Causal Decision Agent

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rdplacebo

Read-only

Validate regression discontinuity designs by testing placebo cutoffs at alternative thresholds, identifying spurious effects.

Instructions

Placebo cutoff test for RD validity.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`c`	No	True cutoff.
`p`	No	p parameter (int).
`x`	Yes	Outcome and running variable names.
`y`	Yes	Outcome and running variable names.
`ax`	No	ax parameter (Optional[Any]).
`side`	No	Which side of the true cutoff to place placebos: 'left', 'right', or 'both'.	both
`alpha`	No	Significance level for confidence intervals and tests.
`fuzzy`	No	Treatment variable for fuzzy RD.
`detail`	No	Payload 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
`kernel`	No	Kernel function used for weighting or smoothing.	triangular
`figsize`	No	figsize parameter (Tuple[float, float]).
`as_handle`	No	If 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_path`	Yes	Absolute 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://.
`n_placebo`	No	Number of placebo cutoffs if auto-generating.
`result_id`	No	Optional 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_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`placebo_cutoffs`	No	Explicit placebo cutoff values. If None, auto-generates from the data distribution.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

C2.8/5.0

Behavior2/5

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

The annotations mark this as read-only, and the description's 'test' wording aligns with that. However, the description discloses no additional behavior such as auto-generating placebo cutoffs when none are supplied, how the running variable is handled, or what the output contains. With annotations already covering the read-only guarantee, the description adds little transparency.

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 one-line description is undeniably concise and front-loaded, with no wasted words. Yet it is too brief to serve as a standalone guide for a tool with 18 parameters, so it earns only a mid score.

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?

Despite having an output schema, the description omits when-to-use, required input roles, and interpretation of results. For a complex RD diagnostic with 18 parameters, this is not enough context to call correctly without supplementing 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%, and the description offers no parameter-specific explanations beyond what the schema already provides. For example, it does not mention how placebo_cutoffs or n_placebo relate to the test. Baseline 3 is appropriate because the schema carries the semantic load.

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 identifies the tool as a placebo cutoff test for RD validity, a specific diagnostic within RD analysis. It is distinct from estimation (rdrobust) or bandwidth selection (rdbwselect), though it does not explicitly name alternatives. The purpose is clear but relies on domain knowledge.

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 provides no guidance on when to call this tool versus other RD diagnostics such as rdrandinf or rddensity. It does not mention preconditions, ordering, or what question it answers. An agent would need to infer usage from the tool name alone.

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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