"Using RAG to Provide Documentation to LLMs" matching MCP servers:

AI modifies a function but misses 5 callers that also need updating. crag fixes this — it builds precise call graphs via static analysis, so AI knows exactly what's affected before making changes.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Enables LLMs to interact with embedded devices by reading and writing Segger RTT data through a J-Link debugger.

MCP server bridging Spinnaker continuous delivery platform to LLMs for application, pipeline, and deployment management

Enables AI assistants to query databases using natural language, with automatic schema discovery and SQL compilation.

MCP server that lets LLMs interact with a running Prometheus instance via its API, enabling metric analysis, PromQL queries, and rule/target inspection. It also provides embedded documentation and guided runbooks for common Prometheus tasks.

Enables AI assistants and LLMs to query SigNoz observability data (metrics, traces, logs, alerts, dashboards) using natural language.

Provides access to your VictoriaTraces instance and seamless integration with VictoriaTraces APIs and documentation for querying traces, services, and operations.

Enables AI coding assistants to search and retrieve information from a locally ingested knowledge base using hybrid search, grounded in user-curated documentation.

Enables LLMs to read and search X posts, manage users and lists, upload media, and publish posts using the X API v2 with OAuth user context.

An MCP (Model Context Protocol) server for managing Tailscale resources using the official Tailscale Go client library v2. This server provides complete coverage of the Tailscale API with enhanced, self-descriptive tools powered by OpenAPI documentation.

A high-performance, asynchronous MCP server that provides comprehensive Google search and web content scraping capabilities through the Firecrawl API, designed for LLMs to retrieve external information efficiently.

Enables LLMs to read, write, browse, search, and subscribe to live data on OPC-UA industrial automation servers, with caching, discovery index, and support for stdio or HTTP transports.

A Model Context Protocol (MCP) server that enables LLMs to run ANY code safely in isolated Docker containers.

MCP proxy that wraps any MCP server and transparently converts JSON responses in tools/call to TOON format — a token-efficient alternative to JSON optimized for LLMs (~40% fewer tokens).

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

MCP server that integrates with the BlockSec https://blocksec.com platform to provide blockchain transaction analysis.

godoc-mcp is a Model Context Protocol (MCP) server that provides efficient access to Go documentation. It helps LLMs understand Go projects by providing direct access to package documentation without needing to read entire source files.

Enables AI agents to communicate with business agents across company boundaries using Google's A2A protocol, with tools for discovery, messaging, and connection management.

Bridges AI coding agents with the browser to provide visual debugging, real-time error capture, screenshot capabilities, DOM inspection, and interactive wireframing through a reverse proxy with injected developer tools.