"Ram" matching MCP servers:

Enables AI agents to search and retrieve jam-nodes documentation, including node info, API references, and guides, via MCP tools without loading everything into context.

Enables AI assistants to access Ramp's developer documentation and API schemas through natural language queries. Provides contextual answers about API endpoints, authentication methods, data relationships, and implementation patterns for developers building on Ramp's platform.

Enables coding agents to retrieve relevant sections from a repository's declared documentation scope using hybrid vector and full-text search, with incremental indexing.

Enables hybrid search and agentic retrieval over Confluence pages via MCP tools, allowing Claude Desktop, Cursor, or Claude Code to search and retrieve Confluence content.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

Enables hybrid retrieval over interpretable Gemini documentation knowledge graphs through eight MCP tools, combining vector, keyword, and graph-boosted search with deprecation filtering.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.

A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.

Enables querying context about the MAM product components and architecture through MCP, allowing agents to retrieve targeted product information on demand instead of loading full documentation.

Enables cited, revision-aware retrieval over real IETF RFC chunks with zero LLM in the answer path, including graph-based supersession resolution and relationship queries via MCP tools.

MCP server for RAG-based search over 1C Enterprise configuration documentation, enabling natural language queries to find objects like справочники, документы, and отчеты.

Enables AI editors to search past Zenn articles and book chapters, get article contents, list topics, receive related-link suggestions, and sync the vector index directly through MCP tools.

Enables MCP clients to semantically search a local folder of markdown documents via a single search_notes tool, with embeddings generated locally by Ollama and support for excluding superseded or archived material.

Enables local retrieval-augmented generation document search for Cline by providing semantic search over indexed files, listing indexed sources, and reporting index status through MCP tools.

MCP server exposing a RAG knowledge base as read-only tools (search_knowledge_base, ask_knowledge_base, kb_stats, kb_diagnostic) for AI clients like Claude Desktop and Cursor, enabling token-efficient document retrieval and Q&A.

A document knowledge base system that enables users to upload PDFs and query them semantically through a web interface or via the Model Context Protocol, allowing integration with AI tools like Cursor.

Provides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.

An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.

Exposes the Ramoira brand schema specification as MCP tools and resources, enabling LLMs to generate, validate, or reason about brand schemas without the CLI.

Enables MCP hosts to index local PDFs and ask grounded questions over them, retrieving semantically relevant passages with optional reranking and source-attributed, citation-backed answers.