Skip to main content
Glama
vikranthviki

Causal Decision Agent

by vikranthviki

mincer_wage_panel

Read-only

Generate a synthetic two-period Mincer wage panel with a structural shift for causal analysis. Use it to test wage-impact models with reproducible data.

Instructions

Two-period Mincer wage distribution with a structural shift.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNon parameter (int).
seedNoRandom seed for reproducible stochastic steps.
detailNoPayload 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_handleNoIf 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_pathNoAbsolute 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_idNoOptional 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_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only safety profile is covered. However, the description adds almost no behavioral context: it does not say whether the tool simulates, loads, fits, or returns a table, nor what side effects or output semantics to expect beyond the vague 'structural shift' phrase.

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 single sentence is short and has no filler, which is superficially concise, but it is under-specified for an 8-parameter tool. It omits the operation and usage context, so the brevity reads as underspecification rather than appropriately sized content, and there is no front-loaded action to orient the agent.

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?

Even with an output schema and read-only annotations, an agent cannot determine whether to supply data_path or rely on n, what the returned object represents, or how this tool relates to sibling wage and structural-shift tools. The essential orientation about what the tool is for 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%, with useful parameter descriptions for detail, as_handle, data_path, and result_id. The tool description itself adds nothing about parameters, but at full schema coverage the schema carries the semantic burden, so the baseline 3 is appropriate.

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

Purpose3/5

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

The description is a noun phrase — 'Two-period Mincer wage distribution with a structural shift' — with no verb or operation such as generate, fit, load, or return. It conveys an econometric topic but leaves it unclear whether the tool simulates data, returns a distribution, or estimates a model, and it does not distinguish itself from siblings like cps_wage or structural_break.

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?

No when-to-use or when-not-to-use guidance is provided. The description does not tell an agent whether to choose this tool over sibling wage-panel, dataset, or structural-break tools, and no context or exclusions are given.

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

Deploy Server

Other Tools