ggdid

Read-only

Visualize aggregated treatment effects from aggte() results with pointwise and uniform confidence bands, enabling clear inspection of causal estimates for informed decisions.

Instructions

Plot an aggte() result, mirroring R :func:did::ggdid.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`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	Output of :func:`aggte`.
`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://.
`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.
`band_color`	No	Colours for the pointwise estimate and the uniform band.	#F18F01
`point_color`	No	Colours for the pointwise estimate and the uniform band.	#2E86AB
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`show_uniform`	No	Draw uniform band (shaded region).
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`show_pointwise`	No	Draw pointwise CI lines.

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

C2.9/5.0

Behavior2/5

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

The annotations already declare readOnlyHint=true, and the description adds little beyond that: it mentions 'mirroring R did::ggdid' but does not disclose what the plot displays, whether it requires a fitted result or can compute from data, or how chaining with result_id/as_handle behaves. There is no contradiction with annotations, but the description is mostly silent on behavior.

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 11-word sentence, front-loaded with the action and target. It is concise and free of filler, but its brevity comes at the cost of omitting important contextual information that would have made the tool more usable.

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 tool has 14 parameters, including data_path, result_id, as_handle, and multiple plotting options, a one-line description is insufficient. The description does not explain how result relates to data_path/result_id, what the plot shows, or when the various toggles are relevant. The output schema exists, but the parameter interaction is too underspecified for reliable invocation.

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 despite the main description adding nothing about parameters. However, several schema descriptions are placeholder-level ('ax parameter.', 'title parameter (Optional[str]).'), so the description does not meaningfully compensate for those weak parameter 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 verb and resource: 'Plot an aggte() result'. This distinguishes it from non-plotting siblings and from plotting tools that target other object types, but it does not describe what the plot shows or how it differs from other plotting tools like did_plot or 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 Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It only implies usage when an aggte() result exists, with no mention of exclusions, prerequisites, or when another plotting tool would be a better fit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Causal Decision Agent