Causal Decision Agent

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verify

Read-only

Validate a single recommendation against empirical data, producing a reproducible verdict with diagnostics and suggested next actions.

Instructions

Empirically verify a single recommendation.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`B`	No	Number of bootstrap replications (auto-reduced if over budget).
`rec`	Yes	A single entry from ``RecommendationResult.recommendations``.
`seed`	No	RNG seed for reproducibility.
`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
`budget_s`	No	Wall-clock budget per recommendation (seconds).
`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://.
`n_placebo`	No	Number of permutation placebo runs.
`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.
`K_subsample`	No	Number of 50% subsample splits.
`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

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the scoping constraint of 'a single recommendation' and the empirical nature of the operation, but it does not mention the computational cost, bootstrap/permutation behavior, optional caching via as_handle, or how results are returned. It does not contradict the annotations.

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?

The description is a single sentence of six words with no filler or redundancy. It is front-loaded and immediately states the tool's purpose without wasted text.

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 rich input schema plus the presence of an output schema cover invocation details and return expectations. However, the description lacks context about how a recommendation is produced, how rec should be sourced, and how this tool relates to verify_recommendation and verify_benchmark. Given the generic name and the large sibling list, this is a meaningful gap for an agent selecting the right tool.

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%, with detailed parameter descriptions for all 12 parameters, so the baseline is 3. The tool description itself adds no parameter-level meaning; agents must rely on the schema, which is adequate.

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 a clear verb and object: 'Empirically verify a single recommendation.' It identifies the resource and scope precisely enough for an agent to understand the core action. However, it does not explicitly distinguish this tool from similarly named siblings like verify_recommendation or verify_benchmark.

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 provides no guidance on when to use this tool versus alternatives such as verify_recommendation, verify_benchmark, replicate, or audit. There are no stated conditions, prerequisites, or exclusions, leaving tool selection to inference.

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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