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Uses MCP's standardized resource and tool interfaces to abstract database operations, allowing clients to perform CRUD operations on vector embeddings without direct database connections.","intents":["Store embedding vectors from LLM outputs and retrieve semantically similar vectors for RAG workflows","Query multi-geometry spatial data (e.g., geographic coordinates, polygon regions) alongside vector similarity","Integrate vector database operations into agentic workflows without managing database connections directly","Build knowledge bases that combine semantic search with spatial/geometric constraints"],"best_for":["AI agents and LLM applications requiring persistent vector storage","Teams building RAG systems that need both semantic and spatial query capabilities","Developers integrating vector databases into MCP-compatible AI frameworks"],"limitations":["Performance characteristics and scaling limits of HyperspaceDB not documented — unclear maximum vector dimensions, index types, or throughput","MCP protocol adds serialization/deserialization overhead for each vector operation","No built-in authentication or multi-tenancy — assumes single-user or trusted network deployment","Vector dimensionality and geometry type support depends on underlying HyperspaceDB implementation details"],"requires":["Node.js runtime (npm package)","MCP-compatible client (Claude, custom LLM framework, or MCP inspector)","HyperspaceDB instance or compatible vector database backend"],"input_types":["vector embeddings (float arrays)","geometry objects (points, polygons, or other spatial types)","query parameters (similarity thresholds, spatial bounds)"],"output_types":["vector records with metadata","similarity scores","geometry matches","structured query results"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_1","uri":"capability://tool.use.integration.schema.based.vector.operation.tool.calling.via.mcp","name":"schema-based vector operation tool calling via mcp","description":"Implements MCP's tool definition interface to expose HyperspaceDB operations (insert, query, delete, update) as callable tools with JSON schema validation. Each tool defines input parameters (vector data, geometry, query filters) and output schemas, allowing LLM agents to invoke database operations with type-safe argument passing and automatic schema validation before execution.","intents":["Enable LLM agents to autonomously decide when to store or retrieve vectors based on task context","Provide type-safe vector database operations with automatic parameter validation","Allow agents to compose multi-step workflows combining vector operations with other MCP tools"],"best_for":["Agentic AI systems that need to make autonomous decisions about vector storage/retrieval","Teams building complex RAG pipelines with multiple tool dependencies","Developers who want schema validation without custom middleware"],"limitations":["Schema complexity limited by MCP's JSON schema expressiveness — may not capture all HyperspaceDB query options","Tool calling adds latency per operation (LLM must generate tool invocation, MCP must parse and validate)","No streaming support for large result sets — entire response must fit in context window"],"requires":["MCP-compatible LLM client with tool-calling support (Claude 3+, GPT-4 with function calling, etc.)","Node.js 14+","Understanding of JSON schema for custom tool definition extension"],"input_types":["tool invocation JSON with vector arrays and geometry objects","query parameters as structured JSON"],"output_types":["tool result JSON with vector records and metadata","error responses with validation details"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_2","uri":"capability://search.retrieval.semantic.search.with.spatial.filtering","name":"semantic search with spatial filtering","description":"Combines vector similarity search with geometric constraint filtering, allowing queries to find semantically similar vectors within specified spatial boundaries (e.g., embeddings near a geographic region or within a polygon). Implements this by executing vector similarity queries and applying geometry-based post-filtering or by leveraging HyperspaceDB's native multi-geometry indexing if available.","intents":["Find semantically similar documents or embeddings within a geographic region (e.g., local news articles similar to a query)","Query knowledge bases that combine semantic relevance with spatial constraints (e.g., nearby restaurants matching a cuisine embedding)","Build location-aware RAG systems that respect both semantic and geographic boundaries"],"best_for":["Location-aware AI applications (local search, geo-targeted recommendations)","Knowledge bases combining semantic and spatial data (maps, geographic databases)","Multi-modal RAG systems with spatial constraints"],"limitations":["Performance of spatial filtering depends on HyperspaceDB's index structure — no documentation on spatial index types or query optimization","Combining similarity scoring with spatial distance may require custom ranking logic not exposed by MCP interface","Geometry type support (points, polygons, lines) not explicitly documented"],"requires":["HyperspaceDB with spatial indexing support","Vector embeddings and corresponding geometry objects in database","MCP client capable of passing complex query parameters"],"input_types":["query vector (embedding)","geometry filter (point, polygon, bounding box)","similarity threshold"],"output_types":["ranked vector results with similarity scores","geometry metadata for each result"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_3","uri":"capability://memory.knowledge.persistent.vector.embedding.storage.with.metadata","name":"persistent vector embedding storage with metadata","description":"Provides durable storage for vector embeddings alongside structured metadata (tags, timestamps, source references, geometry data) using HyperspaceDB as the backing store. Implements persistence through MCP's resource interface, allowing clients to store embeddings once and retrieve them across multiple agent sessions without re-computing embeddings from source documents.","intents":["Cache computed embeddings to avoid re-embedding documents on every query","Build persistent knowledge bases that survive agent restarts","Track embedding provenance with metadata (source document, embedding model, creation time)"],"best_for":["Production RAG systems requiring embedding caching and persistence","Long-running agents that need to maintain knowledge across sessions","Teams building knowledge bases with audit trails and versioning"],"limitations":["No built-in versioning or time-travel queries — updating embeddings overwrites history","Metadata schema flexibility not documented — unclear if custom metadata fields are supported","No transaction support documented — concurrent writes may have consistency issues","Storage capacity and retention policies depend on HyperspaceDB configuration"],"requires":["Persistent HyperspaceDB instance (not in-memory)","Sufficient disk space for vector storage (depends on dimensionality and count)","MCP client with resource persistence support"],"input_types":["vector embeddings (float arrays)","metadata objects (JSON)","geometry data"],"output_types":["stored vector records with IDs","retrieval results with full metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_4","uri":"capability://data.processing.analysis.batch.vector.insertion.and.bulk.operations","name":"batch vector insertion and bulk operations","description":"Supports efficient bulk insertion of multiple vectors and metadata records in a single MCP call, reducing round-trip overhead compared to individual insert operations. Likely implements batching at the MCP protocol level or delegates to HyperspaceDB's native batch APIs, enabling agents to ingest large embedding collections (e.g., from document chunking pipelines) with minimal latency.","intents":["Ingest large document collections into vector database after batch embedding generation","Efficiently populate knowledge bases during initialization or periodic updates","Reduce network overhead when storing hundreds or thousands of embeddings"],"best_for":["RAG systems with batch document processing pipelines","Knowledge base initialization and bulk updates","High-throughput embedding ingestion workflows"],"limitations":["Batch size limits not documented — unclear if there are constraints on vectors per batch","No partial failure handling documented — unclear if failed records are rolled back or skipped","Batch operation atomicity not specified — may not guarantee all-or-nothing semantics","Memory overhead of batching depends on vector dimensionality and batch size"],"requires":["Multiple vectors ready for insertion (typically from embedding model output)","MCP client supporting batch operation syntax"],"input_types":["array of vector embeddings","array of metadata objects","array of geometry objects"],"output_types":["insertion results with assigned IDs","error details for failed records (if applicable)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_5","uri":"capability://search.retrieval.vector.similarity.ranking.and.scoring","name":"vector similarity ranking and scoring","description":"Computes and returns similarity scores (cosine, Euclidean, or other distance metrics) for query vectors against stored vectors, enabling agents to rank results by relevance. Implements this through HyperspaceDB's native similarity computation, returning scored results that can be used for relevance-based filtering or ranking in downstream processing.","intents":["Rank search results by semantic relevance to user query","Filter results based on similarity thresholds (e.g., only return embeddings with >0.8 cosine similarity)","Implement confidence scoring for RAG systems to assess answer quality"],"best_for":["RAG systems requiring relevance ranking","Semantic search applications with confidence thresholds","Quality assessment and filtering in AI pipelines"],"limitations":["Similarity metric options not documented — unclear which distance functions are supported","Score normalization not specified — unclear if scores are in [0,1] range or unbounded","No custom similarity function support documented","Scoring performance depends on vector dimensionality and index structure"],"requires":["Query vector and stored vectors in same dimensionality","HyperspaceDB configured with similarity indexing"],"input_types":["query vector (embedding)","optional similarity threshold"],"output_types":["ranked vector results with similarity scores","metadata for each result"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_6","uri":"capability://data.processing.analysis.vector.deletion.and.lifecycle.management","name":"vector deletion and lifecycle management","description":"Provides operations to delete vectors by ID or