get_global_section

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

No annotations are provided, so the description carries the full burden. It discloses the compact tree format, token savings, and the raw option's large-result cache behavior via the schema. However, it fails to explain the meaning of Nbind and Nev, and does not explicitly state that the compact tree is a lossy summary compared to raw JSON, leaving behavioral gaps.

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 three sentences, each contributing essential information: the action and format, the line-by-line output structure, and the intended use case. There is no verbose or redundant phrasing.

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 simple read tool with well-described parameters, the description covers purpose, output format, and usage guidance. The lack of an output schema is compensated by the line-format explanation. Minor gaps around abbreviations and potential lossy nature prevent a perfect score.

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?

Both parameters are fully described in the schema (100% coverage), so the description adds little per-parameter semantics. The line-format explanation provides context about the default output of the raw parameter, but this is a minor enhancement over the schema's descriptions. 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?

The description clearly states the action: 'Get one global section as a COMPACT tree', specifying both the resource and output format. It distinguishes from siblings by emphasizing the compact tree vs. raw JSON and the learning purpose, which is unique among related tools like get_global_section_element or list_global_sections.

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 provides an explicit usage context: 'Use this to learn how a real Header/Footer/block is composed before building your own.' This tells the agent when to choose this tool, though it does not explicitly mention alternatives or exclusion cases, so it falls short of a 5.

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