Causal Decision Agent

by vikranthviki

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Python

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cps_wage

Read-only

Produce CPS-style wage datasets with a gender gap, enabling causal analysis and reproducible decision audits.

Instructions

CPS-style wage data with a gender gap.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`n`	No	n parameter (int).
`seed`	No	Random seed for reproducible stochastic steps.
`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.
`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

C2.2/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, so the safety profile is known. However, the description adds almost no behavioral context: it does not explain what happens with n/seed, whether data_path overrides built-in data, or what kind of payload is returned beyond the enum on detail. This is not a contradiction, but it is a missed opportunity.

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

Conciseness2/5

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

The description is very short, but this is under-specification rather than useful conciseness. A one-line tagline that lacks a verb does not give the agent enough actionable information.

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 8 optional parameters, an output schema, and a huge sibling list, the description is far too sparse. The agent cannot determine the tool's primary behavior, expected output, or how to chain it with as_handle/result_id. The description is inadequate for 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 fully documents every parameter. The baseline of 3 applies. The description adds nothing about how the parameters relate to the 'gender gap' or the wage data, but it does not need to repeat the schema.

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

Purpose2/5

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

The description is a noun phrase ('CPS-style wage data with a gender gap') rather than a statement with a verb. It roughly restates the tool name and adds one attribute, but never says whether the tool generates, loads, returns, or fits this data. It also does not distinguish cps_wage from related data-oriented siblings like mincer_wage_panel.

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 tool versus alternatives, no exclusions, and no prerequisites. The agent is left to infer the intended scenario from the name and schema 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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