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

The readOnlyHint annotation already declares this as a safe read operation. The description adds valuable behavioral context by detailing what the tool returns, including refusals, working examples, access limits, and provenance fields. No contradictions with annotations.

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?

Two compact sentences: the first front-loads the core purpose and outputs, the second provides usage guidance. Every phrase contributes meaning, and there is no redundancy or filler.

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 single-parameter, read-only helper tool with no output schema, the description covers purpose, output contents, and usage workflow. It is self-contained and sufficient for an agent to invoke it correctly.

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?

The input schema already provides full coverage for the single parameter ('Exact tool name from tools/list'). The description reinforces this by mentioning tools/list but adds no new semantic detail beyond the schema's description.

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 it provides 'blind-caller guidance for one tool' and enumerates the specific information returned (when to use, refusals, example, limits, outputs, etc.). This distinguishes it from sibling data-access tools like get_catalog or query_dataset.

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?

Explicitly instructs to 'Use tools/list to discover names, then call this before an unfamiliar tool.' This gives a clear when-to-use directive and the prerequisite step, making the usage context unambiguous.

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