Causal Decision Agent

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diagnose

Read-only

Run regression diagnostics on your data by specifying outcome and predictor variables. Identify violations and receive next steps for evidence-backed decisions.

Instructions

Comprehensive regression diagnostics in one call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Independent variable names (excluding constant).
`y`	Yes	Dependent variable name.
`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
`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.
`print_results`	No	Print formatted output.

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

C2.4/5.0

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 no behavioral context beyond the annotation—it does not mention that the tool fits a regression model, what diagnostics are computed, whether it caches results (as as_handle suggests), or any side effects. This is a significant gap for a tool with many parameters.

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 single sentence is brief but under-specified—it reads as a tagline rather than a useful description. The structure is minimal and does not front-load key information. This is closer to under-specification than genuine conciseness.

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 9 parameters, complex output schema, and a very large sibling set, the description is far too thin. It fails to explain the tool's unique role, when to reach for it, or what diagnostics are included. An agent cannot reliably select or invoke this tool based on the description 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 individual parameters are fully documented in the schema. The description adds no additional meaning beyond the schema, making the baseline score of 3 appropriate.

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 states the tool performs 'regression diagnostics' with a general 'comprehensive' modifier, identifying the resource and action but not specifics such as which tests, model types, or output formats are included. It does not differentiate from numerous sibling diagnostic tools (e.g., assumption_audit, robustness_report, forest_diagnostics), leaving ambiguity.

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 this tool versus alternatives. There is no mention of typical use cases, prerequisites, or exclusions. An agent is left to infer the tool's purpose solely from the name and schema, which is insufficient.

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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