analyze_content

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

Annotations already provide readOnly/idempotent/non-destructive hints, so the description's burden is lower. It adds useful behavioral context beyond annotations: the 3-credit cost and the constraint that it accepts inline text rather than a URL. It does not describe the output structure, but the listed metrics partially imply what will be returned.

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 dense but every sentence earns its place: when to use, what it analyzes, input format, exclusion, cost, and a concrete usage example. It is front-loaded with the core purpose and ends with the example, which is ideal.

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 read-only, idempotent analysis tool, the description covers the key operational details: input expectations, cost, example invocation, and exclusionary context. The main gap is the lack of an explicit return-shape statement, but the absence of an output schema is partially mitigated by the listed analysis metrics.

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 coverage is 100%, with text described as 'The text content to analyze'. The description adds concrete meaning by showing example option keys (extractTopics, includeSentiment) and clarifying that text is the raw content, not a URL. This compensates for the empty options object in the 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 analyzes text with NLP metrics (language detection, sentiment, topic extraction, entity recognition, readability) for content auditing and classification. It explicitly distinguishes itself from page-reading tools by saying 'Takes text, not a URL', separating it from scrape and related siblings.

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?

It gives explicit when-to-use guidance ('NLP metrics on text you already hold') and when-not-to-use guidance ('Not for reading a page'), even naming the exact alternative path: 'scrape returns the markdown to pass in'. This provides actionable routing to the agent.

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