Causal Decision Agent

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megamma

Read-only

Fits a random-effects Gamma GLMM with log link for positive continuous outcomes with group variation. Returns coefficients, diagnostics, and next-step recommendations for data-driven verdicts.

Instructions

Random-effects Gamma GLMM with log link (Stata meglm family(gamma)).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`nAGQ`	No	nAGQ parameter (int).
`group`	Yes	Group or cohort identifier.
`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
`offset`	No	Offset term or offset column.
`x_fixed`	Yes	x_fixed parameter (Sequence[str]).
`x_random`	No	x_random parameter (Optional[Sequence[str]]).
`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

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered without description. The description adds model-specific context (log link, Stata family(gamma)) but discloses nothing about convergence, result structure, or edge cases; the output schema presumably covers the return format. This is adequate but not rich.

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?

One compact sentence with no filler; the model specification and Stata reference are both front-loaded. It earns its place, though it is terse.

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

Completeness3/5

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

For a 12-parameter model with a full output schema and readOnly annotation, the description is serviceable but minimal: it names the model and link but omits when-to-use context and any note about required data shape. The rich schema compensates for most gaps, but not the usage ambiguity.

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 is 3. The description adds no parameter-level meaning; all parameter semantics are carried by the input schema.

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 identifies the tool as a random-effects Gamma GLMM with log link and cites the Stata equivalent (meglm family(gamma)). It lacks an explicit verb like 'fit' or 'estimate,' but the model name unambiguously conveys the operation and the 'gamma' qualifier helps distinguish it from generic meglm and other mixed-model siblings.

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 given about when to use this tool instead of alternatives; it does not mention that this is for gamma-distributed positive outcomes or that generic meglm covers other families. An agent must infer usage from the Stata reference and sibling names, so this dimension is weak.

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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