Ref
MCP ServerFree** - Up-to-date documentation for your coding agent. Covers 1000s of public repos and sites. Built by [ref.tools](https://ref.tools/)
Capabilities8 decomposed
token-efficient semantic documentation search with context filtering
Medium confidencePerforms semantic search across 1000+ public repositories and documentation sites through the Ref API, returning intelligently filtered results that minimize irrelevant context. The system tracks session-based search trajectories to avoid redundant queries and implements result ranking to surface the most relevant documentation snippets, reducing token consumption compared to unfiltered full-document retrieval.
Implements session-based search trajectory tracking (index.ts 537-544) to maintain stateful search context across multiple requests, combined with client-specific response formatting (DeepResearchShape for OpenAI vs plain text for MCP) to optimize both token efficiency and client compatibility. Uses Ref API's pre-indexed corpus of 1000+ repos rather than requiring local indexing.
More token-efficient than RAG systems requiring full document loading because it returns filtered snippets with source attribution, and faster than web search because it queries a pre-indexed documentation corpus rather than crawling in real-time.
url-based documentation content extraction with client-aware formatting
Medium confidenceFetches and extracts content from specific documentation URLs through the Ref API, returning formatted content optimized for the detected client type. Implements client detection logic (index.ts 23-37, 394-422) to return DeepResearchShape JSON for OpenAI clients or plain text for standard MCP clients, enabling seamless integration across different AI agent architectures.
Implements dynamic client detection and response formatting (createServerInstance function, index.ts 61-212) that adapts output structure based on detected client type without requiring explicit configuration. Uses Ref API's server-side HTML parsing rather than client-side extraction, reducing agent complexity.
More reliable than generic web scraping because it uses Ref API's documentation-aware parsing, and more flexible than hardcoded response formats because it auto-detects client type and returns appropriate structure (JSON for OpenAI, text for MCP).
multi-transport mcp server deployment with stateful http sessions
Medium confidenceDeploys as an MCP server supporting both stdio (local npm package) and HTTP (remote service) transports, with HTTP transport implementing session management through transports and sessionClientInfo objects (index.ts 376-536, 537-544). Enables stateful interactions across multiple requests in HTTP mode while maintaining compatibility with local stdio execution, allowing the same codebase to serve both embedded and remote deployment scenarios.
Implements transport abstraction (StdioServerTransport vs StreamableHTTPServerTransport) with unified tool handling logic, enabling single codebase deployment across local and remote scenarios. HTTP transport includes session tracking via transports and sessionClientInfo objects for stateful multi-request interactions, while stdio remains stateless.
More flexible than single-transport MCP servers because it supports both local and remote deployment without code duplication, and more stateful than typical HTTP APIs because it maintains per-client session context for search trajectory tracking.
priority-based api authentication with runtime configuration override
Medium confidenceImplements a three-tier authentication resolution system (getAuthHeaders function, index.ts 221-242) that prioritizes runtime configuration over environment variables, enabling dynamic API key switching without server restart. Supports both standard REF_API_KEY and early-access REF_ALPHA authentication paths, constructing appropriate X-Ref-Api-Key or X-Ref-Alpha headers and including session identifiers for HTTP transport requests.
Implements priority-based resolution (runtime config > environment variables > alpha access) allowing dynamic API key switching via HTTP parameters without server restart, combined with session identifier injection for stateful API interactions. Supports both standard and alpha authentication paths.
More flexible than static environment-variable-only authentication because it allows runtime override, and more secure than hardcoded keys because it supports environment-based and runtime-configured credentials with session isolation.
client-type detection and response format adaptation
Medium confidenceDynamically detects client type through multiple mechanisms (User-Agent headers, explicit hints, client registry) and adapts tool response formats accordingly. OpenAI clients receive DeepResearchShape JSON objects with structured title/content/source fields, while standard MCP clients receive plain text markdown, enabling seamless integration across heterogeneous AI agent architectures without requiring client-specific configuration.
Implements client detection and response formatting within createServerInstance (index.ts 61-212) using dynamic tool name and response format configuration based on detected client type, enabling single MCP server to serve both OpenAI and standard MCP clients transparently without requiring separate server instances.
