"A list of all MCP items" matching MCP servers:

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

Enables read-only consultation of the latest published content on AI applied to medicine—articles, news, podcasts, lessons, events, conferences and repositories—through tools to list recent items, search by topic, and open item summaries with links.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Enables screenwriters to search for similar scenes, fetch scene details, list indexed movies, find mood reference scenes, and get analysis style guidance, all grounded in real produced scripts.

Enables AI assistants to search a library of over 20,000 open legal skills by keyword, by meaning, or a hybrid of both, filtering by jurisdiction, language, practice area, or tag. It also returns facet counts for the collection and retrieves the full text of any selected skill.

160 tools that do that heavy lifting - storage, agent-to-agent collaboration, document conversions, identity verification -- almost all priced under a tenth of a cent or less per call.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

Enables AI agents to search a local folder of Markdown, AsciiDoc, and text files through a SQLite FTS5 keyword index, returning the most relevant sections with file paths, line ranges, and heading breadcrumbs instead of grepping through a repo. It also exposes tools to list indexed documents and refresh the index incrementally, all offline with no dependencies, network calls, or embeddings.

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Enables code agents to persist and recall durable project knowledge — rules, decisions, procedures, learnings and secrets — across sessions at low context cost, with accent-insensitive PT-BR search, scoped per repository/workspace, and secrets that stay out of the model's context.

Enables local semantic search and management of OKF knowledge bundles via MCP tools, with hybrid BM25 and vector search.

Read-only MCP server exposing a W3C PROV knowledge graph of verified facts with provenance, enabling AI agents to list, search, and check facts while enforcing that writes remain CLI-only.

heroicons-mcp is a Model Context Protocol (MCP) server that exposes Heroicons. It allows LLMs and agentic applications to search, list, and generate usage examples of icons in React applications.

Enables AI and LLM systems to interact with the Buttondown newsletter service, supporting email drafting, scheduling, analytics retrieval, and list management through a Model Context Protocol interface.

Enables querying a compressed knowledge graph of the NVIDIA AI developer stack, allowing users to traverse declared dependencies, prerequisites, and relationships across 20 domains via read-only MCP tools like query_ckg, get_prerequisites, and search_concepts.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.