Causal Decision Agent

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rdsampsi

Read-only

Calculate the minimum sample size required to achieve a target statistical power in a regression discontinuity design, given effect size and significance level.

Instructions

Minimum sample size for a given power in an RD design. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`c`	No	c parameter (float).
`x`	No	Primary running variable, regressor, or feature input for this estimator.
`y`	No	Outcome variable column name or outcome array.
`tau`	Yes	Quantile level or target treatment-effect index.
`alpha`	No	Significance level for confidence intervals and tests.
`ratio`	No	``n_right / n_left``. Default 1.0 assumes equal allocation.
`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
`h_left`	No	h_left parameter (float).
`h_right`	No	h_right parameter (float).
`var_left`	No	var_left parameter (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.
`var_right`	No	var_right parameter (float).
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`target_power`	No	target_power parameter (float).
`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?

Annotations already indicate a read-only operation, so the description does not need to restate safety. The description adds only a terse purpose and an opaque 'Validation: certified parity evidence' note, which provides limited behavioral context but does not contradict the readOnlyHint.

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 short and front-loads the essential purpose. The 'Validation: certified parity evidence' sentence is cryptic and arguably not useful, but the overall definition is compact and not bloated.

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 17 parameters and many closely related RD siblings, this description is too thin. It does not explain when to use it, how tau relates to the calculation, which parameters are needed, or how it differs from rdpower and related tools.

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 even though the description itself adds no parameter-level meaning. The descriptions in the schema are generic and the tool description does not clarify which parameters are actually relevant for an RD sample-size calculation.

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 communicates that this tool computes the minimum sample size for a target power in an RD design. However, it uses a noun phrase rather than an explicit verb and does not directly distinguish itself from closely related siblings such as rdpower or rdms.

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?

There is no guidance about when to use this tool versus alternatives like rdpower, rdsensitivity, or other RD power/sample-size tools. The description provides no prerequisites, no exclusions, and no conditions that would help an agent choose it correctly.

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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