"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.

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

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.

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.

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.

Enables reading and searching API documentation from YApi instances, allowing AI models to access interface definitions, project API lists, and search through API endpoints using YApi URLs or project IDs.

Enables LLMs to access banking, investment, and cryptocurrency data through a standardized MCP interface, featuring Plaid integration for account and transaction data and Robinhood for crypto trading with audit trails.

Enables AI assistants to explore and understand Protocol Buffer (.proto) files through fuzzy search, service definitions, and message structure analysis. Integrates with Cursor IDE to provide natural language queries about protobuf schemas and API definitions.

A secure and pluggable MCP server to run terminal commands on your local machine or cloud server — remotely, safely, and with LLMs or agentic clients.

An MCP server providing web search, image search, and page scraping tools to LLMs without requiring API keys.