Tavily MCP Server

Created By
NoeSamaillea year ago
Basic remote SSE MCP server for Tavily Search.
Overview

What is Tavily MCP Server?

Tavily MCP Server is a FastAPI-based server application designed to provide endpoints for interacting with the Tavily API, enabling users to perform searches and extract content from URLs.

How to use Tavily MCP Server?

To use Tavily MCP Server, install the dependencies using pip install -r requirements.txt, set the required API keys as environment variables, and run the server with uvicorn server:app --host 0.0.0.0 --port 8000 --reload. The server will be accessible at http://127.0.0.1:8000.

Key features of Tavily MCP Server?

  • Provides endpoints for searching and extracting content using the Tavily API.
  • Requires API key authentication for secure access.
  • Supports customizable search parameters including depth, topic, and time range.

Use cases of Tavily MCP Server?

  1. Performing advanced searches on the Tavily API for specific topics.
  2. Extracting content from multiple URLs for data analysis.
  3. Integrating with other applications to enhance data retrieval capabilities.

FAQ from Tavily MCP Server?

  • What API keys are required to use the server?

The server requires two API keys: API_KEY for server access and TAVILY_API_KEY for Tavily API access.

  • How do I run the server?

You can run the server using the command uvicorn server:app --host 0.0.0.0 --port 8000 --reload after setting the API keys.

  • What endpoints are available?

The server provides /tavily-search for searching and /tavily-extract for extracting content from URLs.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
NoeSamaille
Star
0
Language
Python
License
MIT license
Tags

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago