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

With no annotations, the description carries the full burden and does meaningful work: it discloses that returned text is 'verified' and may include warnings, scope, provenance, and version/OS metadata. It does not cover failure behavior for invalid IDs or any rate limits, which is the remaining gap.

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?

Three tight sentences with no filler; the core action and ID format lead, followed by how to use the returned data. The grounding directive is placed where it matters.

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?

No output schema exists, so the description must describe returns, and it does so thoroughly (verified text, warnings, model scope, official URL, version/OS metadata, source provenance). For a single-parameter fetch tool this is essentially complete.

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 100% and the single id parameter already documents the exact pattern and examples, so the description's restatement of the model:<id>/resource:<id> form adds little beyond the schema. Baseline 3 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?

States a specific verb (Fetch) and resource (full verified text and metadata for one result returned by search_support), plus the ID namespace it operates on. It is clearly distinguishable from the sibling search_support, which produces the IDs this tool consumes.

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?

Establishes the workflow context: it consumes IDs from search_support and its output is the grounding source for technical answers, with a clear directive against substituting nearby models or inventing versions. No explicit when-not-to-use case, but the sequencing is unambiguous.

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