Causal Decision Agent

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evalue_rr

Read-only

Compute an E-value from a risk ratio and its confidence interval to quantify the minimum unmeasured confounding strength needed to explain away the observed effect.

Instructions

E-value computed directly from a risk ratio and its CI bounds. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rr`	Yes	Point-estimate risk ratio.
`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
`rr_lower`	No	Optional confidence-interval bounds on the risk ratio scale.
`rr_upper`	No	Optional confidence-interval bounds on the risk ratio scale.
`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.
`rare_outcome`	No	Passed through to :func:`evalue` for rare-outcome OR->RR correction.
`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

C2.8/5.0

Behavior2/5

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

The readOnlyHint annotation already signals safety, and the description adds only the direct-computation claim. 'Validation: certified parity evidence' is opaque and does not meaningfully disclose behavior, side effects, output semantics, or limitations.

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

Conciseness3/5

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

The first sentence is front-loaded and concise. However, the second sentence is cryptic and does not clearly earn its place, and the overall terseness comes at the cost of operational clarity.

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 the tool has 10 parameters, a rich schema, and many similar siblings, the description is too sparse to orient an agent. It does not explain how the parameters relate to the direct computation, when handle/data parameters are relevant, or what validation parity evidence means.

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 schema already documents all parameters. The description only echoes rr and CI bounds and does not add meaningful semantics for detail, data_path, result_id, rare_outcome, or the other optional parameters.

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 the tool computes an E-value directly from a risk ratio and its CI bounds, which names a specific operation and resource. It implicitly differentiates from siblings like evalue_rd or evalue_from_result, but it never explicitly names an alternative.

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 instead of evalue, evalue_from_result, sensitivity_rr, or other sensitivity siblings. The phrase 'computed directly from a risk ratio and its CI bounds' implies a use case, but no explicit selection criteria or exclusions are provided.

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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