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ConceptNet MCP Server

by infinitnet
usage.md7.68 kB
# Usage Examples This guide provides practical examples of using the ConceptNet MCP Server tools. ## Overview The ConceptNet MCP Server provides four main tools for semantic analysis: 1. **concept_lookup** - Get detailed information about a specific concept 2. **concept_query** - Search and filter concepts with advanced criteria 3. **related_concepts** - Find concepts connected through semantic relationships 4. **concept_relatedness** - Calculate semantic similarity between concepts ## Tool Examples ### 1. Concept Lookup Get detailed information about a specific concept. **Basic Example:** ```json { "name": "concept_lookup", "arguments": { "concept": "dog", "language": "en" } } ``` **Response:** ```json { "concept": { "uri": "/c/en/dog", "label": "dog", "language": "en" }, "edges": [ { "relation": "IsA", "start": "/c/en/dog", "end": "/c/en/animal", "weight": 8.5 }, { "relation": "HasProperty", "start": "/c/en/dog", "end": "/c/en/loyal", "weight": 6.2 } ], "total_edges": 150, "languages": ["en", "es", "fr", "de"] } ``` **Multilingual Example:** ```json { "name": "concept_lookup", "arguments": { "concept": "perro", "language": "es" } } ``` ### 2. Concept Query Search for concepts with advanced filtering options. **Basic Search:** ```json { "name": "concept_query", "arguments": { "query": "animal", "language": "en", "limit": 10 } } ``` **Response:** ```json { "concepts": [ { "uri": "/c/en/dog", "label": "dog", "language": "en" }, { "uri": "/c/en/cat", "label": "cat", "language": "en" }, { "uri": "/c/en/bird", "label": "bird", "language": "en" } ], "total": 1500, "offset": 0, "limit": 10, "has_more": true } ``` **Advanced Search with Pagination:** ```json { "name": "concept_query", "arguments": { "query": "machine learning", "language": "en", "limit": 20, "offset": 40 } } ``` **Multilingual Search:** ```json { "name": "concept_query", "arguments": { "query": "tecnología", "language": "es", "limit": 15 } } ``` ### 3. Related Concepts Find concepts connected through semantic relationships. **Basic Example:** ```json { "name": "related_concepts", "arguments": { "concept": "dog", "language": "en", "limit": 5 } } ``` **Response:** ```json { "source_concept": { "uri": "/c/en/dog", "label": "dog", "language": "en" }, "related_concepts": [ { "concept": { "uri": "/c/en/cat", "label": "cat", "language": "en" }, "relation": "SimilarTo", "weight": 7.8 }, { "concept": { "uri": "/c/en/animal", "label": "animal", "language": "en" }, "relation": "IsA", "weight": 8.5 }, { "concept": { "uri": "/c/en/pet", "label": "pet", "language": "en" }, "relation": "IsA", "weight": 7.2 } ], "total": 150 } ``` **Technology Domain Example:** ```json { "name": "related_concepts", "arguments": { "concept": "artificial intelligence", "language": "en", "limit": 8 } } ``` ### 4. Concept Relatedness Calculate semantic similarity between two concepts. **Basic Example:** ```json { "name": "concept_relatedness", "arguments": { "concept1": "dog", "concept2": "cat", "language": "en" } } ``` **Response:** ```json { "concept1": { "uri": "/c/en/dog", "label": "dog", "language": "en" }, "concept2": { "uri": "/c/en/cat", "label": "cat", "language": "en" }, "relatedness_score": 7.8, "explanation": "Both are domestic animals and pets", "shared_relations": [ "IsA animal", "IsA pet", "HasProperty domestic" ] } ``` **Comparing Abstract Concepts:** ```json { "name": "concept_relatedness", "arguments": { "concept1": "love", "concept2": "happiness", "language": "en" } } ``` **Cross-Domain Comparison:** ```json { "name": "concept_relatedness", "arguments": { "concept1": "computer", "concept2": "brain", "language": "en" } } ``` ## Common Use Cases ### 1. Semantic Search Enhancement Use concept query and related concepts to enhance search results: ```json // First, search for concepts { "name": "concept_query", "arguments": { "query": "renewable energy", "language": "en", "limit": 5 } } // Then find related concepts for each result { "name": "related_concepts", "arguments": { "concept": "solar power", "language": "en", "limit": 10 } } ``` ### 2. Content Recommendation Calculate relatedness to recommend similar content: ```json { "name": "concept_relatedness", "arguments": { "concept1": "machine learning", "concept2": "data science", "language": "en" } } ``` ### 3. Knowledge Graph Exploration Start with a concept and explore its neighborhood: ```json // 1. Get detailed information { "name": "concept_lookup", "arguments": { "concept": "neural network", "language": "en" } } // 2. Find related concepts { "name": "related_concepts", "arguments": { "concept": "neural network", "language": "en", "limit": 10 } } // 3. Explore relationships { "name": "concept_relatedness", "arguments": { "concept1": "neural network", "concept2": "deep learning", "language": "en" } } ``` ### 4. Multilingual Analysis Work with concepts across languages: ```json // English concept { "name": "concept_lookup", "arguments": { "concept": "artificial intelligence", "language": "en" } } // Spanish equivalent { "name": "concept_lookup", "arguments": { "concept": "inteligencia artificial", "language": "es" } } // Compare across languages { "name": "concept_relatedness", "arguments": { "concept1": "machine learning", "concept2": "aprendizaje automático", "language": "en" } } ``` ## Error Handling The server provides comprehensive error responses: **Concept Not Found:** ```json { "error": "ConceptNotFound", "message": "Concept 'invalidconcept' not found in language 'en'", "concept": "invalidconcept", "language": "en" } ``` **Rate Limiting:** ```json { "error": "RateLimitExceeded", "message": "Rate limit exceeded. Please wait before making more requests.", "retry_after": 60 } ``` **Invalid Parameters:** ```json { "error": "ValidationError", "message": "Invalid limit value. Must be between 1 and 100.", "field": "limit", "value": 150 } ``` ## Best Practices ### 1. Efficient Pagination Use pagination for large result sets: ```json { "name": "concept_query", "arguments": { "query": "science", "language": "en", "limit": 50, "offset": 0 } } ``` ### 2. Language Consistency Keep language consistent across related queries: ```json // All queries use the same language { "name": "concept_lookup", "arguments": { "concept": "technology", "language": "en" } } { "name": "related_concepts", "arguments": { "concept": "technology", "language": "en", "limit": 10 } } ``` ### 3. Rate Limit Awareness Implement appropriate delays between requests to respect rate limits. ### 4. Error Recovery Implement retry logic with exponential backoff for transient errors. ## Next Steps - Explore the [API Reference](api.md) for detailed parameter information - Check individual [Tool Documentation](tools/) for advanced usage - Review the [Installation Guide](installation.md) for configuration options

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