{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-qdrant-mcp-server-qdrant","slug":"mcp-qdrant-mcp-server-qdrant","name":"mcp-server-qdrant","type":"mcp","url":"https://github.com/qdrant/mcp-server-qdrant","page_url":"https://unfragile.ai/mcp-qdrant-mcp-server-qdrant","categories":["mcp-servers"],"tags":["claude","cursor","llm","mcp","mcp-server","semantic-search","windsurf"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_0","uri":"capability://search.retrieval.semantic.search.with.vector.similarity","name":"semantic-search-with-vector-similarity","description":"Retrieves relevant information from Qdrant collections using semantic similarity matching rather than keyword search. The server converts user queries into embeddings using configurable embedding providers (OpenAI, Ollama, or local models), then performs vector similarity search against stored embeddings to find contextually relevant results. This enables natural language queries to match conceptually similar content even without exact keyword overlap.","intents":["Find relevant code snippets by describing what they do rather than searching for specific function names","Retrieve conversation context and facts semantically related to current LLM queries","Search knowledge bases using natural language descriptions instead of structured queries","Discover similar documents or entries based on semantic meaning across multiple collections"],"best_for":["AI application developers building context-aware LLM agents","teams implementing semantic memory layers for multi-turn conversations","knowledge management teams creating searchable documentation systems"],"limitations":["Embedding quality depends on the chosen embedding provider; local models may have lower semantic accuracy than cloud-based alternatives","Search latency increases with collection size; no built-in query optimization for very large datasets (>1M vectors)","Metadata filtering is applied post-search, not during vector search, potentially reducing efficiency for highly filtered queries"],"requires":["Python 3.10+","Active Qdrant instance (local or remote)","Embedding provider API key (OpenAI) or local embedding model (Ollama/Hugging Face)","Pre-populated Qdrant collection with embeddings"],"input_types":["text query string","optional metadata filter object"],"output_types":["ranked list of matching documents with similarity scores","associated metadata for each result","vector similarity scores (0-1 range)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_1","uri":"capability://memory.knowledge.vector.storage.with.metadata.association","name":"vector-storage-with-metadata-association","description":"Stores text content as semantic embeddings in Qdrant collections with associated structured metadata for filtering and organization. The server converts input text to embeddings via configured embedding providers, then persists both the embedding vector and metadata (custom key-value pairs) to Qdrant. This enables later retrieval with optional metadata-based filtering (e.g., retrieve only embeddings where source='documentation' AND date>'2024-01-01').","intents":["Store conversation turns with metadata (speaker, timestamp, conversation_id) for later retrieval","Index code snippets with metadata (language, function_name, file_path) for organized retrieval","Build knowledge bases where each entry has source, category, and version metadata","Maintain audit trails by storing content with creation date, author, and modification history metadata"],"best_for":["developers building persistent memory systems for LLM agents","teams managing multi-source knowledge bases with structured metadata","applications requiring content organization and filtering beyond semantic similarity"],"limitations":["Metadata filtering is applied post-vector-search, not during the vector search phase, which can be inefficient for highly selective filters","No built-in schema validation for metadata; malformed metadata objects may cause silent failures or inconsistent filtering behavior","Qdrant payload storage has size limits (~1MB per point); large metadata objects must be stored externally with only references in Qdrant"],"requires":["Python 3.10+","Active Qdrant instance with write permissions","Embedding provider (OpenAI API key, Ollama instance, or local model)","Text content to embed (string input)"],"input_types":["text content (string)","metadata object (JSON-serializable dict with custom key-value pairs)"],"output_types":["confirmation of storage with point ID","embedding vector (if requested)","metadata echo confirming stored values"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_10","uri":"capability://search.retrieval.metadata.filtering.with.post.search.application","name":"metadata-filtering-with-post-search-application","description":"Supports filtering search results by metadata attributes (e.g., source='documentation', date>'2024-01-01') applied after vector similarity search completes. The server accepts metadata filter expressions in search requests, performs the vector similarity search first, then filters results by metadata criteria. This enables combining semantic relevance with structured filtering, though with the caveat that filtering happens post-search rather than during the vector search phase.","intents":["Retrieve semantically similar results only from specific sources or categories","Filter search results by