Causal Decision Agent

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panel_compare

Read-only

Compare panel model estimators side by side. Run pooled, fixed, random, two-way, and Mundlak specifications and return a comparison table to guide model selection.

Instructions

Estimate the same model with multiple panel methods and return a side-by-side comparison table.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`time`	Yes	Time period column.
`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
`entity`	Yes	Panel entity identifier column.
`formula`	Yes	Model formula using patsy/R-style syntax.
`methods`	No	List of methods to compare, default: pooled/fe/re/twoway/mundlak
`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.
`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

A3.6/5.0

Behavior3/5

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

Annotations already flag readOnlyHint=true, and the description reflects compute-and-return behavior without contradicting that. It adds the comparison-table output concept, but does not disclose optional server-side caching via as_handle or any runtime caveats; the schema covers those details.

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

Conciseness5/5

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

A single sentence with no filler or redundancy. It front-loads the core action and output, making the tool easy to parse for an agent.

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

Completeness4/5

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

Given the rich input schema and presence of an output schema, the description does not need to enumerate parameters or return fields. It captures the essential purpose and output. The main contextual gap is the absence of guidance about related tools, but that is already penalized under usage guidelines.

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 baseline of 3 applies. The description does not add parameter-level detail beyond the schema; it only restates the high-level notion of multiple panel methods, which is already represented by the 'methods' parameter.

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 states a specific verb ('estimate'), a resource ('same model with multiple panel methods'), and the output ('side-by-side comparison table'). It is clear and differentiates from single-method panel estimators, though it does not explicitly name a sibling such as compare_estimators.

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 when to use the tool: when the user wants the same model estimated across several panel methods. However, it provides no explicit when-not-to-use guidance or comparison against closely related siblings like compare_estimators or panel_fgls.

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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