"General search for the term 'Agent'" matching MCP servers:

Enables agents to read the published article and individual sections, with optional Russian or English language selection and revision pinning.

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.

MCP server for hybrid documentation search (keyword + vector) and RAG chat, enabling natural language queries with cited answers from indexed repositories.

Enables agents to search, analyze, and explore arXiv academic papers with advanced multi-field search, author lookup, category browsing, citation extraction, and bibliography export.

Search Vascue's public healthcare-operations documentation: AI front desk for clinics, provider-side insurance claims automation, Cliniko integration, security. Public content only.

Agent Ready is an AI agent readability scanner — point it at any public URL and get back a 0–100 score plus per-check remediation hints for every failing check. This package wraps the same engine that powers agent-ready.dev as an MCP server, so Claude Desktop, Claude Code, Cursor, Cline, VS Code, and Windsurf can run scans inline and explain the results.

A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Provides 14 enhanced intelligent search tools for both international platforms (GitHub, StackOverflow, NPM, etc.) and Chinese platforms (CSDN, Juejin, WeChat docs, etc.). Each tool includes smart URL generation, search tips, and related suggestions for comprehensive technical research.

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.

CLI and MCP server for AI agent strategy and deployment. Read blueprints, business cases, implementation plans, and specs from your coding agent. 18 tools for listing, retrieving, downloading, and updating blueprints. Exports full implementation specs as Agent Skills directories.

Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.

MCP server for Yandex Wiki with full-text search. Read and write pages, comments, attachments, and dynamic tables (grids); optional server-side read-only mode for agents. Docker-ready.

Live, ranked search across any number of llms.txt documentation sites - Strands, Kiro, the AWS guides, and whatever you add at runtime.

Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.

Enables generating UI design rules, fetching color palettes, and retrieving brand design references from over 328 brands.

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 AI coding assistants to perform real-time web searches for technical documentation and deep-fetch web pages, returning clean structured results. It supports integration with MCP clients such as Cursor, Claude, and CodeBuddy.

Enables searching and retrieving documentation files from a local docs folder using a search tool and dynamic resource URIs.

A Model Context Protocol server that enables intelligent searching across documentation for 30+ programming libraries and frameworks, fetching relevant information from official sources.

Enables AI coding providers to run structured multi-agent workflows through MCP by owning task claims, action logs, handoffs, health gates, and specification metadata. It coordinates use cases, specs, implementation, review, and bounded repair while keeping document files on the filesystem.

Read-only MCP server that provides tools to fetch SealChat public protocol docs, manifest, channel counts, and chat messages via the HTTP Agent API, without write access or database access.

Enables any MCP client to access the Panaversity Agent Factory System of Record curriculum, with login handling and tools for searching, outlining, and reading lessons.

Serves a site's validated public profile, product list and newest-first posts to AI agents through three read-only tools (get_profile, list_products, list_posts) over stdio or any Web Request/Response runtime.

Searches OpenSearch documentation, blogs, and community forums.

Provides tools to search MDN documentation, get page summaries, and retrieve browser compatibility data.

Enables MCP clients to browse a read-only catalog of published products and their documented interfaces, listing entries, fetching one by stable ID, and searching names, descriptions, and limitations with optional protocol filters. It also exposes the versioned manifest as a resource and a prompt that helps clients match a task to catalog entries without inventing unsupported capabilities.

Enables MCP-compatible clients to semantically search, retrieve, and list engineering runbooks, providing relevant runbooks in response to natural language queries.

Provides MCP tools to count, list, and read local documents in formats like PDF, DOCX, Excel, HTML, and TXT, enabling natural language-driven document statistics and content inspection via qwen_agent.