Causal Decision Agent

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synth_power

Read-only

Calculate statistical power for synthetic control designs across hypothetical effect sizes using Monte Carlo simulation.

Instructions

Power analysis for Synthetic Control designs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seed`	No	Random seed for reproducibility.
`time`	Yes	Time period column.
`unit`	Yes	Unit identifier column.
`alpha`	No	Significance level for the placebo test.
`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
`outcome`	Yes	Outcome variable name.
`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`	Yes	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.
`effect_sizes`	No	Grid of hypothetical additive effect sizes to evaluate. If ``None``, auto-generates 10 steps from 0 to 3 * pre-treatment SD of the outcome.
`treated_unit`	Yes	Identifier of the treated unit.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`n_simulations`	No	Number of Monte-Carlo simulations per effect size.
`treatment_time`	Yes	First treatment period (inclusive).

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?

The description is consistent with the readOnlyHint annotation, and no contradiction exists. It adds some scoping ('for Synthetic Control designs') but does not disclose the Monte Carlo simulation behavior, effect-size grid, or result-caching implications of as_handle. The annotation already covers the safety profile, so the minimal disclosure is acceptable but not enriched.

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?

A single, focused sentence with no redundant wording. It is front-loaded and immediately communicates the tool's role, making it exceptionally concise.

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 a rich schema and annotations, the one-sentence description does not integrate the tool into a broader analytical workflow. It does not explain when to run a synthetic-control power analysis, how it relates to other synthetic tools, or what the returned results support. Given the tool has 15 parameters and 6 required inputs, this is insufficient context.

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%, meaning every parameter is already documented with names and descriptions. The tool description itself adds no parameter-level meaning, so the baseline of 3 is appropriate.

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 that the tool performs 'Power analysis for Synthetic Control designs,' making its core function clear. It distinguishes itself from generic estimation tools and most synthetic-control siblings, though close relatives like synth_mde and synth_power_plot are not explicitly differentiated.

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 about when to use this tool versus alternatives such as synth_mde, pretrends_power, or synth_power_plot. There is no mention of prerequisites, appropriate input scenarios, or exclusions, leaving the agent to infer usage from the name and 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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