analyze_content

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by enumerating the specific NLP features available (sentiment, topics, etc.) and provides an example demonstrating how to invoke these features, which goes beyond what annotations convey.

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 plus a short example) and front-loaded with the usage guidance. Every sentence serves a clear purpose: stating when to use, what it does, and how to use it. No unnecessary information.

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 NLP analysis tool with 2 parameters and no output schema, the description adequately covers what the tool does and how to use it. However, it could be more complete by briefly noting the expected return format (e.g., returned object structure) to compensate for the missing output schema.

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 input schema covers both parameters with descriptions, but the description adds significant semantic value by listing concrete analysis capabilities and showing an example with options like extractTopics and includeSentiment. This transforms the generic 'options' object into meaningful parameters.

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's function as providing NLP metrics for text, listing specific capabilities like language detection, sentiment, topic extraction, entity recognition, and readability score. This effectively distinguishes it from sibling tools focused on scraping or extraction.

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 states when to use the tool ('when you need NLP metrics for text') and provides an example of usage, giving clear context. However, it does not mention when not to use it or directly name alternatives among the extensive sibling list.

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