Which integrations are available for this server?

Enables AI agents to interact with [Apache](/mcp/servers/integrations/apache) TinkerPop Gremlin Server and other Gremlin-compatible graph databases through natural language, providing tools for schema discovery, graph queries, relationship analysis, data import/export, and automatic enum discovery for property values. Enables AI agents to interact with Amazon [Neptune](/mcp/servers/integrations/neptune) and other Gremlin-compatible graph databases through natural language, providing tools for schema discovery, graph queries, relationship analysis, data import/export, and automatic enum discovery for property values.

Gremlin MCP Server

Release npm version License: MIT Node.js Version TypeScript

Connect AI agents like Claude, Cursor, and Windsurf to your graph databases!

An MCP (Model Context Protocol) server that enables AI assistants to interact with any Gremlin-compatible graph database through natural language. Query your data, discover schemas, analyze relationships, and manage graph data using simple conversations.

✨ What You Can Do

Talk to your graph database naturally:

🔍 "What's the structure of my graph?" - Automatic schema discovery
📊 "Show me all users over 30 and their connections" - Complex graph queries
🔗 "Find the shortest path between Alice and Bob" - Relationship analysis
📈 "Give me graph statistics and metrics" - Data insights
📥 "Import this GraphSON data" - Data loading
📤 "Export user data as CSV" - Data extraction
🧠 Smart enum discovery - AI learns your data's valid values automatically

🛠️ Available Tools

Your AI assistant gets access to these powerful tools:

Tool	Purpose	What It Does
🔍 get_graph_status	Health Check	Verify database connectivity and server status
📋 get_graph_schema	Schema Discovery	Get complete graph structure with nodes, edges, and relationships
⚡ run_gremlin_query	Query Execution	Execute any Gremlin traversal query with full syntax support
🔄 refresh_schema_cache	Cache Management	Force immediate refresh of cached schema information
📥 import_graph_data	Data Import	Load data from GraphSON, CSV, or JSON with batch processing
📤 export_subgraph	Data Export	Extract subgraphs to JSON, GraphSON, or CSV formats

🚀 Quick Setup

Step 1: Install

# The npx command will automatically install the package if needed # No separate installation step required

Alternative: Build from Source

# Clone and setup git clone https://github.com/kpritam/gremlin-mcp.git cd gremlin-mcp npm install npm run build

Step 2: Configure Your AI Client

Add this to your MCP client configuration:

Claude Desktop / Cursor / Windsurf

Using the published package (recommended):

{ "mcpServers": { "gremlin": { "command": "npx", "args": ["@kpritam/gremlin-mcp"], "env": { "GREMLIN_ENDPOINT": "localhost:8182", "LOG_LEVEL": "info" } } } }

From source:

{ "mcpServers": { "gremlin": { "command": "node", "args": ["/path/to/gremlin-mcp/dist/server.js"], "env": { "GREMLIN_ENDPOINT": "localhost:8182", "LOG_LEVEL": "info" } } } }

With Authentication

{ "mcpServers": { "gremlin": { "command": "npx", "args": ["@kpritam/gremlin-mcp"], "env": { "GREMLIN_ENDPOINT": "your-server.com:8182", "GREMLIN_USERNAME": "your-username", "GREMLIN_PASSWORD": "your-password", "GREMLIN_USE_SSL": "true" } } } }

Step 3: Start Your Gremlin Server

Make sure your Gremlin-compatible database is running:

# For Apache TinkerPop Gremlin Server ./bin/gremlin-server.sh start # Or using Docker docker run -p 8182:8182 tinkerpop/gremlin-server

Step 4: Test the Connection

Restart your AI client and try asking:

"Can you check if my graph database is connected and show me its schema?"

💡 Usage Examples

Schema Exploration

You ask: "What's the structure of my graph database?"

AI response: The AI calls get_graph_schema and tells you about your node types, edge types, and how they're connected.

Data Analysis

You ask: "Show me all people over 30 and their relationships"

AI response: The AI executes g.V().hasLabel('person').has('age', gt(30)).out().path() and explains the results in natural language.

