cate_group_plot

Read-only

Plot group-level CATE estimates with confidence intervals, enabling quick comparison of treatment effects across subgroups for evidence-based decisions.

Instructions

Plot group-level CATEs with confidence intervals.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`color`	No	color parameter (str).	#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
`figsize`	No	figsize parameter (tuple).
`group_df`	Yes	Output from ``cate_by_group()``.
`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.

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

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description need not restate that this is a safe read operation. The description adds minimal context: it indicates the plot includes confidence intervals, which is helpful but does not disclose behavioral traits like what happens with missing data, how the plot is returned, or whether it caches results. Since annotations cover the safety profile, a score of 3 is appropriate.

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 or redundant information. It is front-loaded with the primary purpose and does not waste tokens. This is an exemplary level of conciseness.

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?

Despite having an output schema and comprehensive parameter descriptions, the main description is too sparse to provide adequate context for an 11-parameter visualization tool. It does not mention the relationship to cate_by_group, how to interpret the output, or any usage scenario. While the schema carries some burden, the description should at least orient the agent on when and how to use the tool; it currently leaves too much implicit.

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 all 11 parameters are documented in the schema. The description itself adds no parameter-specific meaning beyond the schema. According to the calibration, with high schema coverage the baseline is 3, and the description does not enhance or clarify parameters further.

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-level CATEs with confidence intervals'), which clearly conveys the tool's function. It does not explicitly distinguish it from siblings like cate_plot or plot_from_result, but the 'group-level' qualifier provides some differentiation. The purpose is not a tautology and is understandable.

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 offers no guidance on when to use this tool versus alternatives such as cate_plot or plot_from_result. It does not mention prerequisites (e.g., that group_df must come from cate_by_group), nor does it describe any context for choosing this tool over others. The schema parameter description for group_df mentions this dependency, but the main description itself provides no usage direction.

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