toolathlon_gym MCP Server
eigent-ai/toolathlon_gym
[](https://agentrank-ai.com/tool/eigent-ai--toolathlon_gym/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Toolathlon-Gym for testing AI agents real-world tool-use capabilities across diverse MCP servers.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
eigent-ai/toolathlon_gym is a Python MCP server licensed under Apache-2.0. Toolathlon-Gym for testing AI agents real-world tool-use capabilities across diverse MCP servers.
Ranked #1050 out of 24846 indexed tools.
In the top 5% of all indexed tools.
Ecosystem
Score Breakdown
151 stars → growing community
Last commit 28d ago → recently updated
3/3 issues closed → responsive maintainer
2 contributors → solo project
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# Toolathlon-GYM — 503 multi-tool tasks backed by a local PostgreSQL database, no external APIs required Training and evaluating LLM agents on real-world tool use is hard. Most existing datasets are either too narrow in tool coverage, too small in scale, or depend on live external APIs that change over time. We introduce [Toolathlon-GYM](https://github.com/eigent-ai/toolathlon_gym), a large-scale, self-contained environment with 503 tasks, 25 MCP servers, and a rich mock database. It runs entirely locally, with no external API calls required at running time. Toolathlon-GYM is built on and extends the infrastructure from [Toolathlon](https://github.com/hkust-nlp/Toolathlon) by HKUST-NLP. The task format, evaluation framework, MCP server interfaces, and database schema design all originate from the Toolathlon project. This dataset applies the same format at larger scale, producing a substantially bigger and more diverse task pool for training and evaluation. Each task asks an agent toRead full README on GitHub →
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