generate_report

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 discloses the output format (base64-encoded PDF) and supported content types, but omits details like filesystem dependencies for image/logo paths, failure behavior on invalid specs, or potential side effects. This is partial transparency — core behavior is covered, but important operational traits are missing.

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 sentences that are fully front-loaded with the primary verb and resource. Every clause earns its place — the input format, content summary, and output encoding are all covered with no filler or redundancy.

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?

This is a complex tool with a deeply nested, undocumented schema, no output schema, and no annotations. The two-sentence description covers the return value but not the substantial context an agent needs to build a valid `spec`: required keys, `after_section` semantics, theme expectations, default behaviors (page_size, orientation), or filesystem prerequisites for images/logo. The description is far from complete relative to the tool's complexity.

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 for the single complex `spec` parameter. It adds value by naming the top-level content types (metadata, sections, tables, charts, images), which helps an agent envision the spec's shape. However, it doesn't explain required sub-structure (e.g., metadata requires a title, sections require heading/body, theme uses RGB color arrays), so the compensation is incomplete.

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 uses a specific verb ('Generate') and resource ('professional PDF report'), and specifies the exact input format ('structured JSON specification') with supported content types (metadata, sections, tables, charts, images). The phrase 'structured JSON specification' differentiates it from the sibling generate_report_from_text, which presumably handles unstructured text input.

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 phrase 'from a structured JSON specification' implies the tool is the right choice when structured JSON input is available, but it never explicitly states when to use it versus generate_report_from_text, nor does it mention exclusions or preconditions. The usage context is implied rather than explicitly stated.

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