Causal Decision Agent

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twoway_cluster

Read-only

Compute two-way cluster-robust standard errors for OLS results, adjusting for correlations across two grouping dimensions. Provides certified parity evidence.

Instructions

Compute two-way cluster-robust standard errors. 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	Fitted OLS result. Must have ``data_info`` containing ``'X'`` (design matrix), ``'y'`` (response), and ``'residuals'``.
`cluster1`	Yes	Column name for the first clustering dimension.
`cluster2`	Yes	Column name for the second clustering dimension.
`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

B3.3/5.0

Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=false. The description adds a 'Validation: certified parity evidence' claim, which provides some context beyond annotations, but it does not describe side effects, return behavior, or other operational traits. It neither contradicts the annotations nor fully elaborates on them.

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 extremely concise and front-loaded with the core purpose. However, the 'Validation: certified parity evidence' phrase is vague and not clearly actionable, slightly weakening the conciseness despite the short length.

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 10 parameters and 4 required, the description provides only a one-line operation and a cryptic validation note. It does not explain how to choose this over similarly named tools, nor what 'certified parity evidence' means, leaving substantial gaps in selection and invocation 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%, so the schema already documents all parameters. The description adds no parameter-specific information, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact operation and object: 'Compute two-way cluster-robust standard errors.' The adjective 'two-way' distinguishes it from sibling tools like cluster_robust_se (one-way) and multiway_cluster_vcov (general multiway), so an agent can clearly tell them apart.

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 on when to prefer this tool over alternatives such as cluster_robust_se or multiway_cluster_vcov. There are no exclusions, prerequisites, or context cues, leaving the agent to infer appropriate usage.

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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