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MCP-Atlas is a corpus of multi-step, multi-server tool-use tasks generated by EigenData-CLI to improve model performance on the MCP-Atlas benchmark. Each task spans a ~40-server MCP graph (github, wikipedia, arxiv, filesystem, slack, airtable, notion, mongodb, twelvedata, …), is really executed against a live MCP environment, then frozen so it can be replayed deterministically. Each task pairs a natural-language request with (a) a claim-based reward rubric, (b) a frozen snapshot of every tool I/O it consumed, and (c) restorable environment snapshots, so the same sample trains an SFT model and seeds a reproducible RL rollout.
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):

Access & licensing

A free demo sample is available now under CC BY-NC-ND 4.0 — see Demo Samples. The full corpus is available for commercial licensing; contact support@eigenai.com.