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vikranthviki

Causal Decision Agent

by vikranthviki
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Python
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rdd

Read-only

Estimate causal effects using sharp or fuzzy regression discontinuity design with flexible data inputs, cutoff, and optional result caching for chained analysis.

Instructions

Sharp / fuzzy RD -- article-friendly alias for :func:rdrobust.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yYesOutcome variable column name or outcome array.
fuzzyNofuzzy parameter (Optional[str]).
cutoffNocutoff parameter (float).
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
runningYesrunning parameter (str).
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.
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

B3.2/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds no behavioral context beyond 'alias for rdrobust'—it doesn't mention side effects, performance, or limitations. With annotations covering safety, this is adequate but not enriched.

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, efficient sentence that communicates the core purpose. It's appropriately front-loaded with the RD type and alias reference, with no wasted words. Though terse, it 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 present, return values don't need explanation. However, for a causal inference tool, the description doesn't mention assumptions, when to prefer sharp vs. fuzzy, or how it differs from the many RD siblings. It's minimally complete but leaves the agent to infer usage context from the schema.

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 coverage is 100%, so all 10 parameters are documented in the input schema. The description adds no parameter-specific meaning beyond what the schema already provides. Baseline 3 is appropriate since the schema does the heavy lifting.

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 states this is an RD estimation tool ('Sharp / fuzzy RD') and identifies it as an alias for rdrobust, which conveys the primary function. It distinguishes itself slightly by the 'article-friendly' qualifier but doesn't fully differentiate from the many other RD sibling tools beyond that.

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 given on when to use this tool versus alternatives like rdrobust or other RD functions. It doesn't mention when sharp vs. fuzzy is appropriate, nor any exclusions. The alias reference implies same usage as rdrobust but doesn't state it explicitly.

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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