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vikranthviki

Causal Decision Agent

by vikranthviki

from_r

Read-only

Convert a single R expression for causal models (feols, felm, lm, att_gt, did) into a validated StatsPAI tool-call payload for statistical analysis.

Instructions

Translate a single R / fixest / felm / did expression to a verified StatsPAI tool-call payload. Returns the same shape as from_stata. Supported callables: feols / felm / lm / att_gt / did. Pass ONE expression -- no assignment, no piping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNoPayload 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_handleNoIf 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_pathNoAbsolute 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_idNoOptional 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.
expressionYesOne R expression, e.g. 'feols(y ~ x | id^year, data=df, cluster="id")'.
data_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds useful behavioral detail: it returns a payload rather than executing, produces the same shape as from_stata, and enforces a single-expression/no-assignment/no-piping rule. No contradictions with annotations.

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?

Three sentences, all dense with information: purpose, output-shape reference, supported callables, and hard constraints. There is no filler or redundant restatement of the tool name.

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?

With an output schema present and all seven parameters described in the schema, the description is largely complete. It could explicitly direct Stata-style expressions to from_stata, but the tool name, the 'R expression' framing, and the from_stata reference make that inferable.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds value by enumerating supported callables ('feols / felm / lm / att_gt / did') and by constraining the expression parameter to a single expression with no assignment or piping, which goes beyond the schema example.

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

Purpose5/5

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

The description states a specific verb ('Translate'), a clear resource ('a single R / fixest / felm / did expression'), and the output ('a verified StatsPAI tool-call payload'). It also names the supported callables and explicitly references from_stata, which distinguishes it from its most likely sibling.

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

Usage Guidelines4/5

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

The description gives clear context: it is for R/fixest/felm/did expressions, and it imposes explicit constraints ('Pass ONE expression -- no assignment, no piping'). It stops short of explicitly telling the agent when not to use it or naming from_stata as the alternative for Stata expressions, but the context is strong enough to route correctly.

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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