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The MCP protocol layer translates client requests into Qdrant API calls, handling vector embedding lookup, distance metric computation (cosine, Euclidean, dot product), and result ranking without requiring clients to manage vector databases directly.","intents":["I want Claude to search my knowledge base semantically without writing SQL or managing embeddings myself","I need to build an agent that can retrieve contextually relevant documents from a vector store in real-time","I want to implement RAG (retrieval-augmented generation) where Claude queries a Qdrant collection and uses results to ground responses"],"best_for":["AI engineers building Claude-powered agents with semantic memory","Teams implementing RAG pipelines with minimal infrastructure overhead","Developers wanting vector search without learning Qdrant's native API"],"limitations":["MCP protocol adds ~50-100ms latency per query due to serialization/deserialization overhead","No built-in query optimization — complex multi-filter searches may require manual query composition","Vector embeddings must be pre-computed; the MCP server does not generate embeddings itself"],"requires":["Qdrant server instance (v1.0+) running and accessible","Claude or compatible MCP client (Claude Desktop, custom MCP host)","Pre-embedded vectors in target Qdrant collection","Network connectivity between MCP client and Qdrant server"],"input_types":["natural language query strings","vector arrays (float32)","collection names (string identifiers)"],"output_types":["ranked result sets with scores","structured JSON with metadata","point IDs and payloads"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_1","uri":"capability://data.processing.analysis.collection.aware.point.insertion.and.upsert.with.metadata.preservation","name":"collection-aware point insertion and upsert with metadata preservation","description":"Allows MCP clients to insert or update vector points into Qdrant collections while preserving structured metadata payloads. The capability handles batch operations, conflict resolution (upsert semantics), and automatic ID management, translating MCP write requests into Qdrant's point insertion API with full support for custom metadata fields and conditional updates.","intents":["I want to add new documents to my vector store with associated metadata (source, timestamp, author) without managing IDs manually","I need to update existing vectors when documents change, ensuring metadata stays in sync","I want to batch-insert thousands of embeddings with their original context in a single operation"],"best_for":["Data engineers building ETL pipelines that feed Qdrant from external sources","LLM application developers who need to dynamically add training data or documents to memory","Teams implementing document indexing workflows where metadata is critical for retrieval context"],"limitations":["Batch insert performance degrades with payloads >1MB per point; Qdrant recommends keeping metadata lean","No built-in deduplication — duplicate detection must be handled by client logic","Upsert operations are not atomic across multiple collections; cross-collection consistency requires external coordination"],"requires":["Qdrant collection pre-created with matching vector dimension","Vector embeddings pre-computed (float32 arrays)","MCP client capable of sending structured write requests","Write permissions on target Qdrant collection"],"input_types":["vector arrays (float32, matching collection dimension)","point IDs (integer or UUID)","JSON metadata objects","batch operation lists"],"output_types":["operation status (success/failure)","inserted point IDs","acknowledgment with timestamp"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_2","uri":"capability://search.retrieval.filtered.vector.search.with.payload.based.constraints","name":"filtered vector search with payload-based constraints","description":"Enables semantic search queries filtered by structured metadata conditions (e.g., 'find similar documents where source=arxiv AND year>2020'). The MCP server translates filter expressions into Qdrant's filter DSL, combining vector similarity scoring with boolean/range/geo constraints on point payloads, returning only results matching both semantic and metadata criteria.","intents":["I want to search my knowledge base semantically but only within documents from a specific source or time range","I need to retrieve similar items while excluding certain categories or authors","I want to implement faceted search where Claude can narrow results by metadata dimensions"],"best_for":["Information retrieval specialists building multi-dimensional search experiences","RAG systems requiring temporal or categorical filtering alongside semantic matching","Teams with large, heterogeneous document collections needing fine-grained access control"],"limitations":["Complex nested filters (deeply nested AND/OR logic) can degrade query performance; Qdrant recommends flattening where possible","Filter evaluation happens server-side after vector search, not before — cannot use filters to reduce search space before similarity computation","Geo-spatial filters require pre-computed geographic metadata; no built-in geocoding"],"requires":["Qdrant collection with indexed payload fields (optional but recommended for performance)","Metadata stored in point payloads with consistent schema","MCP client capable of expressing filter conditions","Understanding of Qdrant's filter DSL syntax"],"input_types":["vector query (float32 array)","filter expressions (JSON-serialized Qdrant filter DSL)","limit/offset parameters"],"output_types":["ranked results with similarity