Causal Decision Agent

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forest_diagnostics

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Check overlap and CATE distributions for a fitted causal forest to validate unconfoundedness and identify calibration failures, guiding next steps.

Instructions

Return overlap and CATE-distribution diagnostics for a fitted forest. Assumptions: Unconfoundedness given the covariates; Overlap / positivity; Honesty: separate subsamples are used to choose splits and to estimate effects. Pre-conditions: Covariates, treatment, and outcome with enough data to grow an honest forest. Failure modes: Calibration test rejects -- the forest's heterogeneity is not well calibrated -> Increase the sample / number of trees, or fall back to a doubly-robust learner. Alternatives: sp.dml, sp.auto_cate, sp.tmle. Typical minimum N: 1000.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`T`	No	T parameter (Optional[np.ndarray]).
`X`	No	Feature matrix or covariate DataFrame.
`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
`forest`	Yes	forest parameter ('CausalForest').
`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`	No	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.
`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.
`propensity_bounds`	No	propensity_bounds parameter (Tuple[float, float]).

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

A4.7/5.0

Behavior5/5

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

Annotations already provide readOnlyHint=true and openWorldHint=false, establishing the safety profile. The description goes further by disclosing behavioral assumptions, failure modes, and corrective actions, which is valuable context beyond what annotations supply. No contradiction with annotations.

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 organized into labeled sections (Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N) that front-load the core purpose and then add actionable context. Every sentence earns its place with no redundancy.

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

Completeness5/5

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

For a tool with 10 parameters and an output schema, the description covers purpose, assumptions, preconditions, failure modes, remediation, alternatives, and minimum sample size. Return-value details are handled by the output schema, so nothing an agent needs to select and invoke this tool correctly is missing.

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 all 10 parameters are documented in the schema. The description does not add parameter-specific meaning beyond naming the fitted-forest resource, but the schema already carries the burden. Baseline 3 is appropriate.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Return overlap and CATE-distribution diagnostics for a fitted forest.' It clearly distinguishes itself from related causal-forest tools by naming alternatives (sp.dml, sp.auto_cate, sp.tmle), which helps an agent route correctly without inspecting schemas.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance: assumptions (unconfoundedness, overlap, honesty), pre-conditions (enough data), failure modes with remediation steps (calibration test rejection -> increase sample/trees or fall back to doubly-robust learner), and a typical minimum N of 1000. It also names alternatives, so the agent knows which tools to consider instead.

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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