fetch_gcp_service_docs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: extracting content from cloud.google.com, converting HTML to Markdown, prioritizing sections, removing non-content elements, and handling various input formats. It also mentions truncation based on max_bytes. However, it lacks details on error handling or rate limits.

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 well-structured with clear sections (purpose, usage guidelines, advantages, supported inputs, args, returns, example) and uses bullet points for readability. It is appropriately sized for the tool's complexity, though it could be slightly more concise by integrating some bullet points into prose.

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?

Given the tool's moderate complexity (2 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, usage, behavioral traits, parameter details, and return format. However, without an output schema, it could benefit from more detail on the JSON structure returned (e.g., specific fields like 'content', 'size', 'url').

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 0%, so the description must compensate. It fully explains both parameters: 'service' with examples (e.g., 'Cloud Storage', 'vertex ai', 'gke audit') and acceptable formats (exact names, abbreviations, multi-word queries), and 'max_bytes' with its default value (20KB) and purpose (controlling content size). This adds significant meaning beyond the bare schema.

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 the tool fetches documentation content for GCP services, specifying the verb 'fetch' and resource 'GCP service documentation'. It distinguishes from siblings like 'search_gcp_services' by focusing on retrieving actual content rather than searching, and mentions specific advantages over generic tools like curl or WebFetch.

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 includes explicit 'USE THIS WHEN' and 'BEST FOR' sections that detail when to use this tool (for detailed documentation, guides, tutorials, or API references) and why it's better than alternatives (e.g., curl or WebFetch). It also lists specific use cases like handling multi-word service names and common abbreviations.

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