Want to try it first? A free, individually-verified sample is available on the Demo Samples page.
What MCP-Atlas is
A bundle for downstream RL training and reproducible evaluation:- Multi-step, multi-server tool-use tasks that fan out across many MCP servers — research-style reads (wikipedia, arxiv, github), stateful writes (airtable, notion, mongodb), and filesystem/git work — in a single goal-directed trajectory.
- Claim-based grading — each task ships 1–7 drift-resistant claims that define success without referencing volatile facts (star counts, prices, rankings), so the reward stays stable over time.
- Frozen environment — every task includes a captured tool-I/O sequence and before/after world snapshots, so external-API calls are served deterministically at replay time with no network drift or cross-task state leakage.
At a glance
What’s inside
Task composition
Tasks mix three classes of server, by how the environment is reconstructed:
The pure-local subset is the strongest reproducibility tier — every call is served by a deterministic local process and can be exercised infinitely without network access.
Trajectory length
Per-task agent-side complexity:
Tasks run roughly 1.7× more tool calls than the upstream benchmark’s gold trajectories — the proposer prefers multi-server plans, and real execution often retries a step the proposer did not anticipate.
Tool usage
filesystem dominates as a sink for writing artifacts; github and wikipedia dominate as research-style sources. Tasks that touch each server (top of ~40):