| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Implements MCP-standardized semantic search by wrapping Qdrant's native vector similarity API with pluggable embedding providers (OpenAI, Ollama, local models), enabling LLM clients to perform semantic queries without direct Qdrant knowledge. The qdrant-find tool abstracts collection-specific search logic through configurable tool descriptions.
vs alternatives: Tighter integration with LLM workflows than raw Qdrant clients because it handles embedding generation transparently and exposes search as a standardized MCP tool callable by any MCP-compatible client (Claude, Cursor, Windsurf).
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').
Unique: Provides MCP-standardized vector storage through the qdrant-store tool, which abstracts Qdrant's point insertion API and handles embedding generation transparently. Supports arbitrary metadata schemas without pre-definition, allowing flexible organization of stored content across different use cases.
vs alternatives: Simpler than managing raw Qdrant clients because embedding generation and MCP protocol handling are built-in; more flexible than fixed-schema vector databases because metadata is schema-free and queryable.
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.
Unique: Implements metadata filtering as a post-search step applied to vector similarity results, allowing arbitrary metadata schemas without pre-definition. Filters are applied in the MCP server layer, not in Qdrant, enabling flexible filtering logic.
vs alternatives: More flexible than pre-defined schemas because metadata is schema-free; less efficient than pre-filter vector search because filtering happens after similarity computation.
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.
Unique: Uses environment variables as the sole configuration mechanism, eliminating config files and enabling pure containerized deployments. All settings (Qdrant URL, embedding provider, collections, transport) are configurable via environment variables.
vs alternatives: Simpler than config file management because environment variables are native to containerized environments; more secure than hardcoded defaults because secrets can be injected at runtime.
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.
Unique: Implements dynamic MCP tool registration based on Qdrant collection configuration, allowing a single server instance to expose multiple isolated search/storage interfaces. The tool filtering mechanism enables selective collection exposure without code changes, supporting multi-tenant and permission-based deployments.
vs alternatives: More operationally efficient than running separate MCP servers per collection because it consolidates infrastructure; more flexible than single-collection servers because it supports diverse use cases in one deployment.
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.
Unique: Implements a provider-agnostic embedding abstraction that allows runtime selection of embedding models (OpenAI, Ollama, local) via configuration, with support for per-collection embedding strategies. The abstraction is transparent to MCP clients, which never interact with embedding provider details directly.
vs alternatives: More flexible than hardcoded embedding providers because it supports multiple models and allows switching without code changes; more practical than raw Qdrant because it handles embedding generation transparently rather than requiring clients to manage embeddings separately.
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.
Unique: Implements full MCP specification compliance for vector search and storage, exposing Qdrant capabilities as standardized tools discoverable by any MCP client. The server handles protocol serialization, transport abstraction (stdio/SSE/HTTP), and tool schema registration automatically.
vs alternatives: More seamless than custom plugins because MCP is a standard protocol supported natively by Claude, Cursor, and Windsurf; more flexible than direct API clients because it abstracts transport and protocol details.
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.
Unique: Implements read-only mode by conditionally registering MCP tools at startup, completely removing write capabilities rather than adding runtime checks. This is a deployment-level safety mechanism rather than a per-operation guard.
vs alternatives: Simpler and more reliable than runtime permission checks because it prevents write tools from being registered at all; more appropriate for production than relying on client-side enforcement.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs mcp-server-qdrant at 32/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities