analyze_draft_content

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 mentions that the tool returns 'content analysis with score, suggestions, and recommended hashtags,' which gives some behavioral insight. However, it lacks critical details: whether this is a read-only operation, if it has rate limits, what permissions are needed, or how the analysis is generated. For a tool with no annotations, this is insufficient.

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 well-structured and front-loaded: the first sentence states the purpose clearly. The 'Args' and 'Returns' sections are efficient, though they could be integrated more seamlessly. There's minimal waste, but it's slightly verbose with separate sections instead of a flowing paragraph.

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 complexity (analysis tool with 2 parameters, no annotations, but an output schema exists), the description is moderately complete. It covers the purpose and parameters but lacks behavioral details like error handling or usage context. The output schema likely documents return values, so the description doesn't need to explain those, but it should address more operational aspects.

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. It explains 'content' as 'Draft post content to analyze' and 'industry' as 'Optional industry for targeted hashtag suggestions,' adding meaningful context beyond the bare schema. However, it doesn't detail format constraints (e.g., length limits for content, valid industry values), leaving gaps. With 0% coverage, this is adequate but not comprehensive.

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 purpose: 'Analyze draft content and get suggestions for improvement.' It specifies the verb ('analyze') and resource ('draft content'), and distinguishes it from siblings like 'analyze_content_performance' or 'analyze_engagement' by focusing on draft content rather than published content. However, it doesn't explicitly differentiate from 'analyze_my_content_performance' which might also involve drafts, so it's not a perfect 5.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'analyze_content_performance' or 'analyze_engagement', nor does it specify prerequisites or exclusions. The only implied usage is for draft content, but that's already covered in purpose clarity.

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