mcp-local-rag

What is mcp-local-rag?

mcp-local-rag is a RAG-based web search and deep research MCP server that runs entirely locally, requiring no API keys. It integrates with 9+ search backends, performs semantic similarity ranking, and is intended for users who need private, local web research via an MCP-compatible client.

How to use mcp-local-rag?

Install uv or Docker, then add the server configuration to your MCP client settings. For uvx, use the command uvx --python=3.10 --from git+https://github.com/nkapila6/mcp-local-rag mcp-local-rag. For Docker, run docker run --rm -i --init -e DOCKER_CONTAINER=true ghcr.io/nkapila6/mcp-local-rag:v1.0.2. The server exposes tools like deep_research, deep_research_google, deep_research_ddgs, rag_search_ddgs, and rag_search_google.

Key features of mcp-local-rag

• Multi-engine deep research across 9+ search backends.
• Semantic similarity ranking using embeddings from Google's MediaPipe Text Embedder.
• No API keys required – all processing runs locally.
• Privacy-focused engines (DuckDuckGo, Brave) are supported.
• Customizable result limits and backend selection per query.
• Quick single searches via rag_search_ddgs and rag_search_google.

Use cases of mcp-local-rag

• Comprehensive multi-perspective research on complex topics.
• Technical deep dives using Google's search index for documentation.
• Privacy-aware web searches without tracking or external services.
• Factual verification by cross-referencing Wikipedia and other authoritative sources.
• Quick web lookups integrated into chat workflows (e.g., Claude Desktop).

FAQ from mcp-local-rag

What does mcp-local-rag do?

It performs web searches and deep research using multiple search engines, extracts content from result pages, converts it to Markdown, and returns context to the language model for response generation.

Does mcp-local-rag require any API keys?

No. All processing, including search and embedding generation, runs entirely locally with built-in models.

How can I install mcp-local-rag?

You can run it directly via uvx (requires uv) or using Docker (recommended). Both methods are documented with exact configuration JSON for your MCP client.

What search engines are supported?

DuckDuckGo, Google, Bing, Brave, Wikipedia, Yahoo, Yandex, Mojeek, and Grokipedia.

What are Agent Skills and how do I use them?

Agent Skills are instructional folders that teach Claude how to best use mcp-local-rag’s tools. They cover smart tool selection, multi‑engine research, query formulation, and privacy‑aware searching. Load the skill folder (skills/local-rag-search/) in Claude Desktop’s Skills settings.