panel_logit

Read-only

Estimates panel logit models on binary outcomes with fixed, random, or Mundlak effects, enabling evidence-backed causal decisions through certified parity evidence and robust diagnostics.

Instructions

Panel logit model. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Regressors.
`y`	Yes	Binary dependent variable (0/1).
`id`	No	Unit and time identifier columns.	id
`tol`	No	Gradient tolerance.
`time`	No	Unit and time identifier columns.	time
`alpha`	No	Significance level for confidence intervals.
`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
`method`	No	'fe' (conditional FE logit), 're' (random effects), 'cre' (Mundlak).	fe
`robust`	No	'nonrobust' or 'robust'.	nonrobust
`cluster`	No	Column for cluster-robust SEs.
`maxiter`	No	Maximum optimizer iterations.
`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.
`n_quadrature`	No	Gauss-Hermite quadrature points (RE/CRE only).
`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?

readOnlyHint=true and openWorldHint=false already signal the general safety profile, and the description does not contradict those annotations. However, the description itself contributes no useful behavioral context: the 'Validation: certified parity evidence' fragment is opaque and does not explain side effects, chaining behavior, or other runtime characteristics.

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 text is short but under-specified rather than effectively concise. The second sentence is a vague fragment that does not earn its place, and the description omits the core purpose and selection information an agent needs.

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 a rich input schema and the presence of an output schema, the description alone is not complete enough for an agent to choose this tool among dozens of related estimators. It lacks a clear statement of what the tool does, the panel-data setting it targets, and how it relates to alternatives such as panel_probit, clogit, or logit.

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%, and the schema entries are unusually detailed, including payload-depth guidance, method choices, data path formats, and handle-based chaining. Since the description adds no parameter-level meaning beyond the schema, the baseline score of 3 is appropriate.

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 essentially a restatement of the tool name: 'Panel logit model' names the model type but provides no verb, action, or scope. 'Validation: certified parity evidence' is too vague to clarify what the tool actually computes or how it differs from siblings like logit, clogit, or panel_probit.

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 guidance is provided about when to use panel_logit rather than alternative estimators such as logit, clogit, melogit, or panel_probit. There are no exclusions, prerequisites, or selection rules; the 'Validation' clause does not function as usage guidance.

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