Tavily
This Tavily integration for MCP, developed by an unknown author, provides a bridge between AI assistants and the Tavily search API. It enables AI systems to perform web searches and retrieve relevant information through a standardized MCP interface. The implementation uses Python and leverages libraries like FastMCP and Tavily's official Python SDK to handle API interactions and request processing. By abstracting Tavily's search capabilities into MCP-compatible endpoints, it allows AI assistants to easily incorporate web search functionality without directly managing API complexities. This integration is particularly useful for scenarios requiring up-to-date information retrieval, fact-checking, or research tasks, enhancing AI systems with real-time web search capabilities.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- productivity
- Source
- PulseMCP
- Repository
- github.com/mcp2everything/mcp2tavily
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Tavily from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.