scores","filtered point payloads","result count"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_3","uri":"capability://memory.knowledge.collection.schema.introspection.and.metadata.discovery","name":"collection schema introspection and metadata discovery","description":"Exposes Qdrant collection metadata (vector dimension, distance metric, indexed fields, point count) through MCP, allowing clients to discover available collections and their structure without direct API access. The MCP server queries Qdrant's collection info endpoints and surfaces schema details, enabling dynamic client behavior based on collection capabilities.","intents":["I want Claude to automatically discover what collections are available and their dimensions before attempting queries","I need to validate that a collection supports the queries I'm about to run (e.g., checking if a field is indexed)","I want to build a generic RAG agent that adapts to different collection schemas at runtime"],"best_for":["Multi-collection RAG systems where schema varies across collections","Developers building generic MCP clients that need to adapt to different Qdrant deployments","Teams implementing dynamic agent routing based on available memory structures"],"limitations":["Schema introspection is read-only; cannot modify collection configuration through MCP","No real-time schema change notifications — clients must poll for updates","Does not expose internal Qdrant statistics (e.g., index size, segment count) useful for optimization"],"requires":["Qdrant server with accessible collection info endpoints","Read permissions on target collections","MCP client capable of handling schema metadata responses"],"input_types":["collection name (string)","optional filter for specific metadata fields"],"output_types":["collection metadata (JSON)","vector dimension (integer)","distance metric (string: cosine, euclidean, dot)","indexed field names (array)","point count (integer)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_4","uri":"capability://data.processing.analysis.point.deletion.and.collection.cleanup.with.conditional.removal","name":"point deletion and collection cleanup with conditional removal","description":"Allows MCP clients to delete specific points from collections by ID or filter condition (e.g., 'delete all points where timestamp < 2020'). The capability supports both targeted deletion and bulk cleanup operations, translating MCP delete requests into Qdrant's point deletion API with support for conditional removal based on payload metadata.","intents":["I want to remove outdated or irrelevant vectors from my knowledge base to keep it fresh","I need to delete all points matching a condition (e.g., from a deprecated source) without manual enumeration","I want to implement data retention policies where old documents are automatically purged"],"best_for":["Data governance teams implementing retention policies on vector stores","RAG systems requiring dynamic memory cleanup and refresh","Developers building self-healing agents that remove stale or incorrect information"],"limitations":["Deletion is not reversible; no built-in undo or soft-delete mechanism","Bulk deletion with complex filters can lock the collection temporarily, impacting concurrent reads","No transaction support — if deletion fails mid-operation, partial deletions may remain"],"requires":["Write permissions on target Qdrant collection","Point IDs or filter conditions identifying points to delete","MCP client capable of sending delete requests"],"input_types":["point IDs (integer or UUID array)","filter expressions (JSON-serialized Qdrant filter DSL)","collection name (string)"],"output_types":["deletion status (success/failure)","count of deleted points","operation timestamp"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_5","uri":"capability://search.retrieval.batch.semantic.similarity.scoring.across.multiple.query.vectors","name":"batch semantic similarity scoring across multiple query vectors","description":"Enables clients to submit multiple query vectors in a single MCP request and receive similarity scores against all points in a collection. The server processes batch queries efficiently, computing distances for all query-point pairs and returning ranked results per query, useful for bulk similarity assessment or multi-query retrieval scenarios.","intents":["I want to find similar documents for multiple queries in one operation without sequential round-trips","I need to score a batch of candidate embeddings against my knowledge base to find the best matches","I want to implement multi-query RAG where Claude generates several search queries and retrieves results for all simultaneously"],"best_for":["High-throughput RAG systems processing multiple queries per request","Batch similarity assessment workflows (e.g., finding duplicates across a corpus)","Multi-query retrieval strategies that benefit from parallel evaluation"],"limitations":["Batch size is limited by Qdrant's request payload size (~100MB default); very large batches require chunking","Results are returned as separate ranked lists per query; no cross-query aggregation or fusion built-in","Performance scales linearly with batch size and collection size; very large batches on huge collections may timeout"],"requires":["Multiple query vectors (float32 arrays, matching collection dimension)","Qdrant collection with sufficient capacity","MCP client capable of batching requests"],"input_types":["array of query vectors (float32)","optional filters (applied to all queries)","limit/offset per query"],"output_types":["array of ranked result sets","similarity scores per result","point IDs and