ConceptNet MCP Server

A Model Context Protocol (MCP) server that provides seamless access to the ConceptNet knowledge graph through FastMCP framework.

Overview

ConceptNet MCP provides AI assistants and applications with structured access to ConceptNet's semantic knowledge through four powerful MCP tools:

Concept Lookup: Get detailed information about specific concepts
Concept Query: Search and filter concepts with advanced criteria
Related Concepts: Find concepts connected through semantic relationships
Concept Relatedness: Calculate semantic similarity between concepts

Features

🚀 FastMCP Integration: Built on the modern FastMCP framework for optimal performance
🔍 Comprehensive Search: Advanced querying with language filtering and pagination
🌐 Multi-language Support: Access ConceptNet's multilingual knowledge base
📊 Semantic Analysis: Calculate relatedness scores between concepts
🔄 Async Operations: Full async/await support for non-blocking operations
📝 Type Safety: Complete Pydantic v2 type validation and IDE support
🧪 Production Ready: Error handling, logging, and testing
⚡ Optimized Output Formats: Choose between minimal (~96% smaller) or comprehensive responses

Output Formats

ConceptNet MCP Server supports two output formats for all tools to optimize performance and reduce token usage:

Minimal Format (Default - Recommended)

Size: ~96% smaller than verbose format
Optimized: Designed specifically for LLM consumption
Content: Essential data only - concepts, relationships, similarity scores
Performance: Faster processing and reduced API costs
Usage: Perfect for most AI applications and chat interfaces

Verbose Format

Size: Full ConceptNet response data
Content: Complete metadata, statistics, analysis, and original API responses
Usage: Detailed analysis, debugging, or when full context is needed
Backward Compatibility: Maintains compatibility with existing integrations

Setting the Format

All tools accept a verbose parameter:

{
  "name": "concept_lookup",
  "arguments": {
    "term": "artificial intelligence",
    "verbose": false  // Default: minimal format
  }
}

{
  "name": "related_concepts",
  "arguments": {
    "term": "machine learning",
    "verbose": true   // Full detailed format
  }
}

Examples of size difference:

Minimal: {"concept": "dog", "relationships": {"IsA": ["animal", "mammal"]}}
Verbose: Full ConceptNet response with complete metadata, statistics, timestamps, etc.

Quick Start

Installation

# Clone the repository
git clone https://github.com/infinitnet/conceptnet-mcp.git
cd conceptnet-mcp

# Install in development mode
pip install -e .

Running the MCP Server

The server supports both stdio (for desktop MCP clients) and HTTP (for web clients) transport modes:

Stdio Transport (Default - for desktop MCP clients)

# Start with stdio transport (default)
conceptnet-mcp

# Or explicitly specify stdio
conceptnet-mcp-stdio

# Or use Python module
python -m conceptnet_mcp.server

HTTP Transport (for web clients)

# Start HTTP server on localhost:3001
conceptnet-mcp-http

# Or with custom host/port
python -c "from conceptnet_mcp.server import run_http_server; run_http_server('0.0.0.0', 8080)"

Development Modes

# Development mode with debug logging
conceptnet-mcp-dev

# Production mode with optimized logging
conceptnet-mcp-prod

MCP Client Integration

For Desktop MCP Clients (stdio transport)

Add to your MCP client configuration:

{
  "mcpServers": {
    "conceptnet": {
      "command": "python",
      "args": ["-m", "conceptnet_mcp.server"]
    }
  }
}

For Web Applications (HTTP transport)

Add to your MCP client configuration:

{
  "mcpServers": {
    "conceptnet": {
      "command": "python",
      "args": ["-m", "conceptnet_mcp.server", "--transport", "http", "--port", "3001"]
    }
  }
}

Or start the HTTP server manually and connect to:

http://localhost:3001

// Example web client connection
const client = new MCPClient('http://localhost:3001');
await client.connect();

☁️ Cloudflare Workers Deployment

Deploy ConceptNet MCP Server to Cloudflare's global edge network for worldwide access and automatic scaling using a FastAPI-based implementation optimized for Python Workers.

Architecture

The Cloudflare Workers deployment uses a completely different architecture from the standard FastMCP server:

FastAPI Framework: Manual MCP protocol implementation using FastAPI for HTTP routing
Standard Workers Pattern: Uses fetch(request, env, ctx) handler (no Durable Objects)
Native HTTP Client: Custom CloudflareHTTPClient using Workers' native fetch() API
Manual MCP Protocol: JSON-RPC 2.0 MCP messages handled directly without FastMCP framework

Benefits

🌍 Global Edge Network: Low-latency access worldwide via Cloudflare's CDN
🚀 Auto-scaling: Serverless scaling based on demand with zero cold starts
🔄 Dual Transport Support: Both SSE and Streamable HTTP endpoints for maximum compatibility
🤖 Remote MCP Access: Enable AI agents to access ConceptNet from anywhere
💰 Cost-effective: Pay only for actual usage with generous free tier

Quick Deploy

# Clone and navigate to Workers directory
git clone https://github.com/infinitnet/conceptnet-mcp.git
cd conceptnet-mcp/cloudflare-workers

# Install Wrangler CLI
npm install -g wrangler

# Authenticate and deploy
wrangler login
wrangler deploy

Usage After Deployment

Your ConceptNet MCP Server will be available at:

# Streamable HTTP Transport (recommended for MCP clients)
https://your-worker.your-domain.workers.dev/mcp

# SSE Transport (legacy support)
https://your-worker.your-domain.workers.dev/sse

# Tools listing endpoint
https://your-worker.your-domain.workers.dev/tools

Example remote client connection (direct HTTP):

import httpx
import json

# Connect to your deployed Workers instance
async with httpx.AsyncClient() as client:
    response = await client.post(
        "https://your-worker.your-domain.workers.dev/mcp",
        json={
            "jsonrpc": "2.0",
            "id": 1,
            "method": "tools/call",
            "params": {
                "name": "concept_lookup",
                "arguments": {"term": "artificial intelligence"}
            }
        }
    )
    result = response.json()
    print(result["result"])

For detailed deployment instructions, configuration options, and troubleshooting, see the Cloudflare Workers Documentation.

