hide-mcp
MCP ServerFreeStore and recall user-specific facts across conversations with a structured knowledge graph. Add, relate, and search information about people, organizations, events, and preferences to maintain consistent context. Automatically extract locations and build place hierarchies for richer, more accurate
Capabilities5 decomposed
structured knowledge graph storage
Medium confidenceThis capability allows users to store and manage user-specific facts in a structured knowledge graph format. It utilizes graph database principles to create nodes for entities like people, organizations, and events, and edges to represent relationships between them, enabling complex queries and efficient retrieval of related information. This architecture supports dynamic updates and context-aware recall across conversations, distinguishing it from traditional flat data storage methods.
Employs a graph-based approach for context storage, allowing for dynamic relationships and efficient querying, unlike traditional relational databases.
More flexible in managing complex relationships than standard key-value stores, enabling richer context recall.
contextual information recall
Medium confidenceThis capability enables the system to automatically recall relevant user-specific facts during conversations by leveraging the structured knowledge graph. It employs algorithms to prioritize and retrieve the most pertinent information based on the current conversation context, ensuring that responses are personalized and contextually relevant. This is achieved through a combination of semantic search techniques and graph traversal methods.
Utilizes advanced graph traversal algorithms to retrieve contextually relevant information quickly, enhancing user interaction quality.
More efficient in maintaining conversational context than linear search methods, reducing response time.
automated location extraction
Medium confidenceThis capability automatically identifies and extracts location data from user interactions, building a hierarchical structure of places. It employs natural language processing (NLP) techniques to parse text for location mentions and uses a predefined taxonomy to categorize these locations, which enhances the knowledge graph's richness and accuracy. The hierarchical structure allows for better contextual understanding of user references to places.
Combines NLP with a structured approach to build place hierarchies, allowing for richer context than simple keyword extraction.
More robust in handling complex location references than basic regex-based extraction methods.
relationship mapping between entities
Medium confidenceThis capability allows users to define and manage relationships between different entities within the knowledge graph. It uses a flexible schema that supports various relationship types, enabling users to create complex networks of information that can be queried and analyzed. This feature is particularly useful for applications that require understanding how different entities are interconnected.
Supports dynamic relationship definitions that can evolve over time, unlike static relationship models in traditional databases.
More adaptable to changes in entity relationships than rigid relational database schemas.
semantic search within knowledge graph
Medium confidenceThis capability enables users to perform semantic searches within the knowledge graph, allowing for more intuitive querying of user-specific facts. It employs vector embeddings and similarity search techniques to match user queries with relevant nodes in the graph, enhancing the accuracy and relevance of search results. This approach allows for natural language queries to yield meaningful results based on context rather than exact matches.
Integrates semantic search capabilities directly into the knowledge graph, allowing for context-aware retrieval that traditional keyword searches lack.
More effective in understanding user intent than traditional keyword-based search systems.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with hide-mcp, ranked by overlap. Discovered automatically through the match graph.
memory-graph
MCP server: memory-graph
Jean Memory
** - Premium memory consistent across all AI applications.
mem0
Universal memory layer for AI Agents
llama-index
Interface between LLMs and your data
Mem0
Persistent memory layer for AI agents.
Memory Graph
Remember user details and preferences across conversations. Organize facts into connected profiles for richer, long-term context. Search, update, and automatically extract locations to keep memories accurate and actionable.
Best For
- ✓developers building conversational agents that require persistent context
- ✓developers creating chatbots that need to remember user interactions
- ✓developers building applications that require geographical context
- ✓developers needing to model complex relationships in their applications
- ✓developers looking to improve search functionalities in conversational applications
Known Limitations
- ⚠Requires careful schema design to avoid performance bottlenecks in complex queries
- ⚠Graph database knowledge is necessary for optimal usage
- ⚠Performance may degrade with a very large number of nodes and edges if not optimized
- ⚠Requires tuning of retrieval algorithms for best results
- ⚠Accuracy depends on the quality of NLP models used for location extraction
- ⚠May struggle with ambiguous location references without additional context
Requirements
Input / Output
UnfragileRank
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About
Store and recall user-specific facts across conversations with a structured knowledge graph. Add, relate, and search information about people, organizations, events, and preferences to maintain consistent context. Automatically extract locations and build place hierarchies for richer, more accurate recall.
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