Causal Decision Agent

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synthdid_units_plot

Read-only

Visualize unit weight contributions with a horizontal bar chart, showing top-N donors by weight to identify key units in synthetic difference-in-differences analysis.

Instructions

Horizontal bar chart of unit weight contributions.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ax`	No	ax parameter.
`top_n`	No	Show the top-N donors by weight.
`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 parameter (CausalResult).
`figsize`	No	figsize parameter (Tuple[float, float]).
`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

C2.9/5.0

Behavior2/5

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

The readOnlyHint annotation already establishes this is a safe read operation, and the description adds little behavioral context beyond that. It does not disclose how the result is consumed, how top_n affects the chart, what is returned, or any edge-case behavior, so the description carries almost none of the behavioral 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, front-loaded sentence with no filler or redundancy. It is concise and immediately communicates the output type, though it could arguably include one more clause about the required 'result' input without losing 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 full parameter coverage, the tool has 10 parameters and sits among dozens of plot and synthetic-control siblings. The description does not explain how to obtain the required 'result', how result_id/data_path alternatives work together, or how this plot relates to synthdid_estimate/synthdid_plot, leaving important invocation context missing.

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 input schema already documents all parameters and their purpose. The description itself adds no parameter meaning beyond what the schema provides; baseline 3 applies.

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 deliverable as a horizontal bar chart and narrows the content to 'unit weight contributions,' which matches the tool name and is distinct from generic plotting siblings like synthdid_plot or synthplot. However, it does not explicitly differentiate this from closely related synthdid plotting tools or state what a 'unit' refers to in this context.

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 provided about when to use this tool versus the many sibling plotting and synthdid tools. There are no exclusions, prerequisites, or alternative tool references, so the agent must infer usage entirely from the name and parameter schema.

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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