
│ Direct Dependents (12):                                     │
│   └── AuthController.login()                                │
│   └── ProfileController.getProfile()                        │
│   └── AdminService.getUserDetails()                         │
│   └── ... 9 more                                            │
│                                                             │
│ Transitive Dependents (34):                                 │
│   └── 8 controllers, 15 services, 11 tests                  │
│                                                             │
│ Affected Files: 23                                          │
│ Recommendation: Add deprecation warning before changing     │
└─────────────────────────────────────────────────────────────┘

Graph Traversal

Explore relationships in any direction:

UserController
    │
    ├── INJECTS ──► UserService
    │                   │
    │                   ├── INJECTS ──► UserRepository
    │                   │                   │
    │                   │                   └── MANAGES ──► User (Entity)
    │                   │
    │                   └── INJECTS ──► CacheService
    │
    └── EXPOSES ──► POST /users
                        │
                        └── ACCEPTS ──► CreateUserDTO

Dead Code Detection

Find code that can be safely removed:

Dead Code Analysis: 47 items found
├── HIGH confidence (23): Exported but never imported
│   └── formatLegacyDate() in src/utils/date.ts:45
│   └── UserV1DTO in src/dto/legacy/user.dto.ts:12
│   └── ... 21 more
├── MEDIUM confidence (18): Private, never called
└── LOW confidence (6): May be used dynamically

Duplicate Code Detection

Identify DRY violations across your codebase:

Duplicate Groups Found: 8

Group 1 (Structural - 100% identical):
├── validateEmail() in src/auth/validation.ts:23
└── validateEmail() in src/user/validation.ts:45
    Recommendation: Extract to shared utils

Group 2 (Semantic - 94% similar):
├── parseUserInput() in src/api/parser.ts:78
└── sanitizeInput() in src/webhook/parser.ts:34
    Recommendation: Review for consolidation

Swarm Coordination

Execute complex, multi-file changes with parallel AI agents.

The swarm system enables multiple Claude agents to work on your codebase simultaneously, coordinating through the code graph without stepping on each other.

                         ┌──────────────────┐
                         │   ORCHESTRATOR   │
                         │                  │
                         │ "Add JSDoc to    │
                         │  all services"   │
                         └────────┬─────────┘
                                  │
                    ┌─────────────┼─────────────┐
                    │             │             │
                    ▼             ▼             ▼
             ┌──────────┐  ┌──────────┐  ┌──────────┐
             │ Worker 1 │  │ Worker 2 │  │ Worker 3 │
             │          │  │          │  │          │
             │ Claiming │  │ Working  │  │ Claiming │
             │ AuthSvc  │  │ UserSvc  │  │ PaySvc   │
             └──────────┘  └──────────┘  └──────────┘
                    │             │             │
                    └─────────────┼─────────────┘
                                  │
                                  ▼
                    ┌─────────────────────────────┐
                    │      PHEROMONE TRAILS       │
                    │                             │
                    │  AuthService: [claimed]     │
                    │  UserService: [modifying]   │
                    │  PayService:  [claimed]     │
                    │  CacheService: [available]  │
                    │                             │
                    └─────────────────────────────┘

Two Coordination Mechanisms

1. Pheromone System (Stigmergic)

Agents leave markers on code nodes that decay over time—like ants leaving scent trails:

Pheromone	Half-Life	Meaning
`exploring`	2 min	"I'm looking at this"
`claiming`	1 hour	"This is my territory"
`modifying`	10 min	"I'm actively changing this"
`completed`	24 hours	"I finished work here"
`warning`	Never	"Don't touch this"
`blocked`	5 min	"I'm stuck"

Self-healing: If an agent crashes, its pheromones decay and the work becomes available again.

