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ConceptNet MCP Server

by infinitnet
OverviewInspectNewSchemaRelated ServersScore
Python
Hybrid
GPL 3.0

ConceptNet MCP Server

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

CI Release Python 3.10+ License: GPL-3.0

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.

Deploy to Cloudflare 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

3. Related Concepts

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

  1. Fork the repository: https://github.com/infinitnet/conceptnet-mcp

  2. Create a feature branch: git checkout -b feature-name

  3. Make your changes and add tests

  4. Run the test suite: python run_tests.py

  5. 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

Support

Built with ❤️ for the AI and semantic web community.

Deploy Server
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security – no known vulnerabilities
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license - permissive license
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quality - confirmed to work

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