test

Read-only

Run a Wald test on fitted model coefficients to validate linear restrictions, delivering diagnostics and suggested actions for evidence-backed decisions.

Instructions

Wald test for linear restrictions on coefficients. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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
`result`	Yes	Fitted model with ``.params`` and ``.std_errors``.
`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.
`hypothesis`	Yes	Hypothesis specification. Examples: - ``"x1 = 0"`` -- test if beta_x1 = 0 - ``"x1 = x2"`` -- test if beta_x1 = beta_x2 - ``"x1 = x2 = 0"`` -- joint test - ``"x1 + x2 = 1"`` -- linear restriction
`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

B3.3/5.0

Behavior3/5

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

The annotations include readOnlyHint=true, so the tool is already known to be non-mutating. The description adds the operation type but no additional behavioral context such as side effects, prerequisites, or output details. The 'Validation: validated evidence tier' sentence is cryptic and does not clearly disclose a behavioral trait.

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 description is short and front-loaded with the core purpose. The second sentence about 'validated evidence tier' is a fragment that does not earn its place as explanatory content and may confuse rather than clarify. It is concise but not fully disciplined.

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

Completeness3/5

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

The schema is rich and an output schema exists, so the description does not need to explain return values or parameter formats. Still, for a tool named 'test' with many statistical-test siblings, the description lacks contextual guidance about when to prefer this Wald test over alternatives. The validation sentence adds little to make the tool's role complete.

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 already well documented. The tool description itself adds no parameter-level meaning beyond what the schema provides. The baseline of 3 applies because the schema carries the full burden and the description does not improve on it.

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 clearly states the tool's function: 'Wald test for linear restrictions on coefficients.' This is a specific verb and resource, and it helps distinguish the tool from generic siblings like lrtest or reset_test. However, it does not explicitly compare itself to any sibling, so it stops short of full differentiation.

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

Usage Guidelines3/5

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

The description implies usage: an agent should call this when it needs to test linear restrictions on coefficients via a Wald test. It provides no explicit when-to-use or when-not-to-use guidance and does not name alternatives, even though many sibling testing tools exist. The schema gives parameter-level context, but the description itself offers no routing advice.

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

Deploy Server

Causal Decision Agent