cpp_random_effects

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

No annotations, no output schema, and the description says nothing about required input shape (effect sizes, standard errors, study IDs), failure modes, or what the 'approximate interval' is at (e.g., 95%). It does imply leave_one_out triggers per-study exclusion, but that is the only behavioral disclosure.

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?

Extremely short and front-loaded, with zero filler, but it is a noun-phrase list rather than structured prose, so the terseness tips into under-specification rather than clarity.

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?

For a statistical estimator with no annotations, no output schema, and zero parameter documentation, the description is far too thin; an agent cannot reliably construct the studies payload or interpret the returned interval.

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 coverage is 0% with 2 parameters. The description only gestures at leave_one_out ('leave-one-out'); the required studies array — the critical parameter — is left entirely undefined as to expected fields, units, or structure.

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?

Names the statistical method (DerSimonian-Laird) and the quantities produced (Q, I², tau², interval), which lets an agent infer this computes a random-effects meta-analysis. But there is no verb or resource statement, and the sibling cpp_fixed_effect_meta is never contrasted, so the agent must infer which model to pick.

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?

No when-to-use guidance and no mention of when to prefer this over cpp_fixed_effect_meta or the other statistical siblings. The agent gets no criteria (e.g., heterogeneity) for choosing the random-effects model.

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