treatment_rollout_plot

Read-only

Visualize treatment rollout timing across units. Plot staggered adoption and never-treated cohorts to reveal when each group first receives treatment.

Instructions

Visualise staggered treatment adoption timing.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`id`	Yes	Unit identifier.
`time`	Yes	Time period variable.
`title`	No	title parameter (Optional[str]).
`treat`	Yes	First-treatment-period column (0 = never treated), or binary treatment indicator.
`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
`figsize`	No	figsize parameter (Tuple[float, float]).
`sort_by`	No	Sort units by: 'treat_time' (earliest first), 'id', or 'random'.	treat_time
`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`	Yes	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.
`never_color`	No	Color for never-treated units.	#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.
`treated_color`	No	Color for treated unit-periods.	#E74C3C
`untreated_color`	No	Color for untreated unit-periods.	#ECF0F1
`show_cohort_labels`	No	Annotate cohort boundaries on the y-axis.

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

A3.6/5.0

Behavior3/5

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

The annotation readOnlyHint=true already conveys this is a read-only operation, and the description's verb 'Visualise' is consistent with that. The description adds no extra behavioral context beyond the annotation, but it also does not omit anything critical because the annotation covers the safety profile and the output schema is present.

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 sentence with no filler, front-loads the primary action, and earns its place by clearly stating the tool's purpose. It is appropriately sized for a simple visualization tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although the description is terse, the schema is highly detailed with 100% parameter coverage, the annotations indicate read-only behavior, and an output schema exists. Together these structured elements provide sufficient context for an agent to invoke the tool correctly, making the minimal description acceptable.

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 each parameter is already documented in the input schema. The description itself adds no parameter-level meaning beyond the schema, which is acceptable under the baseline given full schema coverage.

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 ('Visualise') and a specific resource ('staggered treatment adoption timing'), making the tool's goal clear and distinct from generic plotting tools. However, it does not explicitly differentiate from sibling plotting tools like did_plot or event_study_plot, relying on the tool name to carry that distinction.

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 should be used when one needs to visualize staggered treatment adoption timing, but it provides no explicit guidance on when to choose this tool over alternatives or what distinguishes it from similar visualization tools. It does not exclude any use cases or name sibling alternatives.

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