AI Agent for mcp-local-rag
Integrate mcp-local-rag, a local Retrieval-Augmented Generation (RAG) tool, seamlessly with your workflows. Enable your AI models to perform live web searches, extract and embed fresh contextual information, and respond with up-to-date knowledge—all without relying on external APIs. Boost accuracy, privacy, and control for your AI-powered applications with this lightweight, open-source MCP server.
Real-Time Local Web Search AI
Empower your Large Language Models (LLMs) with real-time, privacy-focused web search through mcp-local-rag. This integration allows AI to fetch, embed, and contextualize up-to-date online information—locally and securely. No third-party APIs needed.
- Live Web Search.
- Fetches up-to-the-minute information directly from the web using DuckDuckGo—no API keys required.
- Privacy First.
- Runs entirely locally, ensuring sensitive queries and data never leave your environment.
- Contextual Embedding.
- Uses Google's MediaPipe Text Embedder to vectorize and rank search results for highly relevant context.
- Seamless LLM Integration.
- Works out-of-the-box with top MCP clients like Claude Desktop, Cursor, and Goose for effortless toolcalling.
Flexible, Secure Deployment
Deploy mcp-local-rag your way—run directly via Python or in a Docker container for maximum compatibility and security. Automated security audits ensure you stay compliant and protected.
- Docker Support.
- Deploy with a single command using Docker for rapid, isolated, and repeatable setups.
- Regular Security Audits.
- Verified by MseeP with up-to-date public audit reports for peace of mind.
- Easy Configuration.
- Simple integration with your MCP server config—no complex setup required.
Open Source, Community-Driven
Built under the MIT License, mcp-local-rag is open to contributions and improvements from AI practitioners worldwide. Join a growing community focused on privacy, transparency, and innovation.
- Community Support.
- Issues and pull requests are welcome—drive new features and improvements together.
- MIT Licensed.
- Open-source foundation with flexible, business-friendly licensing.
MCP INTEGRATION
Available mcp-local-rag MCP Integration Tools
The following tools are available as part of the mcp-local-rag MCP integration:
- search_web
Search the web in real time and retrieve relevant information and context for your queries using DuckDuckGo and content extraction.
Run a Private, Real-Time Web Search RAG Locally
Try mcp-local-rag: a lightweight, API-free Retrieval Augmented Generation (RAG) server that brings fresh web context to your LLM, all from your own machine. Search, fetch, and embed live data—no external APIs required.
What is mcp-local-rag
mcp-local-rag is an open-source, local server implementation of a Retrieval-Augmented Generation (RAG) system designed for use with Model Context Protocol (MCP) clients and language models. The project acts as a 'primitive' RAG-like web search model context protocol server that runs entirely on your own machine—no APIs or external cloud services are needed. It enables language models to perform live web searches, fetch real-time information, and supply up-to-date context for LLM queries directly from the internet. The system works by searching the web via DuckDuckGo, extracting relevant content, generating embeddings using Google's MediaPipe Text Embedder, and ranking the most relevant results, which are then returned as markdown content for language models to process. This tool is particularly useful for users who prioritize privacy, want full control over their data, or need up-to-date information integrated into their AI workflows.
Capabilities
What we can do with mcp-local-rag
mcp-local-rag enables powerful, real-time data retrieval and context augmentation for AI models without relying on third-party APIs. Users can search the latest web content, extract and rank relevant results, and provide language models with information that is both current and contextually aware, all from a locally hosted server. The service integrates seamlessly with popular MCP clients such as Claude Desktop, Cursor, and Goose, making it easy to add on-demand web search capabilities to your AI agent workflows.
- Live web search
- Perform real-time searches on the internet for up-to-date information directly from your LLM queries.
- Local privacy
- All search and retrieval operations occur locally, ensuring full data privacy and no leaks to third-party APIs.
- Context extraction
- Extracts relevant markdown content from web pages to enrich AI-generated responses.
- Embeddings & ranking
- Uses MediaPipe Text Embedder to create semantic embeddings and rank search results for relevance.
- Seamless integration
- Works with any MCP client that supports tool calling, such as Claude Desktop and Cursor.
What is mcp-local-rag
AI agents benefit greatly from mcp-local-rag by gaining the capability to search the web and retrieve the most recent and relevant information, even when their internal models are out-of-date. This empowers agents to answer questions about breaking news, newly published research, or other time-sensitive topics, all while maintaining user privacy and operating without dependency on cloud APIs.