Causal Decision Agent

Overview Schema Source Related Servers Discussions Admin

cate_plot

Read-only

Visualize the conditional average treatment effect (CATE) distribution from metalearner results to inspect effect heterogeneity and guide rollout decisions.

Instructions

Plot the CATE distribution.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`kind`	No	'hist' for histogram, 'kde' for kernel density, 'both'.	hist
`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
`result`	Yes	Result from ``metalearner()``.
`figsize`	No	figsize parameter (tuple).
`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

B3.1/5.0

Behavior3/5

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

The readOnlyHint annotation already establishes that this is a safe, non-mutating operation, and the description does not contradict it. The description adds little behavioral context beyond the annotation—no mention of output format, side effects, or how the plot is delivered—but for a read-only plotting tool this is acceptable, not excellent.

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 zero filler and the key action is front-loaded. It is concise to the point of being sparse, but it does not waste words.

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?

For a tool with 12 parameters, a required 'result' input, and many sibling plotting/search tools, a one-sentence description is not enough. It does not explain the intended pipeline position, distinguish the plot type from cate_group_plot, or describe the output payload, leaving the agent to infer too much from the schema alone.

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 parameters are already documented in the input schema. The description itself adds no new meaning about parameters such as 'kind', 'as_handle', or 'detail', so it does not improve on the baseline.

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 uses a specific verb ('Plot') and resource ('CATE distribution'), making the core action clear. However, it does not differentiate this from sibling tools like cate_group_plot or plot_from_result, which could plausibly perform overlapping work.

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?

No guidance is given on when to use this tool versus alternatives such as cate_group_plot, cate_summary, or the generic plot_from_result. The required 'result' parameter hints at a metalearner dependency through the schema, but the description itself provides no usage context or exclusions.

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

Deploy Server