metadata criteria, enabling agents to manage knowledge base lifecycle (remove outdated embeddings, purge sensitive data, implement retention policies). Implements deletion through HyperspaceDB's delete APIs, potentially supporting soft deletes or immediate hard deletes depending on configuration.","intents":["Remove outdated or incorrect embeddings from knowledge base","Implement data retention policies (e.g., delete embeddings older than N days)","Purge sensitive information from vector storage for privacy compliance"],"best_for":["Long-lived RAG systems requiring knowledge base maintenance","Privacy-sensitive applications needing data deletion capabilities","Systems with evolving knowledge bases and document updates"],"limitations":["Deletion semantics not documented — unclear if deletes are immediate or eventually consistent","No cascading delete or referential integrity — deleting vectors doesn't update related metadata","Bulk deletion performance not specified — may be slow for large-scale purges","No audit trail for deletions — cannot track what was removed or when"],"requires":["Vector ID or metadata criteria for deletion","Appropriate permissions to delete from database"],"input_types":["vector ID or metadata filter criteria"],"output_types":["deletion confirmation with count of deleted records","error details if deletion fails"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-hyperspacedb__cap_7","uri":"capability://search.retrieval.metadata.based.vector.filtering.and.querying","name":"metadata-based vector filtering and querying","description":"Enables filtering vectors by structured metadata fields (tags, timestamps, source references, custom attributes) before or alongside similarity search, allowing agents to narrow result sets by non-semantic criteria. Implements filtering through HyperspaceDB's metadata indexing, potentially using secondary indexes for efficient metadata-based lookups.","intents":["Filter embeddings by source document, creation date, or custom tags","Implement access control by filtering vectors based on user/role metadata","Combine semantic search with metadata constraints (e.g., find similar embeddings from documents published in last 30 days)"],"best_for":["Multi-tenant RAG systems requiring access control via metadata","Knowledge bases with rich metadata (source, date, category, author)","Systems combining semantic and metadata-based filtering"],"limitations":["Metadata schema flexibility not documented — unclear if arbitrary JSON fields are indexed","Filter query language not specified — unclear if complex boolean queries are supported","Metadata indexing overhead not documented — may impact insertion performance","No full-text search on metadata fields — only exact/range matching likely"],"requires":["Vectors stored with metadata fields","Metadata fields indexed in HyperspaceDB"],"input_types":["metadata filter criteria (key-value pairs, ranges)","optional query vector for combined semantic+metadata search"],"output_types":["filtered vector results with metadata","similarity scores if semantic search combined with filtering"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js runtime (npm package)","MCP-compatible client (Claude, custom LLM framework, or MCP inspector)","HyperspaceDB instance or compatible vector database backend","MCP-compatible LLM client with tool-calling support (Claude 3+, GPT-4 with function calling, etc.)","Node.js 14+","Understanding of JSON schema for custom tool definition extension","HyperspaceDB with spatial indexing support","Vector embeddings and corresponding geometry objects in database","MCP client capable of passing complex query parameters","Persistent HyperspaceDB instance (not in-memory)"],"failure_modes":["Performance characteristics and scaling limits of HyperspaceDB not documented — unclear maximum vector dimensions, index types, or throughput","MCP protocol adds serialization/deserialization overhead for each vector operation","No built-in authentication or multi-tenancy — assumes single-user or trusted network deployment","Vector dimensionality and geometry type support depends on underlying HyperspaceDB implementation details","Schema complexity limited by MCP's JSON schema expressiveness — may not capture all HyperspaceDB query options","Tool calling adds latency per operation (LLM must generate tool invocation, MCP must parse and validate)","No streaming support for large result sets — entire response must fit in context window","Performance of spatial filtering depends on HyperspaceDB's index structure — no documentation on spatial index types or query optimization","Combining similarity scoring with spatial distance may require custom ranking logic not exposed by MCP interface","Geometry type support (points, polygons, lines) not explicitly documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.07958800173440753,"quality":0.26,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":125,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-hyperspacedb","compare_url":"https://unfragile.ai/compare?artifact=mcp-hyperspacedb"}},"signature":"NREDeQbgP6CmNeMWo6FlonludSFuUZWxHk9bCF1MVu60vrlLDg/ey3QRlpLdOwvMO04vHaUgOVdVEMhfczNoCw==","signedAt":"2026-06-19T19:58:57.265Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-hyperspacedb","artifact":"https://unfragile.ai/mcp-hyperspacedb","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-hyperspacedb","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}