Causal Decision Agent

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robustness_report

Read-only

Check how stable a key estimate is under specification changes—dropping or adding controls, trimming, winsorizing, and subsampling—to identify violations and next steps.

Instructions

Run an automated battery of robustness checks.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Key explanatory variable whose estimate stability is assessed.
`detail`	No	Payload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.	agent
`formula`	Yes	Baseline regression formula, e.g. ``"y ~ x1 + x2 + x3"``.
`subsets`	No	Named boolean masks for subsample checks.
`trim_pct`	No	Drop observations beyond this percentile from both tails. E.g. ``0.01`` trims top and bottom 1 %.
`as_handle`	No	If true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
`data_path`	Yes	Absolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
`result_id`	No	Optional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
`cluster_var`	No	Column for clustered SE check.
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`drop_controls`	No	Baseline controls to drop (one-by-one) for sensitivity.
`winsor_levels`	No	Winsorization percentiles, e.g. ``[0.01, 0.05]``.
`extra_controls`	No	Additional controls to add (one-by-one) beyond baseline.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true and openWorldHint=false, so the description does not need to restate safety. However, it adds almost no behavioral context beyond the fact that a 'battery' of checks is run; it does not disclose what checks are performed, whether the operation is heavy, or what aspects of model stability are assessed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler. It briefly states the core purpose without wasting words, though the extreme brevity underscores that it under-specifies behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 14 parameters, 3 required, and an output schema, a description that only says 'Run an automated battery of robustness checks' is too sparse. It does not explain what the report contains, how to interpret results, or how to distinguish this from related tools. The presence of an output schema mitigates return-value ambiguity, but the tool's usage context remains unclear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters are fully documented in the schema. The description adds no parameter-level meaning beyond the schema, which is acceptable per the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Run') and resource ('automated battery of robustness checks'), but is vague about what specific checks are included, and does not differentiate it from siblings like 'sensitivity' or 'robust_synth' which also perform robustness-related tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives such as 'sensitivity', 'robust_synth', or 'sensitivity_dashboard'. There is no mention of preferred scenarios, exclusions, or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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