Causal Decision Agent

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jive

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Estimate causal effects using jackknife instrumental variable analysis to correct endogeneity bias and produce validated confidence intervals.

Instructions

Jackknife Instrumental Variables Estimation (JIVE). Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`z`	No	Instrument, proxy, or auxiliary variable used by this estimator.
`alpha`	No	Significance level for confidence intervals and tests.
`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
`robust`	No	Robust standard-error or covariance estimator option.	nonrobust
`x_exog`	No	x_exog parameter (Optional[List[str]]).
`cluster`	No	Cluster identifier column for clustered standard errors.
`variant`	No	'jive1' (Angrist et al. 1999) or 'jive2' (alternative).	jive1
`x_endog`	Yes	x_endog parameter (List[str]).
`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

C2.5/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds only a validation-tier label, which is not behavioral disclosure and does not explain side effects, output characteristics, or prerequisites.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and readable, but the validation sentence is not actionable and could be omitted without loss. It is concise yet under-informative rather than economically informative.

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 complex estimator with 14 parameters and multiple variants, the description is far too thin. It lacks guidance on selecting variants, interpreting results, or when this tool is appropriate, leaving the agent to rely entirely on the schema and sibling names.

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 every parameter is documented in the input schema. The description adds no parameter-level meaning, meeting the baseline expected when the schema fully covers semantics.

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

Purpose3/5

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

The description identifies the estimator (JIVE) but lacks a verb like 'estimates' or 'computes', making it read as a title rather than an actionable purpose. It does not differentiate itself from other IV estimators among siblings such as ivreg or liml.

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 provided on when to use JIVE versus alternative estimators. The only additional context, a validation tier note, is metadata about evidence quality rather than usage direction.

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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