Causal Decision Agent

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zip_model

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Fit zero-inflated Poisson regression to count data with excess zeros, providing maximum-likelihood estimates, diagnostics, and certified parity evidence for evidence-backed decisions.

Instructions

Zero-Inflated Poisson (ZIP) regression via MLE. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	No	Count-equation regressors (alternative to formula).
`y`	No	Dependent variable name (alternative to formula).
`tol`	No	Convergence tolerance.
`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
`robust`	No	"nonrobust", "HC0", "HC1", etc.	nonrobust
`cluster`	No	Cluster variable name for clustered standard errors.
`formula`	No	Patsy-style formula for the count equation, e.g. "y ~ x1 + x2".
`inflate`	No	Inflation-equation regressors. Default: same as count regressors.
`maxiter`	No	Maximum iterations for optimizer.
`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

C2.8/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, so the description's 'via MLE' adds a minor methodological detail. However, it does not disclose what the tool actually returns, how it handles inputs (e.g., formula vs. x/y), or any side effects. The cryptic phrase 'Validation: certified parity evidence' adds no usable behavioral context.

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 (two sentences) and front-loaded with the core purpose. However, the second sentence is an unclear fragment ('Validation: certified parity evidence') that does not earn its place and detracts from overall clarity.

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?

For a tool with 15 parameters and an output schema, the description is severely under-specified. It does not mention that the model is for count data with excess zeros, how to specify the count and inflation equations, or that the tool returns a fitted model. An agent would have to rely entirely on the schema to understand the tool's use.

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 all 15 parameters are documented in the input schema. The tool description adds no parameter-specific meaning; it neither repeats nor supplements the schema, so the baseline score of 3 applies.

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

Purpose5/5

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

The description states the tool performs Zero-Inflated Poisson (ZIP) regression via MLE, which is a specific verb-resource combination that clearly distinguishes it from siblings like poisson (standard Poisson) and zinb (zero-inflated negative binomial). The model type and estimation method are explicit.

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

Usage Guidelines1/5

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

No guidance is provided on when to use ZIP regression versus alternatives such as poisson, zinb, or hurdle. The description does not mention prerequisites, data requirements, or conditions that would select this tool over others.

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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