date ranges, authors, or other structured metadata","Combine semantic search with structured queries (e.g., 'find similar code from the last 30 days')","Organize large result sets by metadata attributes"],"best_for":["applications with rich metadata requiring combined semantic and structured filtering","knowledge bases organized by source, date, or category","teams needing to restrict search results to specific data subsets"],"limitations":["Filtering is applied post-search, not during vector search, so it cannot reduce the vector search scope; inefficient for highly selective filters","No built-in query optimization; complex filter expressions may require scanning all search results","Filter syntax is custom to this implementation; no standard query language support"],"requires":["Python 3.10+","Metadata stored with vectors in Qdrant","Filter expression in search request (custom syntax or JSON format)"],"input_types":["search query (text)","metadata filter object (key-value pairs with comparison operators)"],"output_types":["filtered search results","result count after filtering"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_11","uri":"capability://automation.workflow.environment.variable.based.configuration.system","name":"environment-variable-based-configuration-system","description":"Centralizes all server configuration (Qdrant connection, embedding provider, collections, transport protocol) in environment variables, enabling deployment without code changes or config files. The server reads environment variables at startup and applies them to initialize connections, register tools, and configure behavior. This pattern enables containerized deployments, CI/CD pipelines, and multi-environment setups where configuration varies but code is identical.","intents":["Deploy the same server binary to development, staging, and production with different configurations","Configure the server in containerized environments (Docker, Kubernetes) without mounting config files","Manage secrets (API keys) via environment variables in secure CI/CD pipelines","Enable rapid configuration changes without rebuilding or redeploying code"],"best_for":["teams using containerized deployments (Docker, Kubernetes)","organizations with CI/CD pipelines requiring environment-specific configuration","developers wanting to avoid config file management"],"limitations":["Environment variables are flat; complex multi-collection setups may require many variables or custom parsing","No built-in validation of environment variables; misconfiguration may cause silent failures at runtime","Configuration is static at server startup; changing configuration requires server restart"],"requires":["Python 3.10+","Environment variables set before server startup (via shell, Docker, Kubernetes, etc.)"],"input_types":["environment variables (QDRANT_URL, EMBEDDING_PROVIDER, etc.)"],"output_types":["initialized server with configuration applied"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_2","uri":"capability://tool.use.integration.multi.collection.management.with.tool.filtering","name":"multi-collection-management-with-tool-filtering","description":"Manages multiple Qdrant collections within a single MCP server instance, with per-collection tool registration and optional filtering to expose only specific collections to clients. The server loads collection configurations from environment variables or config files, dynamically registers qdrant-store and qdrant-find tools for each collection, and can selectively hide collections based on client permissions or deployment context. This enables a single server to serve multiple use cases (e.g., code search, documentation search, conversation memory) with isolated data and independent embedding strategies.","intents":["Run a single MCP server that manages separate semantic search indexes for different data types (code, docs, conversations)","Expose different collections to different LLM clients based on permissions or deployment environment","Organize large knowledge bases into logical collections for better performance and management","Support multiple embedding models by assigning different models to different collections"],"best_for":["teams running shared MCP infrastructure serving multiple applications","enterprises with multi-tenant deployments requiring collection-level isolation","developers managing diverse knowledge bases (code, documentation, conversation history) in one server"],"limitations":["Tool registration is static at server startup; adding/removing collections requires server restart","No built-in cross-collection search; queries must target a specific collection","Tool filtering is coarse-grained (include/exclude entire collections); no field-level or row-level access control"],"requires":["Python 3.10+","Multiple Qdrant collections pre-created in target Qdrant instance","Environment variables or config file defining collection names and embedding strategies","MCP client supporting dynamic tool discovery"],"input_types":["collection configuration (environment variables or YAML/JSON config)","optional tool filter list (collection names to expose)"],"output_types":["dynamically registered MCP tools (qdrant-store-{collection}, qdrant-find-{collection})","tool availability list reflecting filtered collections"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_3","uri":"capability://tool.use.integration.pluggable.embedding.provider.abstraction","name":"pluggable-embedding-provider-abstraction","description":"Abstracts