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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts free-form library names (e.g., 'mongo', 'react hooks') and resolves them to Context7-compatible canonical library IDs through the /v1/search API endpoint. The MCP tool 'resolve-library-id' wraps this API call, encrypting the client IP in the mcp-client-ip header for privacy-preserving analytics. Returns a list of matching library IDs with descriptions, enabling downstream documentation retrieval without requiring users to know exact library identifiers.
Unique: Implements privacy-preserving library search by encrypting client IP in request headers rather than logging raw IPs, while maintaining full API compatibility with Context7's backend search infrastructure. Uses MCP tool registration pattern to expose search as a callable function within LLM context.
vs alternatives: Faster than manual documentation site searches and more accurate than LLM hallucination of library names, because it queries a live, curated index of 100+ libraries rather than relying on training data or regex-based matching.
Fetches current, version-specific documentation for a resolved library ID via the GET /v1/:libraryID API endpoint. The 'get-library-docs' MCP tool accepts a canonical library ID, optional topic filter, and token limit (default 5000, minimum 1000), then returns formatted documentation text injected directly into the LLM's context window. Includes Authorization header with API key and X-Context7-Source header for request attribution, enabling the backend to track which MCP clients consume which libraries.
Unique: Implements token-bounded documentation retrieval with configurable limits (minimum 1000 tokens enforced server-side) to prevent context window overflow in LLMs, while maintaining version-specificity by querying the live Context7 API rather than serving static docs. Tracks request source via X-Context7-Source header for analytics and attribution.
vs alternatives: More current and accurate than static documentation snapshots or LLM training data, and more efficient than web scraping or manual API reference lookups, because it delivers live, curated docs with version awareness in a single API call.
Initializes an McpServer instance (src/index.ts) that implements the Model Context Protocol specification, supporting three transport mechanisms: stdio (default, for local IPC), HTTP (for remote clients on configurable port), and SSE (Server-Sent Events, for streaming responses). The server accepts CLI arguments (--transport, --port, --api-key) to configure deployment mode, enabling Context7 to run as a local tool in Cursor, a remote HTTP service, or an SSE-streaming endpoint. Tool registration happens during initialization, binding resolve-library-id and get-library-docs to the MCP request handler.
Unique: Abstracts transport mechanism selection via CLI arguments, allowing the same MCP server binary to operate in stdio (local), HTTP (remote), or SSE (streaming) modes without code changes. This transport-agnostic design enables Context7 to integrate with diverse MCP clients (Cursor, Claude Desktop, custom agents) through a single codebase.
vs alternatives: More flexible than hardcoded transport implementations (e.g., Copilot's HTTP-only or Cursor's stdio-only), because it supports three transport modes from a single deployment, reducing operational complexity for teams managing multiple MCP clients.
Encrypts the client's IP address in the mcp-client-ip request header before sending it to the Context7 backend API. This header is included in both resolve-library-id and get-library-docs API calls, enabling the backend to track library usage patterns and client distribution without logging raw IP addresses. The encryption approach (algorithm, key management) is not detailed in the provided DeepWiki excerpt, but the pattern ensures privacy compliance while maintaining analytics capability.
Unique: Implements privacy-by-design analytics by encrypting client IPs at the MCP server level before transmission to the backend, rather than logging raw IPs or relying on anonymization post-hoc. This ensures that even if the Context7 backend is compromised, client IP data remains encrypted.
vs alternatives: More privacy-preserving than unencrypted IP logging (standard in most analytics tools) and more useful than complete anonymization (which prevents usage tracking), because it enables backend analytics while maintaining client privacy guarantees.
Supports a context7.json configuration file that allows library authors and maintainers to define which libraries are indexed in the Context7 catalog, their metadata (name, description, versions), and documentation sources. The schema enables declarative library registration without modifying the Context7 MCP codebase. Libraries are indexed by the Context7 backend during build/deployment, making them discoverable via the resolve-library-id tool. This decouples library management from server deployment, allowing the catalog to grow without server updates.
Unique: Decouples library catalog management from MCP server deployment via a declarative context7.json schema, allowing library authors to self-serve library registration without modifying Context7 code or waiting for releases. This enables a crowdsourced, community-driven library catalog similar to npm or PyPI.
vs alternatives: More scalable than hardcoded library lists (which require server updates for each new library) and more flexible than centralized registry APIs (which may have approval delays), because it enables library authors to define their own metadata and documentation sources declaratively.
Registers two MCP tools (resolve-library-id and get-library-docs) with the McpServer instance, mapping each tool to a specific API function and parameter schema. The server's request handler routes incoming MCP tool calls to the appropriate function, validates parameters (e.g., enforcing minimum token limit of 1000 for get-library-docs), and returns structured responses. This tool registration pattern follows the MCP specification, enabling LLM clients to discover available tools via the MCP protocol and invoke them with type-safe parameters.
Unique: Implements MCP tool registration with parameter validation (e.g., minimum token limit enforcement) at the server level, ensuring that invalid requests are rejected before reaching the backend API. This reduces unnecessary API calls and provides immediate feedback to clients about parameter errors.
vs alternatives: More robust than client-side validation alone, because server-side validation ensures that all requests (regardless of client implementation) meet minimum requirements, preventing malformed API calls and reducing backend load.
Retrieves version-specific documentation via get-library-docs and injects the formatted text directly into the LLM's context window, enabling the model to reference current APIs during code generation. The documentation is fetched at prompt time (not training time), ensuring the LLM always has access to the latest library APIs. This pattern addresses the core problem Context7 solves: LLMs trained on historical data generate code using outdated or hallucinated APIs. By injecting live docs into the context, the LLM can generate accurate, version-aware code without retraining.
Unique: Implements just-in-time documentation injection at prompt time rather than relying on LLM training data, using the MCP tool calling pattern to fetch and inject docs within the LLM's context window. This ensures the LLM has access to current APIs without requiring model retraining or fine-tuning.
vs alternatives: More effective than RAG (Retrieval-Augmented Generation) systems that rely on vector similarity, because it fetches exact, version-specific documentation from the authoritative source (Context7 API) rather than searching a potentially stale vector database. More practical than LLM retraining, because it works with existing models and updates instantly as libraries change.
Includes X-Context7-Source header in get-library-docs API calls to track which MCP client (e.g., 'cursor', 'claude-desktop', 'custom-agent') is consuming documentation. This enables the Context7 backend to attribute usage to specific clients and build analytics on which tools are using which libraries. The header is set by the MCP server based on client identification (mechanism not documented in excerpt), allowing the backend to correlate documentation requests with client types without storing raw request metadata.
Unique: Implements client attribution via HTTP headers rather than query parameters or request body, enabling transparent tracking without modifying API request structure. This allows the backend to correlate documentation requests with client types for analytics without requiring clients to explicitly identify themselves.
vs alternatives: More transparent than user-agent sniffing (which is unreliable) and more efficient than explicit client registration (which requires additional API calls), because it uses standard HTTP headers to identify clients with minimal overhead.
+1 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 Context 7 at 22/100.
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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