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

Annotations provide readOnlyHint=true. The description adds value by specifying that the data is public, sanitized, and sourced from a published mirror, and it discloses a behavioral detail: returns availability=false if no dataset was baked. This enriches the agent's understanding without contradiction.

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 two sentences, front-loading the main purpose and data structure. Every sentence adds value without redundancy.

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 no-parameter tool, the description covers the return structure and a special case (availability=false). It is sufficiently complete given the simplicity, though it omits potential error conditions or additional context.

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?

Input schema has 0 parameters with 100% coverage, so baseline is 4. The description does not need to add parameter semantics and does not mention parameters, which is appropriate.

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 description clearly states that the tool returns public sanitized OPERANT results, specifying the data structure (per-model OCS profiles, headline figures, calibration-profiles). It distinguishes itself from siblings like get_document, get_profile, etc., by focusing on the OPERANT benchmark.

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 implicitly suggests usage for retrieving OPERANT results but lacks explicit guidance on when to use this tool versus alternatives, nor does it specify when not to use it. It does not reference sibling tools or provide context for selection.

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