2. Task Queue (Blackboard)

Explicit task management with dependencies:

┌─────────────────────────────────────────────────────────────┐
│                        TASK QUEUE                           │
├─────────────────────────────────────────────────────────────┤
│ [available] Add JSDoc to UserService         priority: high │
│ [claimed]   Add JSDoc to AuthService         agent: worker1 │
│ [blocked]   Update API docs ─────────────────► depends on ──┤
│ [in_progress] Add JSDoc to PaymentService    agent: worker2 │
│ [completed] Add JSDoc to CacheService        ✓              │
└─────────────────────────────────────────────────────────────┘

Swarm Tools

Tool	Purpose
`swarm_post_task`	Add a task to the queue
`swarm_get_tasks`	Query tasks with filters
`swarm_claim_task`	Claim/start/release a task
`swarm_complete_task`	Complete/fail/request review
`swarm_pheromone`	Leave a marker on a code node
`swarm_sense`	Query what other agents are doing
`swarm_cleanup`	Remove pheromones after completion

Example: Parallel Refactoring

// Orchestrator decomposes the task and creates individual work items
swarm_post_task({
  projectId: "backend",
  swarmId: "swarm_rename_user",
  title: "Update UserService.findUserById",
  description: "Rename getUserById to findUserById in UserService",
  type: "refactor",
  createdBy: "orchestrator"
})

// Workers claim and execute tasks
swarm_claim_task({ projectId: "backend", swarmId: "swarm_rename_user", agentId: "worker_1" })
// ... do work ...
swarm_complete_task({ taskId: "task_1", agentId: "worker_1", action: "complete", summary: "Renamed method" })

Install the Swarm Skill

For optimal swarm execution, install the included Claude Code skill that teaches agents the coordination protocol:

# Copy to your global skills directory
mkdir -p ~/.claude/skills
cp -r skills/swarm ~/.claude/skills/

Or for a specific project:

cp -r skills/swarm .claude/skills/

The skill provides:

Worker agent protocol with step-by-step workflow
Multi-phase orchestration patterns (discovery, contracts, implementation, validation)
Common failure modes and how to prevent them
Complete tool reference

Once installed, just say "swarm" or "parallel agents" and Claude will use the skill automatically.

See skills/swarm/SKILL.md for the full documentation.

All Tools

Tool	Description
Discovery
`list_projects`	List parsed projects in the database
`search_codebase`	Semantic search using vector embeddings
`traverse_from_node`	Explore relationships from a node
`natural_language_to_cypher`	Convert questions to Cypher queries
Analysis
`impact_analysis`	Assess refactoring risk (LOW/MEDIUM/HIGH/CRITICAL)
`detect_dead_code`	Find unreferenced exports and methods
`detect_duplicate_code`	Find structural and semantic duplicates
Parsing
`parse_typescript_project`	Build the graph from source
`check_parse_status`	Monitor async parsing jobs
`start_watch_project`	Auto-update graph on file changes
`stop_watch_project`	Stop file watching
`list_watchers`	List active file watchers
Swarm
`swarm_post_task`	Add task to the queue
`swarm_get_tasks`	Query tasks
`swarm_claim_task`	Claim/start/release tasks
`swarm_complete_task`	Complete/fail/review tasks
`swarm_pheromone`	Leave coordination markers
`swarm_sense`	Query what others are doing
`swarm_cleanup`	Clean up after swarm completion
Utility
`test_neo4j_connection`	Verify database connectivity

Tool Workflow Patterns

Pattern 1: Discovery → Focus → Deep Dive

list_projects → search_codebase → traverse_from_node → traverse (with skip for pagination)

Pattern 2: Pre-Refactoring Safety

search_codebase("function to change") → impact_analysis(nodeId) → review risk level

Pattern 3: Code Health Audit

detect_dead_code → detect_duplicate_code → prioritize cleanup

Pattern 4: Multi-Agent Work

swarm_post_task → swarm_claim_task → swarm_complete_task → swarm_get_tasks(includeStats) → swarm_cleanup

Multi-Project Support

All query tools require projectId for isolation. You can use:

Project ID: proj_a1b2c3d4e5f6 (auto-generated)
Project name: my-backend (from package.json)
Project path: /path/to/project (resolved automatically)

