searchLABSCO SUMMARY
Search with the answer and the sources in one response.
Returning the AI answer and the source list together is the useful shape: the model can lead with the summary and still quote the page it came from. It is a thin wrapper on one vendor, so your search bill and rate limits are Tavily's.
What it is
A small MCP server that hands a query to Tavily and returns the results as text. Each response carries an AI-generated answer alongside the source titles and URIs, so the model gets both a summary and something to cite.
What you get
search— run a query against Tavily and get back the results as text, with the AI response, URIs and titles of the matches- A
search_depthargument set to either basic or advanced, so a quick lookup and a deeper pass are the same tool with a different setting
Requirements
A Tavily API key, supplied as TAVILY_API_KEY. Run from a clone: the documented client entry uses uv --directory <YOUR_PATH> run tavily-search, with PYTHONIOENCODING set to utf-8 to keep non-ASCII results intact. A Docker Compose file is included for hosts where the desktop clients are unavailable.
Setup effort
Build from source
USE CASES
01
Answering from current information
Search mid-conversation for anything that changed after the model's training data, and read the answer with its sources attached.
02
Choosing between a quick check and a deep pass
Use the shallow setting for a fact lookup and the deeper one when the question needs several sources reconciled.
1 tool
Listed in project docs. Your client may show a different list.
BEFORE YOU INSTALL
What to know first
Tools you get
1 tool named in the project's documentation.
Requires
TAVILY_API_KEY in your environment
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
You clone this one yourself.
Server setup
TERMINAL
git clone https://github.com/YeonwooSung/metasearch-mcp.gitMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"tavily-search": {
"command": "uv",
"args": [
"--directory",
"C:\\<YOUR_PATH>\\metasearch-mcp",
"run",
"tavily-search"
],
"env": {
"TAVILY_API_KEY": "<YOUR_TAVILY_API_KEY>",
"PYTHONIOENCODING": "utf-8"
}
}
}
}