embedding generation behind a provider interface supporting OpenAI, Ollama, and local Hugging Face models. The server loads the configured embedding provider at startup (via environment variables), then transparently generates embeddings for all store and search operations without exposing provider details to clients. This enables switching embedding models (e.g., from OpenAI to local Ollama) by changing configuration, not code, and allows different collections to use different embedding models simultaneously.","intents":["Switch between cloud-based (OpenAI) and local embedding models without code changes","Use cost-effective local embeddings (Ollama) for development and OpenAI for production","Assign different embedding models to different collections based on accuracy/latency tradeoffs","Avoid vendor lock-in by supporting multiple embedding providers with identical interfaces"],"best_for":["developers wanting flexibility to switch embedding providers based on cost/performance","teams deploying to both cloud and on-premise environments with different embedding strategies","organizations avoiding vendor lock-in to a single embedding provider"],"limitations":["Embedding quality varies significantly between providers; switching providers requires re-embedding all stored content","Local embedding models (Ollama) require additional infrastructure (GPU or CPU-intensive server) and have higher latency than cloud APIs","No built-in embedding model versioning; changing models can cause semantic drift in search results for existing data"],"requires":["Python 3.10+","One of: OpenAI API key, Ollama instance (http://localhost:11434), or local Hugging Face model","Environment variable specifying embedding provider (EMBEDDING_PROVIDER)","Provider-specific credentials or connection details"],"input_types":["text to embed (string)","embedding provider configuration (environment variables)"],"output_types":["embedding vector (list of floats, typically 1536 dimensions for OpenAI, variable for others)","provider metadata (model name, dimension count)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_4","uri":"capability://tool.use.integration.mcp.protocol.compliant.tool.exposure","name":"mcp-protocol-compliant-tool-exposure","description":"Implements the Model Context Protocol (MCP) specification to expose vector storage and search operations as standardized tools callable by MCP-compatible clients (Claude, Cursor, Windsurf, VS Code). The server registers tools with MCP-compliant schemas (input/output types, descriptions), handles MCP protocol messages (tool calls, responses), and manages the stdio/SSE/HTTP transport layer. This enables LLM clients to invoke semantic search and storage operations as native tools without custom integrations.","intents":["Enable Claude, Cursor, and other MCP clients to call semantic search without custom plugins","Expose vector storage as a tool that LLM agents can invoke to persist information","Provide standardized tool schemas that LLM clients can discover and use automatically","Integrate Qdrant capabilities into IDE workflows (VS Code, Cursor) via MCP protocol"],"best_for":["developers building LLM agents in Claude, Cursor, or Windsurf","teams integrating Qdrant into IDE-based AI workflows","organizations standardizing on MCP for LLM tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms latency per tool invocation due to serialization and transport","Tool discovery is static at server startup; dynamic tool registration requires server restart","No built-in rate limiting or quota management for tool calls; high-volume usage may require external throttling"],"requires":["Python 3.10+","MCP-compatible client (Claude Desktop, Cursor, Windsurf, VS Code with MCP extension)","Server running with stdio, SSE, or HTTP transport","Client configured with server connection details (stdio command or HTTP endpoint)"],"input_types":["MCP tool call messages (JSON-RPC format)","tool input parameters (query string, metadata filter, text to store)"],"output_types":["MCP tool response messages (JSON-RPC format)","structured tool results (search results, storage confirmation)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_5","uri":"capability://safety.moderation.read.only.mode.for.production.deployments","name":"read-only-mode-for-production-deployments","description":"Provides an optional read-only mode that disables write operations (qdrant-store tool) while preserving search functionality (qdrant-find tool). This is configured via environment variable at server startup and prevents accidental or malicious data modification in production environments. The server registers only the qdrant-find tool when read-only mode is enabled, effectively removing the ability to store new data while maintaining full search capabilities.","intents":["Deploy a production Qdrant server that serves search queries without allowing data mutations","Prevent accidental data corruption from misconfigured clients or user errors","Separate read and write access across different server instances (read-only for inference, write-enabled for data ingestion)"],"best_for":["production deployments where data integrity is critical","teams separating read and write access across infrastructure","organizations with strict change control policies"],"limitations":["Read-only