Causal Decision Agent

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group_time_plot

Read-only

Plot group-time ATT estimates from Callaway-Sant'Anna to visualize treatment effects over time for each cohort, highlighting significant and insignificant estimates.

Instructions

Plot group-time ATT estimates from Callaway-Sant'Anna.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`color`	No	Default color for dot plot.	#2C3E50
`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
`result`	Yes	Result from ``callaway_santanna()`` or ``did(method='cs')``. Must have ``detail`` DataFrame with 'group', 'time', 'att' columns.
`figsize`	No	figsize parameter (Tuple[float, float]).
`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://.
`plot_type`	No	'dot' or 'heatmap'.	dot
`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.
`sig_color`	No	Color for significant estimates.	#E74C3C
`alpha_level`	No	Significance threshold.
`insig_color`	No	Color for insignificant estimates.	#BDC3C7
`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

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description's 'Plot' action is consistent with a non-destructive visualization operation. The description adds little behavioral context beyond that—no mention of return format, display behavior, or server-side caching—but the annotations and output schema reduce the burden.

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, front-loading the verb and object. It is appropriately brief for a purpose-only description and every word earns its place.

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 schema and output schema cover inputs and outputs, and annotations cover safety, but the description alone leaves an agent to infer when to choose this over several similar plotting siblings. For a 15-parameter tool with many alternatives, a bit more orientation (e.g., plot_type or post-CS context) would improve completeness.

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 schema already documents all 15 parameters. The description adds no parameter-level meaning beyond connecting the tool to Callaway-Sant'Anna results, which the result parameter description already states.

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 ('Plot') and resource ('group-time ATT estimates') tied to Callaway-Sant'Anna, so the core purpose is clear. However, it does not explicitly differentiate this tool from sibling plotting tools like did_plot, ggdid, or cohort_event_study_plot.

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 phrase 'from Callaway-Sant'Anna' implies the tool should be used with results from callaway_santanna() or did(method='cs'), and the required 'result' parameter reinforces that. But the description never states when to prefer this over alternative plotting tools or provides exclusions.

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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