"Cargo Doc" matching MCP servers:

Provides a documentation search MCP tool (search_docs) that enables AI agents to retrieve relevant chunks from BDC documentation using semantic embeddings and keyword search, with filtering by document type and date.

Enables searching and retrieving documents from a local folder to ground LLM answers in your files.

A Model Context Protocol (MCP) server that gives AI assistants direct access to live AVEVA PI System documentation, eliminating hallucination on PI-specific topics.

MCP server that gives AI assistants direct access to live AVEVA PI System documentation, eliminating hallucination on PI-specific topics by fetching real answers from the official docs.

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

Enables LLMs to dynamically search, scrape, and query official documentation of libraries like uv, OpenAI, LangChain, and LlamaIndex via Google Serper and Groq.

Provides AI assistants with intelligent access to project documentation and API references through smart search, contextual rules, and Docset integration. Enables AI to understand project-specific conventions, patterns, and official framework documentation without token limits.

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

Enables searching Word documents on remote FTP/FTPS servers by keyword, returning matching files with context snippets.

Enables interactive chat with AI models and document retrieval via the Anthropic API, supporting command-based prompts and MCP tool integrations.

An MCP server for the Delovye Linii logistics API, enabling cargo delivery cost calculation, order creation, tracking, and directory lookups for cities and terminals.

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

Enables searching and retrieving content from Lark (Feishu) documents via MCP tools, supporting Doc and Wiki types with OAuth authentication.

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

Turns any folder of PDFs, markdown, and text files into a local, queryable knowledge base exposed as an MCP server. Enables MCP-compatible agents to semantically search indexed documents, retrieve relevant passages with source and relevance scores, list or reindex documents, and inspect cache and token usage — instead of reading whole files into context.

A local-first semantic search server for documents, supporting PDFs, Office files, and text/markdown, enabling natural language search via the Model Context Protocol (MCP).

A document-based MCP server that supports keyword searching and content retrieval from official website documentation.

Enables semantic search and AI-powered Q&A over ingested GitHub documentation repositories via MCP tools.

Enables MCP-based access to Adhoc documentation, providing search, content retrieval, and feedback issue creation with audience gating and leak protection.

Local documentation search server for AI models using hybrid retrieval (phrase, keyword, vector). Provides MCP tools to search and fetch documentation from bundled or custom doc sets without any external API keys.