Causal Decision Agent

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enhanced_event_study_plot

Read-only

Generate event study plots from DID results with pre/post shading and significance coloring to highlight treatment effects.

Instructions

Enhanced event study plot with pre/post shading and significance coloring.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`color`	No	Default color for estimates.	#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
`marker`	No	marker parameter (str).	o
`result`	Yes	DID result with event study in ``model_info['event_study']``.
`figsize`	No	figsize parameter (Tuple[float, float]).
`ci_alpha`	No	Confidence 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://.
`pre_color`	No	Pre-treatment shading color.	#EBF5FB
`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.
`shade_pre`	No	Shade pre-treatment region.
`show_zero`	No	Show horizontal zero line.
`sig_color`	No	Color for significant estimates. None disables coloring.	#E74C3C
`markersize`	No	markersize parameter (int).
`post_color`	No	Post-treatment shading color.	#FDEDEC
`shade_post`	No	Shade post-treatment region.
`alpha_level`	No	alpha_level parameter (float).
`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

B3.1/5.0

Behavior3/5

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

The readOnlyHint annotation already discloses the safety profile, so the description is not burdened with that. It adds context about the plot's visual features (shading, significance coloring) but does not clarify whether the tool creates a new figure, modifies an existing ax, or returns anything beyond the plot.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no fluff, efficiently naming the tool type and its key visual enhancements. While it is short, it avoids unnecessary elaboration.

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?

Given the presence of a rich schema and output schema, the description need not explain return values or parameter details. However, it omits any guidance on when this tool is the right choice relative to many sibling event-study and plotting tools, leaving a significant contextual gap for an agent deciding between them.

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 21 parameters. The description mentions pre/post shading and significance coloring, which map to parameters like shade_pre, shade_post, and sig_color, but adds no new meaning beyond the schema's own descriptions.

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 resource (event study) and the tool's action (produces a plot), and names distinctive features (pre/post shading, significance coloring). However, it does not differentiate from sibling plot tools like did_plot or cohort_event_study_plot, so it falls short of a 5.

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 guidance on when to use this tool versus alternatives with similar names (e.g., event_study, did_plot, cohort_event_study_plot). The description does not provide context or exclusions, leaving the selection decision to the agent without support.

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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