Causal Decision Agent

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conditional_lr_ci

Read-only

Compute Moreira conditional likelihood-ratio confidence sets for instrumental variable analyses, yielding certified evidence to support causal business decisions.

Instructions

Moreira (2003) CLR confidence set -- re-export of Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`exog`	No	exog parameter (Optional[Union[np.ndarray, pd.DataFrame, List[str]]]).
`endog`	Yes	endog parameter (Union[np.ndarray, pd.Series, str]).
`level`	No	Confidence level or reporting level.
`n_sim`	No	Number of sim.
`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_grid`	No	Number of grid.
`add_const`	No	add_const parameter (bool).
`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.
`beta_grid`	No	Grid of beta values to evaluate.
`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://.
`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.
`instruments`	Yes	instruments parameter (Union[np.ndarray, pd.DataFrame, List[str]]).
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`random_state`	No	Random seed or RandomState for reproducible stochastic steps.
`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

D1.7/5.0

Behavior1/5

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

Annotations indicate readOnlyHint=true, so the agent knows it is a non-mutating operation, but the description adds no behavioral context beyond that. It does not explain what output is produced, how the confidence set is constructed, what inputs affect behavior, or any side effects. The 're-export... certified parity evidence' wording is obscure and not informative.

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

Conciseness2/5

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

The description is short and front-loaded with the method name, but the 're-export of Validation: certified parity evidence' fragment is filler that obscures rather than clarifies. Although concise, it sacrifices substance for brevity.

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

Completeness1/5

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

For a tool with 16 parameters, 3 required inputs, an output schema, and many statistical siblings, this description is severely incomplete. It fails to state what the CLR confidence set is for, what inputs mean in context, what the output contains, or how it relates to alternative IV inference tools.

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 16 parameters. The description itself adds no parameter-specific meaning, but because the schema carries full parameter explanations, the baseline of 3 is appropriate.

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

Purpose2/5

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

The description mainly restates the tool name ('Moreira (2003) CLR confidence set') with a citation, but lacks an explicit verb and resource description such as 'Compute a confidence set...'. The phrase 're-export of Validation: certified parity evidence' is cryptic and conveys no functional meaning, making it hard for an agent to know precisely what action this tool performs.

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

Usage Guidelines1/5

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

There is no guidance about when to use this tool versus alternatives such as anderson_rubin_ci or other IV confidence-set tools. The description provides no context, prerequisites, or exclusions, leaving the agent to guess the appropriate invocation scenario.

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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