Graph Metrics

You ask: "Give me some statistics about my graph"

AI response: The AI runs multiple queries to count nodes, edges, and analyze the distribution, then presents a summary.

Data Import

You ask: "Load this GraphSON data into my database"

AI response: The AI uses import_graph_data to process your data in batches and reports the import status.

🧠 Automatic Enum Discovery

Why this matters: AI agents work best when they know the exact valid values for properties. Instead of guessing or making invalid queries, they can use precise, real values from your data.

One of the most powerful features of this MCP server is Automatic Enum Discovery - it intelligently analyzes your graph data to discover valid property values and provides them as enums to AI agents.

🤔 The Problem It Solves

Without Enum Discovery:

AI: "I see this vertex has a 'status' property of type 'string'... Let me try querying with status='active'" Result: ❌ No results (actual values are 'CONFIRMED', 'PENDING', 'CANCELLED')

With Enum Discovery:

AI: "I can see the 'status' property has these exact values: ['CONFIRMED', 'PENDING', 'CANCELLED', 'WAITLISTED'] Let me query with status='CONFIRMED'" Result: ✅ Perfect results using real data values

💡 How It Works

The server automatically scans your graph properties and:

Identifies Low-Cardinality Properties - Properties with a reasonable number of distinct values
Extracts Real Values - Samples actual data from your graph
Provides as Enums - Includes valid values in the schema for AI agents

Example Output:

{ "name": "bookingStatus", "type": ["string"], "cardinality": "single", "enum": ["CONFIRMED", "PENDING", "CANCELLED", "WAITLISTED"], "sample_values": ["CONFIRMED", "PENDING"] }

🎯 Benefits for AI Agents

🎯 Accurate Queries - AI uses real values instead of guessing
⚡ Faster Results - No trial-and-error with invalid values
🧠 Better Understanding - AI learns your data vocabulary
📊 Smarter Analytics - Enables grouping and filtering with actual categories

⚙️ Configuration Options

Fine-tune enum discovery to match your data:

# Enable/disable enum discovery GREMLIN_ENUM_DISCOVERY_ENABLED="true" # Default: true # Control what gets detected as enum GREMLIN_ENUM_CARDINALITY_THRESHOLD="10" # Max distinct values for enum (default: 10) # Exclude specific properties GREMLIN_ENUM_PROPERTY_BLACKLIST="id,uuid,timestamp,createdAt,updatedAt" # Schema optimization GREMLIN_SCHEMA_MAX_ENUM_VALUES="10" # Limit enum values shown (default: 10) GREMLIN_SCHEMA_INCLUDE_SAMPLE_VALUES="false" # Reduce schema size (default: false)

🚫 Property Blacklist

Some properties should never be treated as enums:

Automatically Excluded:

High-cardinality properties (> threshold unique values)
Numeric IDs and UUIDs
Timestamps and dates
Long text fields

Manual Exclusion:

# Exclude specific properties by name GREMLIN_ENUM_PROPERTY_BLACKLIST="userId,sessionId,description,notes,content"

Common Blacklist Patterns:

id,uuid,guid - Unique identifiers
timestamp,createdAt,updatedAt,lastModified - Time fields
description,notes,comment,content,text - Free text fields
email,url,phone,address - Personal/contact data
hash,token,key,secret - Security-related fields

🛠️ Real-World Examples

E-commerce Graph:

{ "orderStatus": { "enum": ["PENDING", "PROCESSING", "SHIPPED", "DELIVERED", "CANCELLED"] }, "productCategory": { "enum": ["ELECTRONICS", "CLOTHING", "BOOKS", "HOME", "SPORTS"] }, "paymentMethod": { "enum": ["CREDIT_CARD", "PAYPAL", "BANK_TRANSFER", "CRYPTO"] } }

Social Network Graph:

{ "relationshipType": { "enum": ["FRIEND", "FAMILY", "COLLEAGUE", "ACQUAINTANCE"] }, "privacyLevel": { "enum": ["PUBLIC", "FRIENDS", "PRIVATE"] }, "accountStatus": { "enum": ["ACTIVE", "SUSPENDED", "DEACTIVATED"] } }