// These all work:
search_codebase({ projectId: "my-backend", query: "auth" })
search_codebase({ projectId: "proj_a1b2c3d4e5f6", query: "auth" })
search_codebase({ projectId: "/path/to/my-backend", query: "auth" })

Framework Support

NestJS (Built-in)

Deep understanding of NestJS patterns:

Controllers with route analysis (@Controller, @Get, @Post, etc.)
Services with dependency injection mapping (@Injectable)
Modules with import/export relationships (@Module)
Guards, Pipes, Interceptors as middleware chains
DTOs with validation decorators (@IsString, @IsEmail, etc.)
Entities with TypeORM relationship mapping

NestJS-Specific Relationships:

INJECTS - Dependency injection
EXPOSES - Controller exposes HTTP endpoint
MODULE_IMPORTS, MODULE_PROVIDES, MODULE_EXPORTS - Module system
GUARDED_BY, TRANSFORMED_BY, INTERCEPTED_BY - Middleware

Custom Framework Schemas

The parser is config-driven. Define your own framework patterns:

// Example: Custom React schema
const REACT_SCHEMA = {
  name: 'react',
  decoratorPatterns: [
    { pattern: /^use[A-Z]/, semanticType: 'ReactHook' },
    { pattern: /^with[A-Z]/, semanticType: 'HOC' },
  ],
  nodeTypes: [
    { coreType: 'FunctionDeclaration', condition: (node) => node.name?.endsWith('Provider'), semanticType: 'ContextProvider' },
  ],
  relationships: [
    { type: 'PROVIDES_CONTEXT', from: 'ContextProvider', to: 'ReactHook' },
  ]
};

The dual-schema system means every node has:

coreType: AST-level (ClassDeclaration, FunctionDeclaration)
semanticType: Framework meaning (NestController, ReactHook)

This enables queries like "find all hooks that use context" while maintaining AST precision for refactoring.

Embedding Configuration

Local BGE-base embeddings (768 dimensions) are the default. No API key is needed. The Python sidecar starts automatically on first use.

An API key alone only enables natural_language_to_cypher. OpenAI embeddings require both OPENAI_EMBEDDINGS_ENABLED=true and a nonempty OPENAI_API_KEY. An explicit false overrides the deprecated OPENAI_ENABLED flag. Request failures never switch providers or models.

Use the same explicit EMBEDDING_MODEL in every client sharing a sidecar and graph. The server rejects a sidecar whose model or vector dimensions differ from the configured model, including different models with equal dimensions.

The sidecar uses MPS (Apple Silicon GPU) when available, falling back to CPU. It auto-shuts down after 3 minutes of inactivity to free memory, and restarts lazily when needed (~15-20s).

Device override: Set EMBEDDING_DEVICE=cpu to force CPU if MPS causes issues.
Half precision: Set EMBEDDING_HALF_PRECISION=true to load the model in float16, roughly halving memory usage.

Available Models

Set via the EMBEDDING_MODEL environment variable:

Model	Dimensions	Description
`BAAI/bge-base-en-v1.5` (default)	768	General text retrieval, used for both code and session memory
`codesage/codesage-base-v2`	1024	Code-specific encoder
`Qodo/Qodo-Embed-1-1.5B`	1536	Larger code-specific encoder
`sentence-transformers/all-MiniLM-L6-v2`	384	Small general text encoder
`nomic-ai/nomic-embed-text-v1.5`	768	General text encoder
`sentence-transformers/all-mpnet-base-v2`	768	General text encoder
`BAAI/bge-small-en-v1.5`	384	Smaller BGE encoder

Dimensions describe vector size, not retrieval quality. Compare models using queries and relevant results from your own code and notes.

Example: Use a lightweight model on a low-memory machine:

claude mcp add --scope user code-graph-context \
  -e EMBEDDING_MODEL=BAAI/bge-base-en-v1.5 \
  -- code-graph-context

Switching Models

Changing models requires re-embedding all affected code and session memory. Re-parsing code alone does not convert note or bookmark vectors. Equal vector dimensions do not make two models compatible.

