Causal Decision Agent

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cr2_se

Read-only

Calculate bias-corrected cluster-robust standard errors (CR2) for a fitted regression result to obtain reliable inference and confidence intervals with cluster variable.

Instructions

CR2 bias-corrected cluster-robust standard errors (Bell & McCaffrey 2002). Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`alpha`	No	Significance level for confidence intervals.
`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	A fitted regression result from ``sp.regress()``.
`cluster`	Yes	Name of the cluster variable in ``data``.
`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.
`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

A3.5/5.0

Behavior3/5

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

The readOnlyHint annotation already signals this is a safe read operation. The description adds context about the specific CR2 bias correction and validation evidence, which is useful, but it does not disclose behavior such as required cluster sizes, output structure, or limitations beyond what the annotation and schema already provide.

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 very short: two sentences that front-load the estimator identity and citation. It contains no fluff and earns its place, though it is terse enough that a bit more usage context could fit without harm.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the read-only annotation, 100% schema coverage, and the presence of an output schema, the description is largely sufficient for an agent to invoke the tool correctly. It could add guidance on when CR2 is preferred over alternative cluster-robust methods, but nothing critical is 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 all nine parameters are documented in the input schema itself. The tool description adds no additional parameter-level meaning beyond that baseline, so the appropriate score is 3.

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 tool as computing CR2 bias-corrected cluster-robust standard errors, citing Bell & McCaffrey 2002. The 'CR2' qualifier differentiates it from sibling tools like cluster_robust_se and cr3_jackknife_vcov, though it does not explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states what the tool computes but provides no explicit guidance on when to choose CR2 over alternative cluster-robust estimators such as CR1 or CR3. The validation note hints at reliability but does not give selection criteria or exclusion conditions.

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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