🔧 Tuning for Your Data

For Large Datasets:

GREMLIN_ENUM_CARDINALITY_THRESHOLD="5" # Stricter enum detection GREMLIN_SCHEMA_MAX_ENUM_VALUES="5" # Fewer values in schema

For Rich Categorical Data:

GREMLIN_ENUM_CARDINALITY_THRESHOLD="25" # More permissive detection GREMLIN_SCHEMA_MAX_ENUM_VALUES="20" # Show more enum values

For Performance-Critical Environments:

GREMLIN_ENUM_DISCOVERY_ENABLED="false" # Disable for faster schema loading GREMLIN_SCHEMA_INCLUDE_SAMPLE_VALUES="false" # Minimal schema size

This intelligent enum discovery transforms how AI agents interact with your graph data, making queries more accurate and insights more meaningful! 🎯

🗄️ Supported Databases

Works with any Gremlin-compatible graph database:

Database	Status	Notes
🟢 Apache TinkerPop	✅ Tested	Local development and CI testing
🟡 Amazon Neptune	🔧 Compatible	Designed for, not yet tested
🟡 JanusGraph	🔧 Compatible	Designed for, not yet tested
🟡 Azure Cosmos DB	🔧 Compatible	With Gremlin API
🟡 ArcadeDB	🔧 Compatible	With Gremlin support

⚙️ Configuration Options

Basic Configuration

# Required GREMLIN_ENDPOINT="localhost:8182" # Optional GREMLIN_USE_SSL="true" # Enable SSL/TLS GREMLIN_USERNAME="username" # Authentication GREMLIN_PASSWORD="password" # Authentication GREMLIN_IDLE_TIMEOUT="300" # Connection timeout in seconds (default: 300) LOG_LEVEL="info" # Logging level: error, warn, info, debug

Advanced Configuration

# Schema and performance tuning GREMLIN_ENUM_DISCOVERY_ENABLED="true" # Enable smart enum detection (default: true) GREMLIN_ENUM_CARDINALITY_THRESHOLD="10" # Max distinct values for enum detection (default: 10) GREMLIN_ENUM_PROPERTY_BLACKLIST="id,timestamp" # Exclude specific properties from enum detection GREMLIN_SCHEMA_INCLUDE_SAMPLE_VALUES="false" # Include sample values in schema (default: false) GREMLIN_SCHEMA_MAX_ENUM_VALUES="10" # Limit enum values shown (default: 10) GREMLIN_SCHEMA_INCLUDE_COUNTS="true" # Include vertex/edge counts in schema (default: true)

🔐 Security Considerations

⚠️ Important: This server is designed for development and trusted environments.

Current Limitations

Basic input sanitization (advanced injection protection in development)
No connection pooling or rate limiting
All Gremlin syntax is permitted
No audit logging for security monitoring

Recommended Security Practices

🔒 Use behind a firewall in production
🔑 Enable strong authentication on your Gremlin server
📊 Monitor query patterns and resource usage
🛡️ Consider a query proxy for additional security controls
🔄 Keep dependencies updated

🆘 Troubleshooting

Connection Issues

Problem	Solution
"Connection refused"	Verify Gremlin server is running: `curl http://localhost:8182/`
"Authentication failed"	Check `GREMLIN_USERNAME` and `GREMLIN_PASSWORD`
"Invalid endpoint"	Use format `host:port` or `host:port/g` for traversal source

Common Error Messages

"Schema cache failed" - Server couldn't discover graph structure (empty database?)
"Invalid query syntax" - Gremlin query has syntax errors
"Timeout" - Query took too long, check GREMLIN_IDLE_TIMEOUT

Testing Your Setup

# Test connection curl -f http://localhost:8182/ # Check server logs tail -f logs/gremlin-mcp.log # Verify schema endpoint curl http://localhost:8182/gremlin

🔧 Developer Documentation

The following sections are for developers who want to contribute to or modify the server.

