Causal Decision Agent

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bjs_pretrend_joint

Read-only

Runs a cluster-bootstrap joint Wald test on pre-treatment coefficients to validate the parallel trends assumption in event studies. Outputs diagnostics for causal decisions.

Instructions

Cluster-bootstrap joint Wald test for BJS pre-treatment coefficients.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`seed`	No	RNG seed for reproducibility.
`time`	Yes	Time period column.
`group`	Yes	Group or cohort identifier.
`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
`n_boot`	No	Cluster-bootstrap replications. Clusters are sampled with replacement; unit ids are reassigned in the resampled frame so BJS refits cleanly.
`result`	Yes	Output of :func:`did_imputation` on ``data`` with a non-trivial ``horizon`` that covers negative values. Only its ``model_info['event_study']`` frame is consulted, to look up the observed pre-period point estimates that we re-test with a covariance-aware statistic. Same arguments you passed to the original :func:`did_imputation` call. Needed to re-run BJS on each cluster-bootstrap resample.
`cluster`	No	Cluster identifier column for clustered standard errors.
`horizon`	No	If omitted, inferred from ``result.model_info['event_study']``.
`controls`	No	Control-variable column names.
`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.
`first_treat`	Yes	first_treat parameter (str).
`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

C2.9/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds only the methodological label (cluster-bootstrap, joint Wald) but no behavioral context beyond annotations—no mention of data requirements, failure modes, output structure, or anything the agent should expect at runtime.

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 a single, front-loaded sentence with no filler. It is concise, but it achieves brevity by omitting important context, so it is not ideal despite being efficient.

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 16 parameters, 6 required, and dependence on a prior did_imputation result, a one-sentence description is inadequate. The output schema covers return values, but the description does not explain prerequisites, workflow, or the role of the `result` parameter, forcing the agent to infer critical context from parameter metadata alone.

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 baseline is 3. The description itself adds no parameter meaning beyond the schema, and it does not compensate for the complexity of the required `result` and `data_path` relationship.

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 uses a specific verb-noun structure: 'cluster-bootstrap joint Wald test' targeting 'BJS pre-treatment coefficients'. It clearly identifies the statistical procedure and resource, though it does not explicitly contrast with siblings such as bjs or pretrends_test.

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 like pretrends_test or event_study. The description also omits prerequisites such as requiring a previously fitted did_imputation result, leaving the agent without routing or sequencing information.

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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