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vikranthviki

Causal Decision Agent

by vikranthviki

brief

Read-only

Create a compact brief of a fitted statistical result to inform next actions, with configurable detail from point estimate to diagnostics. Cache results to chain subsequent calls.

Instructions

One-line agent-friendly brief for a fitted result. Cheaper than calling brief_result if you already have the result object in scope.

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_idNo
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

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so no mutation warning is needed. The description adds useful cost/behavior context ('Cheaper', 'one-line') and a scope prerequisite, but does not disclose deeper behavior such as caching, data loading, or failure modes, which matters given the tool can also accept data_path and result_id.

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?

The description is two sentences with no filler. It front-loads what the tool does, then adds the key comparative guidance about cost and alternative. Every sentence serves a distinct purpose.

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

Completeness3/5

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

The description is adequate for a tool with rich parameter schemas and an output schema, but it leaves ambiguity around what 'result object in scope' means operationally and how an agent should invoke the tool to access that object. The contrast with brief_result helps, but the core prerequisite is not fully specified.

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 83%, so the parameter documentation already carries most of the burden. The description adds little about parameters, mainly the 'result object in scope' context, which is not directly mapped to any schema property. This is a typical baseline where the schema already explains parameters adequately.

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 clearly states the tool produces a 'one-line agent-friendly brief for a fitted result' and explicitly contrasts it with the sibling 'brief_result', including the condition that selects between them. This is enough for an agent to understand the tool's role and distinguish it from related tools.

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 names the alternative 'brief_result' and gives a concrete condition: use this tool when you already have the result object in scope because it is cheaper. It implies the alternative for other cases, but does not explicitly spell out when not to use it, so it is clear but slightly implicit.

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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