Causal Decision Agent

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multiway_cluster_vcov

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Compute N-way cluster-robust variance for OLS coefficients to correct standard errors when observations cluster along multiple dimensions, supporting finite-sample adjustments and PSD projection for valid inference.

Instructions

Compute N-way cluster-robust variance of an OLS coefficient vector. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`X`	Yes	Design matrix used in the regression.
`resid`	Yes	OLS residuals.
`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
`clusters`	Yes	One or more cluster variables, one per dimension. Non-numeric labels are supported.
`n_params`	No	Override for the ``k`` used in DOF adjustment; useful when FEs have been absorbed (pass total absorbed DOF here).
`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://.
`df_adjust`	No	If True, apply the G/(G-1) * (n-1)/(n-k) CR1 finite-sample correction per component variance. If False, uses raw sandwich (useful when the caller has already degreed-freedom adjusted).
`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.
`psd_correct`	No	Project V onto PSD cone by zeroing negative eigenvalues.
`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

B3.2/5.0

Behavior3/5

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

Annotations already disclose readOnlyHint=true, so the agent knows this is a safe read operation. The description adds a validation note ('certified parity evidence') but does not describe return structure, side effects, or any behavioral nuances beyond what annotations provide. It does not contradict annotations, so a 3 is appropriate.

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 concise sentence that states the core purpose and a validation note. It is front-loaded with the main action and contains no fluff. For a tool with such a minimal description, it is appropriately sized.

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's complexity (12 parameters, N-way clustering, DOF adjustments, data loading options), the description is far too sparse. It does not explain the intended use case, how it fits into a regression workflow, or what distinguishes it from many similar cluster-robust tools. The schema covers parameters, but the description fails to provide the contextual guidance an agent needs to choose and invoke this tool correctly.

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 parameters are documented in the input schema. The description adds no parameter-level meaning beyond what the schema already provides. Baseline of 3 applies because the schema does the heavy lifting.

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 specific verb ('Compute') and resource ('N-way cluster-robust variance of an OLS coefficient vector'). It is clear about the mathematical operation, but it does not differentiate from sibling tools like cluster_robust_se, twoway_cluster, or cr2_se, which all compute related quantities. Thus it is clear but lacks sibling differentiation.

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 use this tool versus alternatives. There is no mention of when N-way clustering is appropriate, how it compares to twoway_cluster or other cluster-robust variants, or any exclusions. The only extra note ('Validation: certified parity evidence') does not help with usage selection.

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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