More flexible than single-format MCP servers because it adapts response structure based on client type, and more seamless than requiring explicit client configuration because detection is automatic via User-Agent and headers.
context-aware documentation search with session trajectory tracking
Medium confidenceTracks search history and query patterns within HTTP sessions to avoid redundant searches and inform result ranking. The session-based trajectory system (index.ts 537-544) maintains per-client search context, enabling the system to understand search intent progression and filter results based on previous queries, reducing token waste from repeated documentation lookups and improving result relevance over multiple agent interactions.
Implements session-based search trajectory tracking (transports and sessionClientInfo objects) that maintains per-client search history and uses it to filter redundant results and inform ranking, enabling context-aware search across multiple agent interactions without requiring explicit context passing.
More context-aware than stateless search APIs because it tracks search history within sessions, and more efficient than full RAG systems because it uses trajectory information to avoid redundant retrievals rather than storing all results.
npm package and docker deployment with environment-based configuration
Medium confidenceProvides multiple deployment methods (npm package, Docker container, HTTP server, Smithery platform) with unified environment-variable-based configuration. Supports TRANSPORT_TYPE selection, API key configuration via REF_API_KEY/REF_ALPHA, and HTTP port customization, enabling flexible deployment across development, staging, and production environments without code changes.
Supports four distinct deployment methods (npm, Docker, HTTP, Smithery) from single codebase using environment-based configuration, enabling teams to choose deployment strategy without code changes. Unified configuration approach across all deployment methods.
More flexible than single-deployment-method tools because it supports npm, Docker, HTTP, and Smithery without code duplication, and more portable than hardcoded configuration because environment variables enable seamless environment switching.
mcp tool schema definition with client-specific tool naming
Medium confidenceDefines two core MCP tools (search_documentation and read_url) with client-specific naming conventions and schema validation. The tool definitions include input schemas with required/optional parameters, output descriptions, and client-specific naming adaptations (e.g., different tool names for OpenAI vs standard MCP clients), enabling proper tool discovery and invocation across heterogeneous MCP clients.
Implements client-specific tool naming and schema adaptation within CallToolRequestSchema handler (index.ts 65-93), allowing same tool to be exposed with different names to different clients (e.g., search_documentation for OpenAI, ref_search for standard MCP) without duplicating tool logic.
More flexible than static tool definitions because it adapts tool names based on client type, and more discoverable than implicit tools because it provides explicit MCP schema definitions for proper client integration.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Ref, ranked by overlap. Discovered automatically through the match graph.
context7
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
Mastra/mcp-docs-server
** - Provides AI assistants with direct access to Mastra.ai's complete knowledge base.
context7
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
VpunaAiSearch
** - Connect to [Vpuna AI Search Service](https://aisearch.vpuna.com), a developer first platform for semantic search, summarization, and contextual chat. Each project dynamically exposes its own Remote HTTP MCP server, enabling real-time context injection from structured and unstructured data.
example-remote-server
A hosted version of the Everything server - for demonstration and testing purposes, hosted at https://example-server.modelcontextprotocol.io/mcp
MCP Servers Search
** - An MCP server that provides tools for querying and discovering available MCP servers from this list.
Best For
- ✓AI coding agents and LLM-powered IDEs needing lightweight documentation access
- ✓Teams building agentic systems with strict token budgets
- ✓Developers working with unfamiliar or rapidly-evolving libraries
- ✓AI agents that have identified a specific documentation URL and need full content
- ✓Multi-client deployments serving both OpenAI and standard MCP clients
- ✓Workflows combining search results with targeted deep-dives into specific pages
- ✓Teams deploying MCP servers across multiple environments (local, cloud, containerized)
- ✓Multi-tenant scenarios requiring per-client session isolation
Known Limitations
- ⚠Search results limited to indexed public repositories and documentation sites — private/internal docs require separate indexing
- ⚠Search quality depends on Ref API's indexing freshness; real-time documentation updates may lag
- ⚠Session-based trajectory tracking only persists within HTTP transport mode, not stdio mode
- ⚠No full-text search fallback if semantic search returns insufficient results
- ⚠Requires valid, accessible URL — will fail on private/authenticated documentation pages
- ⚠Content extraction quality depends on Ref API's HTML parsing; complex layouts may not extract cleanly
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
** - Up-to-date documentation for your coding agent. Covers 1000s of public repos and sites. Built by [ref.tools](https://ref.tools/)
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