StatsPAI

causal

Read-only

Analyze a dataset in one call by diagnosing the study design, recommending and fitting an estimator, running robustness checks, and returning structured results.

Instructions

End-to-end causal workflow: diagnose -> recommend estimator -> fit -> run robustness -> return result. The one-shot entry point that lets an agent analyse a dataset in a single call without orchestrating stages itself.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "y": "<y>"
    },
    "name": "causal"
  }
}

Input Schema

Name	Required	Description	Default
`y`	Yes
`id`	No
`time`	No
`cohort`	No
`design`	No
`detail`	No	Payload depth; agent is the fullest.	agent
`data_id`	No	Data handle (d_...) from load_data; replaces data_path.
`data_csv`	No	Small inline table as CSV text.
`as_handle`	No	Cache the fit and return its result_id.
`data_path`	No	Absolute path of a data file on the server.
`result_id`	No	Handle of an earlier fit made with as_handle=true.
`treatment`	No
`covariates`	No
`instrument`	No
`running_var`	No
`data_records`	No	Small inline table as row objects.
`max_output_bytes`	No	Result byte budget (default 262144; 0 = none).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 12, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare it read-only and closed-world, and the description adds that it internally diagnoses, recommends an estimator, fits, and runs robustness checks. It does not disclose runtime/cost, estimator-selection behavior, failure modes, or the schema-level caching behavior of as_handle.

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?

Two sentences are front-loaded with the workflow and the key differentiator, with no redundant restatement. Each clause contributes useful routing or behavioral context.

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 17-parameter orchestration tool, the description gives only a high-level pipeline and omits essential call context such as the required outcome variable, data-source precedence, design handling, handle reuse, and payload limits. The output schema covers return values, but the definition remains thin relative to input complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 47% across 17 parameters, and the description provides no parameter-level guidance at all, not even for the required y or key design/data-source fields. The schema documents some inputs, but the description does not compensate for the low coverage as required.

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 names the resource and enumerates the workflow stages (diagnose, recommend estimator, fit, robustness, result). The phrase 'one-shot entry point' distinguishes it from stage-specific orchestration, though it does not explicitly name any sibling alternative.

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

Usage Guidelines3/5

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

It explains when to choose this tool: when an agent wants to analyze a dataset in a single call instead of orchestrating stages itself. It does not state when not to use it, prerequisites such as loading data or specifying design, or name alternatives like pipeline_* or individual estimator tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.