Causal Decision Agent

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balanceplot

Read-only

Visualize covariate balance after matching or entropy balancing with a Love plot of standardized mean differences, enabling assessment of imbalance and adequacy of adjustment.

Instructions

Love plot: covariate balance visualization (SMD dot plot).

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	Result from ``match()`` or ``ebalance()``.
`figsize`	No	Height auto-scales with number of covariates if None.
`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.
`threshold`	No	SMD threshold lines.
`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 safety profile is known. The description adds one useful behavioral detail: it produces a dot plot of standardized mean differences. It does not describe output or side-effect behavior further, but the annotations and output schema carry most of that burden.

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 compact sentence with no filler words; it front-loads the core purpose and visualization format. It is concise without being padded, though it is slightly too terse to carry full usage context.

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

Completeness3/5

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

The structured fields are rich: full parameter descriptions, an output schema, and read-only annotations. The main gap is prose context about the match()/ebalance() workflow and how to choose this over love_plot, which the schema cannot fully compensate for.

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 coverage is 100%, so all 11 parameters already have descriptions. The tool description adds no parameter-level semantics, which is acceptable under the high-coverage 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 clearly identifies the tool as a covariate balance visualization and specifies the x-axis metric as SMD, making it distinct from generic plotting or regression tools. However, it does not differentiate balanceplot from the sibling love_plot, which appears to serve the same purpose.

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 explicit guidance on when to use this tool, and it does not mention alternatives such as love_plot or balance_diagnostics. The phrase 'covariate balance visualization' only implies a post-matching/weighting diagnostic context without saying so.

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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