get_mandatory_sections

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 adds useful behavioral context: the list is fixed, mandatory, and must be used first. It also gives content guidelines (no inventing facts, etc.). However, it does not explicitly disclose read-only status, authentication requirements, or error behavior, which would be expected for a tool with no annotation support. The content guidance is more about the downstream generate_proposal, so it adds some but not complete behavioral transparency.

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, with the core purpose in the first sentence and usage guidance in the second. It is front-loaded, contains zero fluff, and every sentence contributes actionable information. This is a model of concise tool description.

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 simplicity (no parameters, output schema exists), the description covers the essential context: what is returned, its mandatory nature, and the usage workflow. It also adds valuable content guidelines. It could mention error conditions or output format details, but an output schema presumably covers the latter. Overall, it is complete for an agent to call this tool 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 tool has zero parameters, so per the baseline rule the score is 4. The description adds meaning beyond the empty schema by clarifying the return value's structure (fixed list and order), which is the only semantic content needed. No parameter explanations are necessary.

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 states a specific verb ('Returns') and resource ('the fixed list and order of sections every proposal MUST contain'), clearly distinguishing it from siblings like list_templates or get_company_profile. It precisely defines the tool's scope without ambiguity.

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 explicitly instructs to 'Call this first' and lays out the workflow: draft sections from client brief and tool results, then make one generate_proposal call. This provides clear when-to-use context and differentiates from the generate_proposal sibling by sequencing the calls.

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