mcpadapt — MCP Server by grll

by grll · MCP Server · ★ 417

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About mcpadapt

MCPAdapt For more context, guides and API references visit our new documentation. Unlock 650+ MCP servers tools in your favorite agentic framework. Model Context Protocol is an open-source protocol introduced by Anthropic which allows anyone to simply and quickly make tools and resources available as "MCP Servers". Since its release more than 650 MCP servers have been created giving access to many data & tools to supported "MCP Client". This project makes calling any MCP servers tools seemless from any agentic framework. Virtually providing your agentic worfklow access to 650+ MCP servers tools. Look at glama.ai or smithery.ai to give you an idea of what your agent could access. Installation Instructions Smolagents Smolagents 1.4.1 and above directly ships with mcpadapt integrated in their tool collections object. It means yo

langchainllamaindexmodelcontextprotocolpydantic-aismolagentsswarm

Quick Facts

Stars417
Forks47
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score42.7/100
Open Issues23
Last Updated2025-10-24
Created2025-01-11
Platformsmcp, python
Est. Tokens~363k

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Frequently Asked Questions

What is mcpadapt?

mcpadapt is Unlock 650+ MCP servers tools in your favorite agentic framework.. It is categorized as a MCP Server with 417 GitHub stars.

What programming language is mcpadapt written in?

mcpadapt is primarily written in Python. It covers topics such as langchain, llamaindex, modelcontextprotocol.

How do I install or use mcpadapt?

You can find installation instructions and usage details in the mcpadapt GitHub repository at github.com/grll/mcpadapt. The project has 417 stars and 47 forks, indicating an active community.

What license does mcpadapt use?

mcpadapt is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to mcpadapt?

The top alternatives to mcpadapt on Agent Skills Hub include openinference, full-stack-ai-agent-template, palico-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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