Back up the graph first. Preserve node IDs, note content, and relationships by replacing embedding properties in place. Vector indexes are shared across projects, so changing index dimensions requires coordinating every affected project and client. Updating the package does not migrate existing vectors.

When upgrading a graph built with the previous CodeSage default, explicitly keep EMBEDDING_MODEL=codesage/codesage-base-v2 until you migrate it. Existing BGE users can keep their configuration. New graphs default to BGE.

Using OpenAI Instead

To use OpenAI embeddings:

claude mcp add --scope user code-graph-context \
  -e OPENAI_EMBEDDINGS_ENABLED=true \
  -e OPENAI_API_KEY=sk-your-key-here \
  -- code-graph-context

Troubleshooting

MCP Server Not Connecting

# Check the server is registered
claude mcp list

# Verify Neo4j is running
docker ps | grep neo4j

# Test manually
code-graph-context status

Embedding Errors

"Failed to generate embedding" — The local sidecar may not have started. Check:

# Verify Python deps are installed
code-graph-context status

# Re-run init to fix sidecar setup
code-graph-context init

Out of memory (large model on 16GB machine) — Switch to a lighter model:

claude mcp add --scope user code-graph-context \
  -e EMBEDDING_MODEL=BAAI/bge-base-en-v1.5 \
  -- code-graph-context

Using OpenAI and getting auth errors — Ensure your key is configured:

claude mcp remove code-graph-context
claude mcp add --scope user code-graph-context \
  -e OPENAI_EMBEDDINGS_ENABLED=true \
  -e OPENAI_API_KEY=sk-your-key-here \
  -- code-graph-context

Neo4j Memory Issues

For large codebases, increase memory limits:

# Stop and recreate with more memory
code-graph-context stop
code-graph-context init --memory 4G

Parsing Timeouts

Use async mode for large projects:

parse_typescript_project({
  projectPath: "/path/to/project",
  tsconfigPath: "/path/to/project/tsconfig.json",
  async: true  // Returns immediately, poll with check_parse_status
})

CLI Commands

code-graph-context init [options]   # Set up Neo4j + Python sidecar + embedding model
code-graph-context status           # Check Docker/Neo4j/sidecar status
code-graph-context stop             # Stop Neo4j container

Init options:

-p, --port <port> - Bolt port (default: 7687)
--http-port <port> - Browser port (default: 7474)
--password <password> - Neo4j password (default: PASSWORD)
-m, --memory <size> - Heap memory (default: 2G)
-f, --force - Recreate container

Contributing

git clone https://github.com/andrew-hernandez-paragon/code-graph-context.git
cd code-graph-context
npm install
npm run build
npm run dev  # Watch mode

Conventional Commits: feat|fix|docs|refactor(scope): description

License

MIT - see LICENSE

Links

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Maintenance

ActivityMaintained

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Code Graph Context

Why Code Graph Context?

Features

Architecture

Quick Start

Prerequisites

1. Install

2. Configure Claude Code

3. Parse Your Project

Configuration Files

Manual Configuration

Environment Variables

Core Capabilities

Semantic Code Search

Impact Analysis

Graph Traversal

Dead Code Detection

Duplicate Code Detection

Swarm Coordination

Two Coordination Mechanisms

1. Pheromone System (Stigmergic)

2. Task Queue (Blackboard)

Swarm Tools

Example: Parallel Refactoring

Install the Swarm Skill

All Tools

Tool Workflow Patterns

Multi-Project Support

Framework Support

NestJS (Built-in)

Custom Framework Schemas

Embedding Configuration

Available Models

Switching Models

Using OpenAI Instead

Troubleshooting

MCP Server Not Connecting

Embedding Errors

Neo4j Memory Issues

Parsing Timeouts

CLI Commands

Contributing

License

Links

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Codebase Contextifier 9000

Code Graph Knowledge System

Axon Pro

GraphSense MCP Server