mode is all-or-nothing; no fine-grained write restrictions (e.g., allow updates but not deletes)","Switching between read-only and write-enabled modes requires server restart","No audit logging of attempted write operations in read-only mode"],"requires":["Python 3.10+","Environment variable READ_ONLY_MODE=true","Pre-populated Qdrant collections (no new data can be added in read-only mode)"],"input_types":["configuration flag (READ_ONLY_MODE environment variable)"],"output_types":["tool registration list excluding qdrant-store tools","error responses if clients attempt store operations"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_6","uri":"capability://tool.use.integration.local.and.remote.qdrant.connectivity","name":"local-and-remote-qdrant-connectivity","description":"Supports connections to both local file-based Qdrant instances (for development/testing) and remote Qdrant Cloud or self-hosted servers (for production). The server accepts Qdrant connection parameters (URL, API key) via environment variables and abstracts the connection details behind a QdrantConnector interface. This enables the same server code to work across development (local SQLite-backed Qdrant) and production (cloud-hosted Qdrant) without code changes.","intents":["Use local Qdrant for development and testing without cloud infrastructure","Connect to Qdrant Cloud for production deployments with managed infrastructure","Switch between local and remote Qdrant by changing environment variables","Support self-hosted Qdrant servers for on-premise deployments"],"best_for":["developers testing locally before deploying to production","teams using Qdrant Cloud for managed infrastructure","organizations with on-premise requirements"],"limitations":["Local Qdrant (file-based) has significantly lower performance than cloud instances for large datasets (>100K vectors)","Connection parameters are static at server startup; changing Qdrant instances requires server restart","No built-in connection pooling or failover; connection failures cause server unavailability"],"requires":["Python 3.10+","For local: Qdrant library (installed as dependency)","For remote: Qdrant Cloud account or self-hosted Qdrant server with accessible URL","Environment variables: QDRANT_URL and QDRANT_API_KEY (for remote) or QDRANT_PATH (for local)"],"input_types":["connection configuration (environment variables or config file)"],"output_types":["active Qdrant connection","connection status and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_7","uri":"capability://automation.workflow.docker.containerization.with.environment.configuration","name":"docker-containerization-with-environment-configuration","description":"Provides a Dockerfile and Docker Compose configuration for containerized deployment of the MCP server, with all settings (Qdrant connection, embedding provider, collections) configurable via environment variables. The container image includes Python 3.10+, dependencies, and the server code, enabling deployment to Kubernetes, Docker Swarm, or other container orchestration platforms. Environment variables passed at container runtime configure the server without rebuilding images.","intents":["Deploy the MCP server to Kubernetes or Docker Swarm with environment-based configuration","Create reproducible deployments across development, staging, and production environments","Scale the server horizontally by running multiple container instances","Integrate the server into CI/CD pipelines with containerized testing"],"best_for":["teams using Kubernetes or container orchestration","organizations with containerized deployment pipelines","developers wanting reproducible, portable deployments"],"limitations":["Container image size is ~500MB+ due to Python dependencies; may be slow to pull in bandwidth-constrained environments","No built-in health checks or graceful shutdown handling; orchestrators must implement liveness/readiness probes","Environment variable configuration is flat; complex multi-collection setups may require config file mounting"],"requires":["Docker or container runtime","Docker Compose (optional, for local testing)","Environment variables for Qdrant connection and embedding provider","Container orchestration platform (Kubernetes, Docker Swarm) for production"],"input_types":["Dockerfile and Docker Compose templates","environment variables at container runtime"],"output_types":["running container with MCP server","exposed port (default 8000 for HTTP transport)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_8","uri":"capability://tool.use.integration.transport.protocol.abstraction.stdio.sse.http","name":"transport-protocol-abstraction-stdio-sse-http","description":"Abstracts MCP communication over multiple transport protocols (stdio, SSE, HTTP) via a pluggable transport layer. The server can operate in stdio mode (for direct process communication), SSE mode (for server-sent events over HTTP), or streamable HTTP mode (for request/response over HTTP), selected via configuration. This enables the same server code to work with different client integration patterns without code changes.","intents":["Use stdio transport for direct process communication with Claude Desktop or local tools","Deploy over HTTP/SSE for remote clients or cloud-based LLM applications","Support multiple clients with different transport preferences from a single server instance","Integrate