Causal Decision Agent

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cohort_event_study_plot

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Plot per-cohort event study overlays with confidence intervals from causal difference-in-differences results, enabling comparison of treatment effects over time across groups.

Instructions

Per-cohort event study plot (overlay).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`ci`	No	Show confidence intervals for each cohort.
`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`` with 'group', 'relative_time', 'att' columns, and ``model_info['event_study']`` for aggregate.
`figsize`	No	figsize parameter (Tuple[float, float]).
`palette`	No	Colors for each cohort. Auto-generated if None.
`ci_alpha`	No	CI band transparency.
`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.
`show_aggregate`	No	Overlay the aggregate event study line.
`aggregate_color`	No	Color for aggregate line.	#2C3E50

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 establish readOnlyHint=true, and the description adds only the weak visual trait 'overlay'. It does not explain aggregate handling, required input provenance, or other behavioral details, so the description adds little beyond the annotations.

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 and free of filler, but for a 15-parameter function it is under-specified rather than efficiently structured. It offers no context to help an agent prioritize or interpret the schema.

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?

Although the schema is rich and an output schema exists, the description is too thin to locate this among many similar plotting tools. It never mentions the required `result` provenance or clarifies when this plot is preferred over nearby event-study plot siblings.

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 coverage is 100%, so the baseline is 3. The description contributes no parameter-level meaning beyond what the schema already provides.

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 phrase 'Per-cohort event study plot (overlay)' names a specific visualization resource and granularity ('per-cohort', 'overlay'), and it adds display characteristics rather than purely restating the tool name. However, it does not explicitly distinguish it from closely-related siblings such as enhanced_event_study_plot or group_time_plot.

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?

There is no when-to-use guidance, no exclusions, and no pointer to alternatives despite a large cluster of event-study/plot siblings. The intended selection context is left entirely to inference.

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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