"A system for building an autonomous second brain" matching MCP servers:

Enables retrieval of compact APG pattern cards, such as button, modal, and dialog, through an MCP tool so clients can consult accessibility guidance.

Automatically analyzes codebases and generates beautiful Mintlify-style documentation with API references, code examples, and changelogs. Keeps documentation synchronized with code changes across multiple programming languages.

An MCP server providing steel domain knowledge to AI agents — grade properties, substitution verdicts, and mill-cert guidance as MCP tools. 23 grades, trade-verified substitution logic. Returns honest "not in knowledge base" rather than guessing.

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

Enables offline LSL and SLua reference lookup, code checking, migration guidance, and optional pinned local compilation/static analysis through a combined stdio MCP server, with separate language-specific components.

An MCP server that enables AI agents to search, read, and contribute to a structured markdown knowledge base with citations, freshness tracking, and a safe write path, providing a shared, auditable company memory.

Provides structured instructions to Cursor for generating comprehensive system summaries of Node.js/TypeScript codebases.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Provides a real API for coding agents to look up design system components, props, and tokens, preventing guessed answers.

Enables an MCP client to retrieve explicitly allowlisted Markdown context, create and read local task contracts, write local handoffs, and return locally configured routing metadata without invoking a model, executing commands, or writing to the vault.

Provides AI agents with component documentation, usage examples, and design tokens from the WordPress Design System, enabling them to follow the latest design system guidance for creating user interfaces.

An MCP server that reads any codebase (GitHub repo or local folder) and explains it using AI. Enables users to ask questions about project structure, key files, auth flows, and more in plain language.

Enables AI clients to search and read FastAPI documentation through hybrid semantic and keyword retrieval, returning ranked, citable sections and full pages as structured, typed results. All retrieved text is screened by a prompt-injection guard that strips suspicious instructions and flags the results, so only indexed pages can be read and nothing hostile reaches the model.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Model Context Protocol server exposing system prompt files and summaries.

A local AI assistant that answers questions across a company's internal knowledge using MCP, providing controlled access to internal documents with role-based permissions and citations.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Provides AI assistants with locally bundled tools to explore NIST OSCAL models, retrieve schemas, list community resources, and generate valid OSCAL templates for governance, risk, and compliance workflows.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables users to search and navigate over 22,500 indexed sections across 16 Canadian building codes and user guides. It supports keyword searches, hierarchy navigation, and optional full-text extraction from user-provided PDF documents.

Enables AI agents to search, fetch, and clean live documentation from LangChain, LlamaIndex, and OpenAI via Model Context Protocol, with token-optimized extraction.

Enables Claude to read and analyze PDF documents with automatic OCR processing for scanned files. Features intelligent text extraction, caching for performance, and secure file access with search capabilities.

A Model Context Protocol (MCP) server providing accurate LSL (Linden Scripting Language) reference data to AI coding assistants, grounded in the official Second Life wiki with a curated database of AI-specific pitfalls.

An unofficial MCP server that gives AI assistants structured access to Italy's Design System .italia, including components, design tokens, usage guidelines, and GitHub issues, updated nightly.

Provides access to Appian's Aurora design system documentation via GitHub, enabling LLMs to query components, layouts, and patterns. It supports both public and internal repositories with features for keyword searching and detailed component guidance.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

Exposes the Toggle Brain knowledge base, prompt templates, and executable scripts to any MCP-compatible AI assistant.

Enables IDEs like Cursor, Windsurf, and Claude Code to access and query Appwrite documentation through a vector-powered search system. Provides developers with contextual Appwrite documentation directly within their development environment.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.