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vikranthviki

Causal Decision Agent

by vikranthviki

lincom

Read-only

Compute linear combinations of coefficients with statistical inference to test hypotheses and validate effects from fitted models.

Instructions

Estimate a linear combination of coefficients with inference. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
alphaNoSignificance level.
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
resultYesFitted model.
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.
expressionYesLinear combination. Examples: - ``"x1 + x2"`` -- beta_x1 + beta_x2 - ``"x1 - x2"`` -- beta_x1 - beta_x2 - ``"2*x1 + 3*x2"`` -- 2*beta_x1 + 3*beta_x2
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

B3.1/5.0
Behavior3/5

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

The readOnlyHint annotation already signals a safe read operation, so the description is not required to restate that. It adds 'with inference' as a behavioral trait and an odd 'Validation: validated evidence tier' note, but it does not disclose operational details like caching behavior or side effects. The description neither contradicts the annotations nor adds substantial behavioral context beyond them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence is concise and front-loaded with the core purpose. The second sentence, 'Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact),' reads like metadata filler and does not help an agent select or invoke the tool, so not every sentence earns its place.

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?

With an output schema, 100% parameter coverage, and readOnlyHint annotations, the structured information is strong and the description can afford to be brief. However, the missing guidance about when to use lincom versus closely related siblings, plus the confusing validation sentence, keeps it at a minimally viable rather than fully complete level.

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 the input schema already documents all nine parameters, including expression syntax examples and detail-level behavior. The description's phrase 'linear combination of coefficients' loosely aligns with the expression parameter but adds no meaning beyond the schema.

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 opens with a specific verb ('Estimate') and a concrete resource ('a linear combination of coefficients'), and 'with inference' clarifies that it produces more than a point estimate. It is clear enough to distinguish lincom from raw estimation tools, though it does not explicitly differentiate it from close siblings like margins or contrast.

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 implies a post-estimation use case (linear combination of coefficients), but it never states when to prefer this over alternatives such as margins, contrast, or rdhte_lincom. There are no explicit exclusions or routing hints, which is a meaningful gap given the large sibling list.

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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