Available Tools

1. Concept Lookup

Get detailed information about a specific concept. Returns all relationships and properties.

{
  "name": "concept_lookup",
  "arguments": {
    "term": "artificial intelligence",
    "language": "en",
    "limit_results": false,
    "target_language": null,
    "verbose": false
  }
}

Parameters:

term (required): The concept to look up
language (default: "en"): Language code for the concept
limit_results (default: false): Limit to first 20 results for quick queries
target_language (optional): Filter results to specific target language
verbose (default: false): Return detailed format vs minimal format

2. Concept Query

Advanced querying with sophisticated multi-parameter filtering.

{
  "name": "concept_query",
  "arguments": {
    "start": "car",
    "rel": "IsA",
    "language": "en",
    "limit_results": false,
    "verbose": false
  }
}

Parameters:

start (optional): Start concept of relationships
end (optional): End concept of relationships
rel (optional): Relation type (e.g., "IsA", "PartOf")
node (optional): Concept that must be start or end of edges
other (optional): Used with 'node' parameter
sources (optional): Filter by data source
language (default: "en"): Language filter
limit_results (default: false): Limit to 20 results for quick queries
verbose (default: false): Return detailed format vs minimal format

Find concepts semantically similar to a given concept using ConceptNet's embeddings.

{
  "name": "related_concepts",
  "arguments": {
    "term": "machine learning",
    "language": "en",
    "filter_language": null,
    "limit": 100,
    "verbose": false
  }
}

Parameters:

term (required): The concept to find related concepts for
language (default: "en"): Language code for input term
filter_language (optional): Filter results to this language only
limit (default: 100, max: 100): Maximum number of related concepts
verbose (default: false): Return detailed format vs minimal format

4. Concept Relatedness

Calculate precise semantic relatedness score between two concepts.

{
  "name": "concept_relatedness",
  "arguments": {
    "concept1": "artificial intelligence",
    "concept2": "machine learning",
    "language1": "en",
    "language2": "en",
    "verbose": false
  }
}

Parameters:

concept1 (required): First concept for comparison
concept2 (required): Second concept for comparison
language1 (default: "en"): Language for first concept
language2 (default: "en"): Language for second concept
verbose (default: false): Return detailed format vs minimal format

Configuration

The server can be configured through environment variables:

# ConceptNet API settings
CONCEPTNET_API_BASE_URL=https://api.conceptnet.io
CONCEPTNET_API_VERSION=5.7

# Server settings
MCP_SERVER_HOST=localhost
MCP_SERVER_PORT=3000
LOG_LEVEL=INFO

# Rate limiting
CONCEPTNET_RATE_LIMIT=100
CONCEPTNET_RATE_PERIOD=60

Development

Setup

# Clone the repository
git clone https://github.com/infinitnet/conceptnet-mcp.git
cd conceptnet-mcp

# Install in development mode
pip install -e .[dev]

# Install pre-commit hooks
pre-commit install

API Reference

Core Models

Concept: Represents a ConceptNet concept with URI, label, and language
Edge: Represents relationships between concepts with relation types
Query: Structured query parameters for concept searches
Response: Standardized response format with pagination support

Client Components

ConceptNetClient: Async HTTP client for ConceptNet API
PaginationHandler: Automatic pagination for large result sets
ResponseProcessor: Data processing and normalization

Utilities

Text Processing: Normalize text (underscores to spaces)
Logging: Structured logging with configurable levels
Error Handling: Comprehensive exception hierarchy

Architecture

conceptnet_mcp/
├── client/           # ConceptNet API client
│   ├── conceptnet_client.py
│   ├── pagination.py
│   └── processor.py
├── models/           # Pydantic data models
│   ├── concept.py
│   ├── edge.py
│   ├── query.py
│   └── response.py
├── tools/            # MCP tool implementations
│   ├── concept_lookup.py
│   ├── concept_query.py
│   ├── related_concepts.py
│   └── concept_relatedness.py
├── utils/            # Utility modules
│   ├── exceptions.py
│   ├── logging.py
│   └── text_utils.py
└── server.py         # FastMCP server entry point

Contributing

Fork the repository: https://github.com/infinitnet/conceptnet-mcp
Create a feature branch: git checkout -b feature-name
Make your changes and add tests
Run the test suite: python run_tests.py
Submit a pull request

Guidelines

Follow PEP 8 style guidelines
Add type hints for all functions
Include docstrings for public APIs
Write tests for new functionality
Update documentation as needed

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Acknowledgments

ConceptNet for providing the semantic knowledge base
FastMCP for the MCP framework
Model Context Protocol specification

Support

📖 Documentation: Read the docs
🐛 Bug Reports: GitHub Issues
💬 Discussions: GitHub Discussions
🌐 Author's Website: https://infinitnet.io/

Built with ❤️ for the AI and semantic web community.

Deploy Server

HTTP connection URL

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Provides seamless access to the ConceptNet semantic knowledge graph, enabling concept lookup, search, relationship discovery, and semantic similarity calculations across multiple languages.

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