payloads"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_6","uri":"capability://data.processing.analysis.vector.dimension.validation.and.type.coercion","name":"vector dimension validation and type coercion","description":"Automatically validates that input vectors match the collection's expected dimension and data type (float32), coercing or rejecting mismatched inputs before sending to Qdrant. The MCP server performs client-side validation to catch dimension mismatches early, preventing failed round-trips and providing clear error messages about incompatibilities.","intents":["I want to catch embedding dimension errors immediately instead of failing at the Qdrant server","I need clear error messages when my embeddings don't match the collection schema","I want to automatically handle minor type conversions (e.g., float64 to float32) without manual preprocessing"],"best_for":["Developers integrating multiple embedding models with different dimensions","Teams building robust RAG pipelines with strict data validation","Debugging workflows where early error detection saves iteration time"],"limitations":["Validation adds ~5-10ms latency per request; not suitable for ultra-low-latency scenarios","Cannot validate semantic correctness of embeddings, only structural properties","Type coercion (e.g., float64 to float32) may lose precision; explicit conversion is safer"],"requires":["Collection dimension metadata (obtained via introspection)","Input vectors in array format"],"input_types":["vector arrays (float32, float64, or numeric types)","collection dimension specification"],"output_types":["validated vectors (float32)","validation error messages (string)","dimension mismatch details"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-qdrant__cap_7","uri":"capability://tool.use.integration.mcp.protocol.request.response.serialization.with.vector.optimization","name":"mcp protocol request/response serialization with vector optimization","description":"Handles efficient serialization of vector data and Qdrant responses through the MCP protocol, optimizing for bandwidth and latency. The server implements custom serialization strategies (e.g., base64 encoding for vectors, selective field inclusion) to minimize payload size while maintaining fidelity, translating between MCP's JSON-based protocol and Qdrant's binary-efficient formats.","intents":["I want to minimize network overhead when transferring large vectors through MCP","I need efficient serialization that doesn't bloat payloads with unnecessary metadata","I want to ensure vectors are transmitted accurately without precision loss"],"best_for":["High-bandwidth-constrained environments (e.g., edge deployments, satellite links)","Applications processing thousands of vectors per second","Teams optimizing MCP server performance for production workloads"],"limitations":["Custom serialization adds complexity; debugging serialization issues requires understanding both MCP and Qdrant protocols","Base64 encoding increases payload size by ~33% compared to binary formats; not optimal for extremely large batches","Selective field inclusion may omit data needed by some clients; requires careful schema design"],"requires":["MCP client compatible with custom serialization formats","Understanding of MCP protocol constraints"],"input_types":["Qdrant API responses (JSON/binary)","MCP protocol messages"],"output_types":["optimized MCP-compatible payloads","serialized vectors (base64 or binary)","metadata (JSON)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Qdrant server instance (v1.0+) running and accessible","Claude or compatible MCP client (Claude Desktop, custom MCP host)","Pre-embedded vectors in target Qdrant collection","Network connectivity between MCP client and Qdrant server","Qdrant collection pre-created with matching vector dimension","Vector embeddings pre-computed (float32 arrays)","MCP client capable of sending structured write requests","Write permissions on target Qdrant collection","Qdrant collection with indexed payload fields (optional but recommended for performance)","Metadata stored in point payloads with consistent schema"],"failure_modes":["MCP protocol adds ~50-100ms latency per query due to serialization/deserialization overhead","No built-in query optimization — complex multi-filter searches may require manual query composition","Vector embeddings must be pre-computed; the MCP server does not generate embeddings itself","Batch insert performance degrades with payloads >1MB per point; Qdrant recommends keeping metadata lean","No built-in deduplication — duplicate detection must be handled by client logic","Upsert operations are not atomic across multiple collections; cross-collection consistency requires external coordination","Complex nested filters (deeply nested AND/OR logic) can degrade query performance; Qdrant recommends flattening where possible","Filter evaluation happens server-side after vector search, not before — cannot use filters to reduce search space before similarity computation","Geo-spatial filters require pre-computed geographic metadata; no built-in geocoding","Schema introspection is read-only; cannot modify collection configuration through MCP","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.047Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qdrant","compare_url":"https://unfragile.ai/compare?artifact=qdrant"}},"signature":"+GkApttWIt+bAuDcVzqmFKPMPdu2DNI5JmiboAwBdRNABTxr+tPmFtlKiY4/gMvlmEEpjomljSpIspk7JuBDDQ==","signedAt":"2026-06-23T02:50:59.558Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qdrant","artifact":"https://unfragile.ai/qdrant","verify":"https://unfragile.ai/api/v1/verify?slug=qdrant","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"}}