Causal Decision Agent

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methods_appendix

Read-only

Turn fitted statistical results into a referee-grade Methods and Formulas appendix, including diagnostics, assumptions, and citations in LaTeX, Markdown, or plain text.

Instructions

Generate a referee-grade Methods and Formulas appendix for results.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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
`format`	No	Output format. ``"latex"`` emits ``\subsection*`` blocks with display math; ``"markdown"`` emits ``###`` headings with ``$$`` math; ``"text"`` emits a plain-text rendering.	latex
`results`	Yes	One or more fitted result objects exposing ``method`` / ``model_info`` / ``cite``.
`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`	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.
`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.
`include_citation`	No	Append the APA-style reference from ``result.cite()``.
`include_provenance`	No	Append a one-line provenance trace (StatsPAI version + estimator identity + methods-spec key) -- the "exact code path" leg of the formula / citation / code-path traceability triple.
`include_assumptions`	No	Include the identifying-assumptions list.
`include_diagnostics`	No	Include the inference block (SE method, clustering, bandwidth, F, CI).

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

B3.1/5.0

Behavior2/5

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

Annotations declare readOnlyHint=true and openWorldHint=false, so the agent already knows this is a safe, closed read operation. The description adds no behavioral context beyond the word 'generate'—it does not mention side effects, output size, caching, or anything else not already captured by annotations.

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 wasted words. It is appropriately concise, though it lacks any additional structure or detail—acceptable given the small size, but not as information-dense as the strongest examples.

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

Completeness4/5

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

Given the extremely detailed parameter schema, the read-only annotations, and the presence of an output schema, the one-sentence purpose statement is largely sufficient for an agent to understand what the tool does and what it returns. It does not cover usage context, but that is the responsibility of the usage-guidelines dimension.

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 all 12 parameters including the required 'results' and the enums for detail/format are already well documented. The tool description itself adds no parameter-level meaning beyond what the schema provides, so the baseline 3 applies.

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 states a specific verb and resource: it generates a referee-grade Methods and Formulas appendix from results. This clearly distinguishes it as a reporting utility, though it does not explicitly contrast itself with sibling reporting tools such as cs_report or robustness_report.

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?

The description gives no guidance on when to use this tool instead of the many sibling reporting and robustness tools. It does not state when it is appropriate, what prerequisites exist (e.g., a previously fitted result), or when an alternative would be preferable.

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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