Causal Decision Agent

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margins_at_plot

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Plot predictive margins from margins_at() with confidence bands to reveal group differences and trends, turning model estimates into interpretable visuals for decision-making.

Instructions

Plot predictive margins from margins_at() with confidence bands.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	No	Variable to place on the x-axis. If None, inferred as the at-variable with the most unique values.
`ax`	No	ax parameter.
`by`	No	Variable to produce separate lines for (legend grouping).
`title`	No	title parameter (Optional[str]).
`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
`xlabel`	No	xlabel parameter (Optional[str]).
`ylabel`	No	ylabel parameter (str).	Predicted Value
`figsize`	No	figsize parameter (Tuple[float, float]).
`palette`	No	Colours for each ``by`` group.
`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.
`margins_at_df`	Yes	Output from ``margins_at()``.

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

A3.7/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating a safe, non-mutating operation. The description adds the detail of 'confidence bands', but does not explain how the plot is returned (e.g., as a file, JSON, or inline) or any side effects. It does not contradict annotations.

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?

The description is a single, focused sentence with no redundant information. It is concise and front-loaded with the core purpose, making it easy for an agent to parse quickly.

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?

The tool has 15 parameters and an output schema, so the return format is likely specified there. However, the description does not clarify how the plot is delivered (e.g., whether it returns a figure object, a path, or a base64 string). Given the tool's complexity, a bit more context about the output would be helpful, but the schema may cover it. Overall, adequate but not fully complete.

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 parameters are documented in the schema. The description adds no additional meaning beyond referencing margins_at_df as the input. It does not clarify parameter interactions or defaults beyond what the schema provides, so the baseline 3 is appropriate.

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 clearly states the tool's purpose: to plot predictive margins from margins_at() with confidence bands. It uses a specific verb ('Plot') and names the source function, making it distinguishable from siblings like margins_table or marginsplot, which serve different purposes.

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 the tool is used for plotting margins produced by margins_at(), but it does not explicitly contrast it with alternative plotting tools like marginsplot or did_plot. There is no guidance on when to choose this over others, leaving the agent to infer from context.

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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