ConceptNet MCP Server

Look up information about a specific concept in ConceptNet. This tool queries ConceptNet's knowledge graph to find all relationships and properties associated with a given concept. By default, it returns ALL results (not limited to 20) to provide complete information. Features: - Complete relationship discovery for any concept - Language filtering and cross-language exploration - Summaries and statistics - Performance optimized with automatic pagination - Format control: minimal (~96% smaller) vs verbose (full metadata) Format Options: - verbose=false (default): Returns minimal format optimized for LLM consumption - verbose=true: Returns comprehensive format with full ConceptNet metadata - Backward compatibility maintained with existing tools Use this when you need to: - Understand what ConceptNet knows about a concept - Explore all relationships for a term - Get semantic information - Find related concepts and properties

Advanced querying of ConceptNet with sophisticated multi-parameter filtering. This tool provides powerful filtering capabilities for exploring ConceptNet's knowledge graph. You can combine multiple filters to find specific types of relationships and concepts with precision. Features: - Multi-parameter filtering (start, end, relation, node, sources) - Complex relationship discovery and analysis - Comprehensive result processing and enhancement - Query optimization and performance metrics - Format control: minimal (~96% smaller) vs verbose (full metadata) Format Options: - verbose=false (default): Returns minimal format optimized for LLM consumption - verbose=true: Returns comprehensive format with full ConceptNet metadata - Backward compatibility maintained with existing tools Filter Parameters: - start: Start concept of relationships (e.g., "dog", "/c/en/dog") - end: End concept of relationships (e.g., "animal", "/c/en/animal") - rel: Relation type (e.g., "IsA", "/r/IsA") - node: Concept that must be either start or end of edges - other: Used with 'node' to find relationships between two specific concepts - sources: Filter by data source (e.g., "wordnet", "/s/activity/omcs") Use this when you need: - Precise relationship filtering and discovery - Complex queries with multiple constraints - Analysis of specific relationship types - Targeted exploration of concept connections

Find concepts semantically related to a given concept using ConceptNet's embeddings. This tool uses ConceptNet's semantic similarity algorithms to discover concepts that are related to the input term. Results are ranked by similarity score and include comprehensive analysis. Features: - Semantic similarity discovery using advanced algorithms - Ranked results with detailed similarity analysis - Default English language filtering (can be disabled or changed) - Statistical analysis and categorization - Format control: minimal (~96% smaller) vs verbose (full metadata) Format Options: - verbose=false (default): Returns minimal format optimized for LLM consumption - verbose=true: Returns comprehensive format with full ConceptNet metadata - Backward compatibility maintained with existing tools Similarity Analysis: - Similarity scores from 0.0 (unrelated) to 1.0 (very similar) - Descriptive categories (very strong, strong, moderate, weak, very weak) - Relationship context and likely connections - Language distribution and statistical summaries Use this when you need to: - Discover semantically similar concepts - Expand concept exploration and brainstorming - Find related terms and ideas - Understand semantic neighborhoods

Calculate precise semantic relatedness score between two concepts. This tool uses ConceptNet's semantic embeddings to calculate how related two concepts are to each other. The score ranges from 0.0 (completely unrelated) to 1.0 (very strongly related). Features: - Precise quantitative similarity measurement - Cross-language comparison support - Detailed relationship analysis and interpretation - Confidence levels and percentile estimates - Format control: minimal (~96% smaller) vs verbose (full metadata) Format Options: - verbose=false (default): Returns minimal format optimized for LLM consumption - verbose=true: Returns comprehensive format with full ConceptNet metadata - Backward compatibility maintained with existing tools Analysis Components: - Numeric relatedness score (0.0-1.0) - Descriptive interpretation and confidence level - Likely connection explanations - Semantic distance and relationship strength - Cross-language analysis when applicable Use this when you need to: - Quantify how similar two concepts are - Compare concepts across different languages - Measure semantic distance between ideas - Validate conceptual relationships