with existing HTTP infrastructure (load balancers, reverse proxies)"],"best_for":["developers integrating with Claude Desktop (stdio) or remote clients (HTTP/SSE)","teams deploying to cloud platforms requiring HTTP endpoints","organizations with existing HTTP infrastructure"],"limitations":["Stdio transport is single-client only; multiple concurrent clients require separate server instances","HTTP/SSE transport adds latency (~50-100ms) compared to stdio due to network overhead","No built-in authentication for HTTP transport; requires external reverse proxy for security"],"requires":["Python 3.10+","Transport protocol selection via environment variable or config","For HTTP/SSE: exposed port (default 8000) and network accessibility"],"input_types":["transport protocol configuration (TRANSPORT_TYPE environment variable)","MCP messages in protocol-specific format (JSON-RPC over stdio/HTTP/SSE)"],"output_types":["MCP responses in protocol-specific format","HTTP status codes (for HTTP transport)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-qdrant-mcp-server-qdrant__cap_9","uri":"capability://tool.use.integration.custom.tool.description.generation","name":"custom-tool-description-generation","description":"Generates human-readable tool descriptions for qdrant-store and qdrant-find tools based on collection metadata and configuration. The server creates descriptive text explaining what each tool does, what parameters it accepts, and what results it returns, enabling LLM clients to understand tool purpose without documentation. Descriptions can be customized via configuration to reflect domain-specific language (e.g., 'Search our code repository' vs 'Perform semantic search').","intents":["Help LLM models understand what each tool does and when to use it","Provide domain-specific descriptions that match application terminology","Enable self-documenting tools that don't require external documentation","Improve LLM decision-making by providing clear tool semantics"],"best_for":["teams deploying multiple collections with domain-specific semantics","developers wanting LLM models to understand tool purpose automatically","organizations with non-technical stakeholders who need to understand tool capabilities"],"limitations":["Tool descriptions are static at server startup; changing descriptions requires server restart","No built-in description validation; poorly written descriptions may confuse LLM models","Descriptions are limited to text; no support for examples or structured documentation"],"requires":["Python 3.10+","Collection configuration with optional description overrides"],"input_types":["collection metadata (name, description)","optional custom description text"],"output_types":["tool description strings for MCP tool schema","parameter descriptions for each tool input"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Active Qdrant instance (local or remote)","Embedding provider API key (OpenAI) or local embedding model (Ollama/Hugging Face)","Pre-populated Qdrant collection with embeddings","Active Qdrant instance with write permissions","Embedding provider (OpenAI API key, Ollama instance, or local model)","Text content to embed (string input)","Metadata stored with vectors in Qdrant","Filter expression in search request (custom syntax or JSON format)","Environment variables set before server startup (via shell, Docker, Kubernetes, etc.)"],"failure_modes":["Embedding quality depends on the chosen embedding provider; local models may have lower semantic accuracy than cloud-based alternatives","Search latency increases with collection size; no built-in query optimization for very large datasets (>1M vectors)","Metadata filtering is applied post-search, not during vector search, potentially reducing efficiency for highly filtered queries","Metadata filtering is applied post-vector-search, not during the vector search phase, which can be inefficient for highly selective filters","No built-in schema validation for metadata; malformed metadata objects may cause silent failures or inconsistent filtering behavior","Qdrant payload storage has size limits (~1MB per point); large metadata objects must be stored externally with only references in Qdrant","Filtering is applied post-search, not during vector search, so it cannot reduce the vector search scope; inefficient for highly selective filters","No built-in query optimization; complex filter expressions may require scanning all search results","Filter syntax is custom to this implementation; no standard query language support","Environment variables are flat; complex multi-collection setups may require many variables or custom parsing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.477936945241276,"quality":0.34,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-04-27T12:25:02Z"},"community":{"stars":1382,"forks":267,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-qdrant-mcp-server-qdrant","compare_url":"https://unfragile.ai/compare?artifact=mcp-qdrant-mcp-server-qdrant"}},"signature":"Hs3lNZoHZ6cwvEOLYJQoBzVyWlfQs/p4vsUXTNGB5YZgEN8ps4HsylNsALJe7sHUjxlEDwBN0B1jzYI3PisqCQ==","signedAt":"2026-06-21T14:36:12.517Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-qdrant-mcp-server-qdrant","artifact":"https://unfragile.ai/mcp-qdrant-mcp-server-qdrant","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-qdrant-mcp-server-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"}}