> ## Documentation Index > Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt > Use this file to discover all available pages before exploring further. # Full Dataset > The full MCP-Atlas corpus — multi-step, multi-server tool-use tasks over a ~40-server MCP graph, each frozen with a claims-based reward and a replayable environment snapshot. Built on the MCP-Atlas benchmark; data generated and verified by EigenData-CLI. **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](https://huggingface.co/datasets/ScaleAI/MCP-Atlas)**. 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](/products/eigendata-cli/datasets/mcp-atlas/demo) 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 | Property | Value | | --------------------------- | -------------------------------------------------------------------------------------------------------------------------- | | **Tasks** | 1,745 | | **Frozen tool-I/O entries** | 29,245 | | **Drift-resistant claims** | 5,040 (\~3 per task) | | **Servers in graph** | \~40 (mirroring the benchmark's 36 servers / 220 tools, plus fixtures and stubs) | | **Per task** | `task.json` + `claims.json` + `trajectory.json` (SFT) + `env.jsonl` (RL replay) + restorable `env/workspace_{init,final}/` | | **Grading** | per-claim LLM-as-judge; ship gate **coverage ≥ 0.75** (matches the MCP-Atlas rubric) | | **Source** | grounded proposer + **real tool execution**; tool data is **real frozen API responses**, not generated | ## What's inside | Component | Description | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Task bundles** | One self-contained folder per task, keyed by a 16-hex `task_id`. Each is simultaneously an SFT trajectory and a replayable RL environment. | | **Reference trajectories** | The full `system` / `user` / `assistant` (with reasoning + tool calls) / `tool` conversation (`trajectory.json`), suitable for SFT distillation. | | **Tool-I/O snapshots** | Frozen `(tool, arguments, response)` triples in original execution order (`env.jsonl`) — the reference I/O used to replay external-API calls deterministically. | | **Claim rubrics** | Drift-resistant, independently-verifiable assertions (`claims.json`) with `verify_via` metadata (`exact_match` / `substring` / `count` / `presence`) and a per-claim verdict. | | **Restorable world** | `env/workspace_init/` (before the agent acted) and `env/workspace_final/` (the realized goal state) — `init` to start an RL rollout, `final` as the golden end state for write tasks. | ## Task composition Tasks mix three classes of server, by how the environment is reconstructed: | Composition | Tasks | Share | | --------------------------------------------------------------- | ----: | --------: | | Mixed (live + local) | 1,604 | **91.9%** | | Pure local (no live API; fully reproducible without cache hits) | 117 | **6.7%** | | Pure live (no fixture/sandbox) | 24 | **1.4%** | 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: | Metric | mean | median | p90 | max | | ------------------------- | ---: | -----: | --: | --: | | Assistant turns (steps) | 12.1 | 11 | 21 | 30 | | Tool calls per task | 16.8 | 16 | 28 | 78 | | Distinct servers per task | 4.0 | 4 | 5 | 9 | | Claims per task | 2.9 | 3 | 5 | 7 | 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): | Server | Tasks | Tool calls | | ---------------- | ----: | ---------: | | filesystem | 1,590 | 4,297 | | github | 785 | 5,679 | | wikipedia | 735 | 3,963 | | twelvedata | 534 | 2,150 | | slack | 524 | 1,275 | | arxiv | 500 | 3,409 | | airtable | 429 | 2,311 | | google-workspace | 414 | 1,386 | | brave-search | 310 | 1,006 | ## Access & licensing A free demo sample is available now under [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) — see [Demo Samples](/products/eigendata-cli/datasets/mcp-atlas/demo). The full corpus is available for **commercial licensing**; contact [support@eigenai.com](mailto:support@eigenai.com).