Development Setup

# Clone and install git clone https://github.com/kpritam/gremlin-mcp.git cd gremlin-mcp npm install # Development with hot reload npm run dev # Run tests npm test npm run test:coverage npm run test:watch # Integration tests (requires running Gremlin server) GREMLIN_ENDPOINT=localhost:8182/g npm run test:it # All tests together (unit + integration) npm test && npm run test:it

Architecture

Full Type Safety: TypeScript + Effect functional programming patterns
Effect-based Architecture: Uses Effect.ts for composable, type-safe operations
Service-Oriented Design: Dependencies managed through Effect's Context.Tag patterns
Layer-Based Composition: Application built using Effect.Layer for dependency resolution
Comprehensive Testing: Unit + Integration tests with Effect testing patterns
Error Handling: Effect-based error management with custom error types

Project Structure

src/ ├── server.ts # Effect-based MCP server with graceful startup/shutdown ├── config.ts # Effect.Config-based configuration validation ├── constants.ts # Application constants integrated with Effect configuration ├── gremlin/ │ ├── service.ts # GremlinService using Effect.Context.Tag pattern │ ├── schema-service.ts # SchemaService with Effect dependency injection │ └── types.ts # TypeScript types and schemas ├── handlers/ # Effect-based MCP request handlers │ ├── tools.ts # Effect-based tool handlers │ ├── resources.ts # Effect-based resource handlers │ └── effect-runtime-bridge.ts # ManagedRuntime container for Effect execution └── utils/ # Effect-based utility modules ├── data-operations.ts # Effect-based graph data import/export operations ├── result-parser.ts # Gremlin result parsing with metadata extraction └── type-guards.ts # Runtime type checking functions

Available Scripts

Command	Purpose
`npm run build`	Compile TypeScript to JavaScript
`npm run dev`	Development mode with hot reload
`npm test`	Run unit test suite
`npm run lint`	Code linting with ESLint
`npm run format`	Code formatting with Prettier
`npm run validate`	Run all checks (format, lint, type-check, test)

Smart Schema Discovery

The server implements intelligent schema discovery with enumeration detection:

// Property with detected enum values { "name": "status", "type": ["string"], "cardinality": "single", "enum": ["Confirmed", "Pending", "Cancelled", "Waitlisted"] }

Contributing

Follow the rules in RULES.md
Run npm run validate before committing
Add tests for new functionality
Update documentation for user-facing changes
Ensure all tests pass

Testing Strategy

Unit Tests (tests/): Individual component testing
- Component isolation with comprehensive mocking
- Type safety validation with Zod schemas
- Fast execution without external dependencies
Integration Tests (tests/integration/): Full workflow testing
- Real Gremlin server connections via Docker
- End-to-end MCP protocol validation
- Database operations and query execution
CI Testing: Automated testing in GitHub Actions
- Unit tests run on every commit
- Integration tests run with Docker Gremlin server
- Both required for releases

📄 License

MIT License - feel free to use in your projects!

Questions? Check the troubleshooting guide or open an issue.

Gremlin MCP Server

✨ What You Can Do

🛠️ Available Tools

🚀 Quick Setup

Step 1: Install

Alternative: Build from Source

Step 2: Configure Your AI Client

Claude Desktop / Cursor / Windsurf

With Authentication

Step 3: Start Your Gremlin Server

Step 4: Test the Connection

💡 Usage Examples

Schema Exploration

Data Analysis

Graph Metrics

Data Import

🧠 Automatic Enum Discovery

🤔 The Problem It Solves

💡 How It Works

🎯 Benefits for AI Agents

⚙️ Configuration Options

🚫 Property Blacklist

🛠️ Real-World Examples

🔧 Tuning for Your Data

🗄️ Supported Databases

⚙️ Configuration Options

Basic Configuration

Advanced Configuration

🔐 Security Considerations

Current Limitations

Recommended Security Practices

🆘 Troubleshooting

Connection Issues

Common Error Messages

Testing Your Setup

🔧 Developer Documentation

Development Setup

Architecture

Project Structure

Available Scripts

Smart Schema Discovery

Contributing

Testing Strategy

📄 License

Resources

New MCP Servers

Latest Blog Posts

MCP directory API