Causal Decision Agent

by vikranthviki

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Python

Local

kitagawa_test

Read-only

Checks the validity of local average treatment effect (LATE) assumptions using the Kitagawa specification test.

Instructions

Kitagawa (2015) specification test for the validity of LATE.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable name.
`seed`	No	Random seed for reproducibility.
`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
`n_boot`	No	Number of bootstrap replications for the p-value.
`n_grid`	No	Number of grid points for evaluating the CDF conditions.
`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.
`treatment`	Yes	Endogenous binary treatment variable (D).
`instrument`	Yes	Binary instrument variable (Z).
`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.

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

C2.9/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, and the description adds no behavioral detail: it does not state what null hypothesis is tested, what assumptions are checked, or what kind of result is produced. It does not contradict the annotations, but it also does not add meaningful behavioral context beyond them.

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

Conciseness4/5

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

The description is a single sentence with no filler and front-loads the citation and core construct. It is concise, though arguably too sparse to be considered fully well-rounded, which prevents a 5.

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?

For a tool with 12 parameters and many sibling econometric tests, a one-line description leaves the agent to infer the test's assumptions, interpretation, and place among alternatives. The existence of an output schema does not compensate for missing usage context.

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 input schema already documents all 12 parameters in detail. The tool description adds no parameter-level semantics, but the schema carries the burden effectively, making a baseline 3 appropriate.

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

Purpose4/5

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

The description names a specific test ('Kitagawa (2015) specification test') and states its object ('validity of LATE'), giving an agent a concrete semantic anchor. However, it does not explain what 'validity of LATE' entails or differentiate it from nearby tools such as kitagawa_decompose, so it falls just short of a 5.

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?

There is no guidance on when to use this test versus other LATE/IV specification tests, no exclusions, and no mention of prerequisite conditions. The description merely identifies the test without contextualizing its use.

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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