"A Python library for visualizing neural networks" matching MCP servers:

This MCP server enables AI agents to search and retrieve exact, cited passages from a large corpus of public-domain books, including full-text search, book metadata, chapters, quotes, and 'ask book' Q&A. Payments are handled via x402 micropayments on Base.

Enables locally-first literature research through an MCP host by collecting sources, parsing documents, retrieving evidence, validating citations, and exporting research records in multiple formats.

Provides a 'library keeper' sub-agent that ingests PDF/EPUB books and answers questions grounded in them via local embedding search, with deterministic classification and knowledge gap logging.

Enables a persistent local library for mathematical PDFs with cached LaTeX extraction, full-text search in English and Greek, and page/image retrieval over MCP.

Turns a user's own Markdown wiki into a locally indexed knowledge base, enabling semantic search, per-page retrieval, source listing, and citation-backed RAG question answering. All embedding and generation run through a local Ollama instance, so queries and documents never leave the machine.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

Local MCP server that provides verified answers about Jimmer and internal libraries using a SQLite FTS5 knowledge base.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables users to search and retrieve research datasets via natural language queries, with tools for proposing and committing tag changes. Includes robust authorization, idempotent mutations, and an audit trail.

An enterprise-grade AI processing platform that provides advanced code analysis, automated documentation generation, security scanning, and knowledge extraction capabilities through a scalable MCP architecture. Features iterative neural processing, support for 100+ programming languages, and real-time streaming updates.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A proof of concept MCP server for Apache OFBiz

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

Enables AI agents to create, search, and manage subject matter expert profiles, including private workspace organization and moderated sharing to a common library.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.