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vikranthviki

Causal Decision Agent

by vikranthviki
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Python
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Apply segmented regression to interrupted time series data, estimating changes in level and trend after an intervention, with validated evidence tier for causal decision support.

Instructions

Segmented regression for interrupted time series. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yYesOutcome column.
timeNoTime column. If None, uses row index 0..n-1.
alphaNoSignificance level for confidence intervals and tests.
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
hac_lagNoNewey-West truncation lag.
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_pathYesAbsolute 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.
data_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
interventionNoTime index (integer row position) at which the intervention begins. Required.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
seasonality_periodNoPeriod P of Fourier seasonal terms (e.g. 12 for monthly data with annual cycle). If None, no seasonality is added.
seasonality_harmonicsNoseasonality_harmonics parameter (int).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

The annotation readOnlyHint=true already indicates a safe read-only operation. The description adds a cryptic validation note about evidence tiers, which does not explain tool behavior, side effects, or requirements. It does not contradict the annotation but adds little beyond it.

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 very brief and front-loaded with the core purpose. The second sentence on validation is concise but its relevance is unclear. Overall, it is efficient with no wasted words.

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?

For a complex tool with 13 parameters and no explicit output schema explanation, the description is notably thin. It does not explain key concepts like the intervention parameter, the role of time, or how the validation tier affects usage. The description is insufficient for an agent to understand the full scope of the tool.

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 13 parameters are already documented in the schema. The description adds no extra parameter semantics, which aligns with the baseline of 3 when the schema is fully descriptive.

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 clearly identifies the tool as segmented regression for interrupted time series, which is a specific statistical method. It is distinct from many siblings like arima or event_study, though it does not explicitly name alternatives. The purpose is clear but lacks explicit differentiation.

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. The description does not mention any conditions, exclusions, or alternative tools. An agent would have to infer usage from the name and general domain knowledge.

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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