"Search or general inquiry" matching MCP servers:

Enables coding agents to list, search, and read a curated Shopware developer and merchant knowledge base offline as plain Markdown files, without network access or an API key.

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

Exposes an internal engineering knowledge base to AI assistants, allowing users to search and retrieve standards, runbooks, and architecture decisions. It supports RAG-enhanced search, document scraping, and specialized prompts for incident investigation and code reviews.

MCP server for token-efficient access to Open Finance Brasil rules, enabling coding agents to search and retrieve specific regulations, OpenAPI specs, and business rules through progressive disclosure.

Enables AI agents to ingest documentation from websites or local files and perform semantic search over the indexed content using pgvector-backed embeddings.

Lets AI agents search and read a folder of Markdown documentation at query time, exposing heading-level search, whole-page or section retrieval, and page listing as tools and resources over stdio or Streamable HTTP. Can run as a CLI, a hosted endpoint, or a library inside an existing Node or Next.js site.

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

Provides coding agents with hybrid semantic and exact-identifier search across VHDL source, documentation, and general code, with cross-referencing and exact source attribution.

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.

Serves organizational context to MCP-capable AI agents from a local folder or GitHub repository, and lets agents propose changes that require human approval.

Enables AI models to search and read technical discussions, customizations, and error resolutions from the Sankhya Community forum. It provides tools to retrieve topic lists and extract clean content from specific posts to help resolve system-related issues.

Enables MCP-compatible clients to search and read local markdown documentation, allowing agents to retrieve relevant doc snippets or full files by path.

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

A professional literature search tool built on FastMCP framework that enables AI assistants to search academic literature from Europe PMC, retrieve article details, and analyze journal quality with seamless integration into Claude Desktop and Cherry Studio.

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.

Enables semantic search across multiple AI library documentations to keep coding assistants up-to-date.

Provides tools to search, list, and retrieve Python Enhancement Proposals (PEPs) directly from official sources like peps.python.org. It enables users to query active PEP titles and fetch document content or focused excerpts through natural language.

Enables searching, downloading, and reading academic papers from multiple platforms including arXiv, Semantic Scholar, PubMed, bioRxiv, medRxiv, IACR, Google Scholar, RePEc/IDEAS, and Sci-Hub with PDF to Markdown conversion.

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.