Causal Decision Agent

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yatchew_linearity_test

Read-only

Determine if the conditional expectation E[y|x] follows a polynomial of a chosen order via the Yatchew differencing test, providing verified evidence for causal decision-making.

Instructions

Yatchew differencing test that E[y | x] is a polynomial of order. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Running variable and outcome, same length.
`y`	Yes	Running variable and outcome, same length.
`order`	No	Polynomial order under the null. 1 = linear, 0 = constant.
`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
`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.
`het_robust`	No	Use the heteroskedasticity-robust statistic of the paper's Appendix E. ``did_had`` always does.
`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

C2.8/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, and the description does not contradict that. However, the description adds little beyond it: the 'Validation: validated evidence tier' fragment is vague and does not explain side effects such as the server-side caching enabled by as_handle, or any other behavioral constraints. With annotations carrying the safety profile, this sparse disclosure earns a low score.

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

Conciseness3/5

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

The first sentence is concise and front-loaded. The trailing 'Validation: validated evidence tier...' fragment is cryptically worded and does not clearly earn its place; it reads like an internal metadata tag rather than an explanatory sentence. Overall short but with a confusing inclusion.

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?

Despite 100% schema coverage and an output schema, the description is too terse for a tool with 10 parameters and many statistically similar siblings. It does not state key context such as sorting/numeric requirements for x, when to prefer this over other specification tests, or what the validation evidence tier means. The missing context hurts correct selection and invocation.

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 schema already documents all parameters, including the meaning of order (polynomial order under the null). The description's phrase 'polynomial of order' reinforces but does not add new semantics. Baseline 3 is appropriate because the schema does the heavy lifting.

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 statistical test (Yatchew differencing test) and states the null hypothesis: E[y|x] is a polynomial of the given order. This is a clear verb+resource statement. It does not explicitly contrast with sibling specification tests like reset_test or functional_form_test, so it misses the top score.

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 instead of the many related specification tests among the siblings (reset_test, functional_form_test, blp_test). No conditions, prerequisites, or exclusions are stated, so the agent must infer appropriateness from the name alone.

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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