Aurora

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/fantasylab-ai-aurora

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What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

4 read · 0 write · 0 delete · 3 not yet classified

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Python package

Tools (7)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• aurora_analyze

  Statistical analysis of a dataset (CSV, TSV, Parquet, XLSX): runs Aurora's battery of 19 research-grade methods on-device — anomaly detection (isolation forest + robust-z), change-point detection, trend and seasonality, correlation screening with FDR control, forecasting, causal system-model discovery, and more. Returns CITED findings (each carries its method, threshold, and claim_id), an overall confidence, and a fabricated_count that is contractually zero: every number is computed from the data, never generated. Changepoint findings carry a calibration block: the empirically measured false-f…

• aurora_explain

  Full evidence for ONE finding by claim_id: the computed values behind the claim plus the method's registry spec — assumptions, parameters, and references. Use whenever you are about to cite, verify, or defend a specific statistical claim; this is the receipt, not a summary.

• aurora_findings

  List the verified findings from an Aurora run or bundle: each one carries severity (crit/warn/info), the exact statistical method and threshold that produced it, a plain-language citation, and a claim_id for evidence drill-down via aurora_explain. Filter by severity or method. Use after aurora_analyze to enumerate what was actually found — quote findings from here instead of paraphrasing from memory.

• aurora_forecast

  Model-based forecast for the run's target column, fitted and validated on the actual data with the method disclosed. Returns point predictions with an honest horizon, or just the peak within horizon_hours (return_peak=true). Use for any 'what will X be / when does it peak' question instead of extrapolating by eye.

• aurora_intervene

  What-if intervention: perturb one variable in the data's discovered system model and propagate the shock through validated relationships (up to max_depth hops). Returns per-node deltas WITH confidence intervals. Use for 'what happens to Y if X changes by Δ' questions — answers come from the data's own causal graph, not from priors.

• aurora_load_bundle

  Load a portable .aurora.json analysis bundle and verify its SHA-256 integrity hash (and Ed25519 signature when present) BEFORE trusting its findings. Use when someone shares an Aurora bundle and you need proof it is untampered. Returns run identity, confidence, fabricated_count, and findings-by-severity counts.

• aurora_simulate

  Simulate the system forward n_steps using dynamics fitted and validated on the data — and it PAUSES honestly when confidence intervals grow too wide to keep going, rather than extrapolating noise. Use for trajectory questions ('where is this heading') on a completed run; pass target_entity_id to simulate a specific node.

Signals

Stars

6

Forks

0

Open issues

0

Weekly downloads

50

Last commit

Aug 31, 2026 (2 months ago)

Last release

Aug 16, 2026 (2 months ago)

Last package publish

Aug 31, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.FantasyLab-ai/aurora

Version

0.10.0

Auth

No auth

Packages

pypi aurora-mcp

License

Apache-2.0

Repository

github.com/FantasyLab-ai/aurora

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql