mcp-local-rag
Local RAG server for web search and context injection using Model Context Protocol.
Key Features
Use Cases
README
mcp-local-rag
"primitive" RAG-like web search model context protocol (MCP) server that runs locally. ✨ no APIs ✨
%%{init: {'theme': 'base'}}%%
flowchart TD
A[User] -->|1.Submits LLM Query| B[Language Model]
B -->|2.Sends Query| C[mcp-local-rag Tool]
subgraph mcp-local-rag Processing
C -->|Search DuckDuckGo| D[Fetch 10 search results]
D -->|Fetch Embeddings| E[Embeddings from Google's MediaPipe Text Embedder]
E -->|Compute Similarity| F[Rank Entries Against Query]
F -->|Select top k results| G[Context Extraction from URL]
end
G -->|Returns Markdown from HTML content| B
B -->|3.Generated response with context| H[Final LLM Output]
H -->|5.Present result to user| A
classDef default stroke:#333,stroke-width:2px;
classDef process stroke:#333,stroke-width:2px;
classDef input stroke:#333,stroke-width:2px;
classDef output stroke:#333,stroke-width:2px;
class A input;
class B,C process;
class G output;
Installation
Locate your MCP config path here or check your MCP client settings.
Run Directly via uvx
This is the easiest and quickest method. You need to install uv for this to work. Add this to your MCP server configuration:
{
"mcpServers": {
"mcp-local-rag":{
"command": "uvx",
"args": [
"--python=3.10",
"--from",
"git+https://github.com/nkapila6/mcp-local-rag",
"mcp-local-rag"
]
}
}
}
Using Docker (recommended)
Ensure you have Docker installed. Add this to your MCP server configuration:
{
"mcpServers": {
"mcp-local-rag": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"--init",
"-e",
"DOCKER_CONTAINER=true",
"ghcr.io/nkapila6/mcp-local-rag:latest"
]
}
}
}
Security audits
MseeP does security audits on every MCP server, you can see the security audit of this MCP server by clicking here.
MCP Clients
The MCP server should work with any MCP client that supports tool calling. Has been tested on the below clients.
- Claude Desktop
- Cursor
- Goose
- Others? You try!
Examples on Claude Desktop
When an LLM (like Claude) is asked a question requiring recent web information, it will trigger mcp-local-rag.
When asked to fetch/lookup/search the web, the model prompts you to use MCP server for the chat.
In the example, have asked it about Google's latest Gemma models released yesterday. This is new info that Claude is not aware about.
Result
mcp-local-rag performs a live web search, extracts context, and sends it back to the model—giving it fresh knowledge:
Buy Me A Coffee
If the software I've built has been helpful to you. Please do buy me a coffee, would really appreciate it! 😄
Contributing
Have ideas or want to improve this project? Issues and pull requests are welcome!
License
This project is licensed under the MIT License.
Star History
Repository Owner
User
Repository Details
Programming Languages
Tags
Topics
Join Our Newsletter
Stay updated with the latest AI tools, news, and offers by subscribing to our weekly newsletter.
Related MCPs
Discover similar Model Context Protocol servers
Driflyte MCP Server
Bridging AI assistants with deep, topic-aware knowledge from web and code sources.
Driflyte MCP Server acts as a bridge between AI-powered assistants and diverse, topic-aware content sources by exposing a Model Context Protocol (MCP) server. It enables retrieval-augmented generation workflows by crawling, indexing, and serving topic-specific documents from web pages and GitHub repositories. The system is extensible, with planned support for additional knowledge sources, and is designed for easy integration with popular AI tools such as ChatGPT, Claude, and VS Code.
- ⭐ 9
- MCP
- serkan-ozal/driflyte-mcp-server
MCP-searxng
MCP server bridging agentic systems with SearXNG web search
MCP-searxng enables agentic systems to interface with web search engines via the SearXNG platform by implementing the Model Context Protocol. It supports both command-line and local server deployment, providing flexible integration options. Users can configure custom SearXNG server URLs and connect through clients like uvx or claude desktop. The tool simplifies access to structured web search within agentic workflows.
- ⭐ 107
- MCP
- SecretiveShell/MCP-searxng
tavily-search MCP server
A search server that integrates Tavily API with Model Context Protocol tools.
tavily-search MCP server provides an MCP-compliant server to perform search queries using the Tavily API. It returns search results in text format, including AI responses, URLs, and result titles. The server is designed for easy integration with clients like Claude Desktop or Cursor and supports both local and Docker-based deployment. It facilitates AI workflows by offering search functionality as part of a standardized protocol interface.
- ⭐ 44
- MCP
- Tomatio13/mcp-server-tavily
AgentQL MCP Server
MCP-compliant server for structured web data extraction using AgentQL.
AgentQL MCP Server acts as a Model Context Protocol (MCP) server that leverages AgentQL's data extraction capabilities to fetch structured information from web pages. It allows integration with applications supporting MCP, such as Claude Desktop, VS Code, and Cursor, by providing an accessible interface for extracting structured data based on user-defined prompts. With configurable API key support and streamlined installation, it simplifies the process of connecting web data extraction workflows to AI tools.
- ⭐ 120
- MCP
- tinyfish-io/agentql-mcp
RAE Model Context Protocol (MCP) Server
An MCP server enabling LLMs to access RAE’s dictionary and linguistic resources.
Provides a Model Context Protocol (MCP) server implementation for the Royal Spanish Academy API, facilitating integration with language models. Offers tools such as search and word information retrieval, exposing RAE’s dictionary and linguistic data to LLMs. Supports multiple transports including stdio and SSE, making it suitable for both direct and server-based LLM interactions.
- ⭐ 3
- MCP
- rae-api-com/rae-mcp
Scrapeless MCP Server
A real-time web integration layer for LLMs and AI agents built on the open MCP standard.
Scrapeless MCP Server is a powerful integration layer enabling large language models, AI agents, and applications to interact with the web in real time. Built on the open Model Context Protocol, it facilitates seamless connections between models like ChatGPT, Claude, and tools such as Cursor to external web capabilities, including Google services, browser automation, and advanced data extraction. The system supports multiple transport modes and is designed to provide dynamic, real-world context to AI workflows. Robust scraping, dynamic content handling, and flexible export formats are core parts of the feature set.
- ⭐ 57
- MCP
- scrapeless-ai/scrapeless-mcp-server
Didn't find tool you were looking for?