generate_report_from_text

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

With no annotations, the description carries the full burden. It discloses the automatic section detection behavior and the output being a structured PDF. However, it does not explain how the PDF is returned (e.g., file path or download link), any side effects like file creation, or limitations on input size/format, leaving gaps in 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 concise, two sentences, and front-loaded with the core action. Every phrase adds value, and it avoids redundancy with the schema.

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 no output schema and no annotations, the description is only moderately complete. It covers the main function and input, but omits details about the return value, how the tool differs from sibling 'generate_report', and any edge cases or preconditions. For a 6-parameter tool, more context would be needed for full autonomous invocation.

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 100%, so the baseline is 3. The description adds the concept of 'basic metadata' (mapping to author, company, theme, classification) and notes auto-detection, but does not provide additional parameter-specific semantics beyond what the schema already details.

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 the specific verb 'Convert' and clearly identifies the resource: 'raw text or LLM chat output' transformed into a 'structured PDF report'. This directly distinguishes it from siblings like 'generate_report' by focusing on unstructured text input and automatic section detection.

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 clear context: use when you have unstructured text or LLM output to convert into a PDF report. It does not explicitly name alternatives or exclusions, but the phrase 'Pass unstructured text and basic metadata' gives actionable guidance on when this tool applies.

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