Causal Decision Agent

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interflex_plot

Read-only

Plot marginal effects of a treatment variable across a moderator variable from interflex output. Use this to visualize conditional effect estimates and confidence bands for causal analysis.

Instructions

The interflex figure: marginal effect of d across x with its

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	Draw into an existing axes; a new figure is created otherwise.
`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:`interflex`.
`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.
`show_hist`	No	Add the moderator histogram (treated / control) under the curve.
`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

C2.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds some useful context by naming the figure and the marginal-effect relationship it displays, but it does not disclose more substantive behaviors such as return/caching behavior or how the figure is produced, and it trails off mid-sentence.

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

Conciseness2/5

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

The description is short but structurally incomplete, ending abruptly with 'with its'. This reads as an under-specified fragment rather than a clean, complete sentence with a front-loaded action and object.

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 9 parameters, an enum-driven detail payload, and many related plot and estimation tools, a one-line truncated description is not enough context. The output schema may document return values, but the description fails to place the tool in a workflow, such as first fitting interflex and passing its result.

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. The description's references to `d` and `x` add slight meaning about what the plot displays, but it does not elaborate on parameters like result, ax, as_handle, or detail beyond what the schema already states.

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 identifies the tool's output as the interflex figure and names its core content: the marginal effect of `d` across `x`. This makes the purpose reasonably clear and helps distinguish it from generic plotting or estimation siblings like plot_from_result, but the missing verb and truncated 'with its' prevent 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?

The description gives no guidance on when to use this tool, what prerequisite result to supply, or how it relates to alternatives such as interflex or plot_from_result. With a very large sibling list, the absence of any usage or workflow direction is a clear gap.

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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