"The most widely used models" matching MCP servers:

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Index docs websites and local folders, then let Claude, Cursor, or any MCP client answer from the current docs, with a link to every section it used.

Shrink the web for your local LLMs! Provides web research capabilities to low resource models and environments.

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.

Enables agents to run hybrid dense and BM25 search over a local folder of Markdown files, read and write notes, and trigger reindexing as the folder changes. It also injects the most relevant sections into each prompt automatically and runs entirely locally with a bundled embedding model.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

A local-first personal archive with a read-only MCP gateway that preserves files and AI conversations, derives evidence-linked memory candidates using local models, and enables bounded context retrieval for questions.

Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.

Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.

MCP Ollama server integrates Ollama models with MCP clients, allowing users to list models, get detailed information, and interact with them through questions.

An MCP server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question for more comprehensive answers.

This is a Model Context Protocol (MCP) server adaptation of LangChain Ollama Deep Researcher. It provides the deep research capabilities as MCP tools that can be used within the model context protocol ecosystem, allowing AI assistants to perform in-depth research on topics (locally) via Ollama

An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.

Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.

This is an MCP server that provides access to the Alpha Vantage API, allowing stock data retrieval to be used as context to LLMs.

Enables integration with OpenAI models through the MCP protocol, supporting concise and detailed responses for use with Claude Desktop.

A Model Context Protocol implementation that enables large language models to call external tools (like weather forecasts and GitHub information) through a structured protocol, with visualization of the model's reasoning process.

A Python implementation of the Model Context Protocol (MCP) that connects client applications with AI models, primarily Anthropic's models, with setup instructions for local development and deployment.

The APISIX Model Context Protocol (MCP) server bridges large language models (LLMs) with the APISIX Admin API.

A Model Context Protocol server that enables LLMs to perform Google searches via the Serper API, allowing models to retrieve current information from the web.

An experimental MCP gateway that provides specialized LLM enhancement prompts based on the L1B3RT4S repository, primarily intended to enhance weaker models' capabilities.

A Model Context Protocol server that enables web search capabilities using the Tavily API, allowing AI models to retrieve current information from the internet through natural language commands.

Enables users to search and fetch documentation from supported libraries (langchain, llama-index, openai, uv) as clean, readable text, which can be used as context for answering questions.

Persistent memory and handoff intelligence layer for MCP agents. Most memory servers retrieve text — Memory Nexus compounds operational context, learning from usage and progressively synthesizing observations into higher-order intelligence across sessions and tools.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Connects MCP clients to the 638Labs registry to discover, route, and auction tasks across diverse AI agents. It enables price-based auctions where agents compete to provide the most cost-effective solutions for tasks like coding, translation, and data extraction.

A proof of concept MCP server for Apache OFBiz

Enables AI assistants to perform intelligent web searches using the Baidu Wenxin API, supporting multiple models, search modes, and providing search results with reference sources.

Stores metadata for MCP servers and provides smart search capabilities, allowing users to find appropriate MCP servers for their queries and route requests to the most suitable server.

MCP server for ByteDance Volcano Engine enabling text generation, multi-turn chat, and embeddings via Doubao LLM models through the Volcano Ark API.