rd_forest

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

readOnlyHint=true already signals a safe read operation, and the description adds valuable behavioral context: assumptions (continuity, no sorting, monotonicity for fuzzy designs), pre-conditions, and common failure modes including manipulation and bandwidth sensitivity. Nothing contradicts the annotations. The description goes beyond the safety hint to explain when results may be invalid.

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

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

The description is front-loaded with the core purpose, then organized into labeled sections for assumptions, pre-conditions, failure modes, alternatives, and minimum N. Every sentence carries useful information, though it is somewhat longer than necessary; structure and labeling make it easy to parse.

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

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

With an output schema present, the description need not explain return values. It covers what the method does, when it is appropriate, what must be true for validity, how to diagnose failures, alternatives, and a typical sample-size minimum. It is complete enough for an agent to decide and invoke the tool correctly, though explicit guidance on when to choose this over rdrobust for average effects would be slightly better.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% — all 16 parameters have descriptions in the schema. The tool description adds no parameter-level meaning beyond what the schema already provides, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The opening clause 'Causal Forest for RD -- heterogeneous treatment effect estimation' names the method, the design, and the estimand, making it immediately distinguishable from siblings like rdrobust (local polynomial average effects) and rdbwselect (bandwidth selection). It is specific, action-oriented, and clearly states what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists alternatives (sp.rdrobust, sp.rdrandinf, sp.rdbwselect), states pre-conditions (continuous running variable, known cutoff, sufficient observations), and gives failure-mode guidance with remedial actions (rdplotdensity for manipulation; bandwidth-sensitivity checks). It lacks an explicit decision rule such as 'use this for heterogeneous effects, use rdrobust for average effects,' but the purpose statement strongly implies that.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.