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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits like multi-page support and lists extracted data fields, but omits rate limits, authentication requirements, and error handling behavior.

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?

Single, dense sentence front-loaded with the action verb and comprehensive output list. Zero redundancy—every word contributes to understanding the tool's scope and differentiator (Diffbot provider).

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 exists, the description effectively compensates by enumerating the extracted fields (text, author, date, tags, sentiment, images). With 100% input schema coverage and no annotations, this provides sufficient context for 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?

Input schema has 100% description coverage, establishing a baseline of 3. The description adds marginal value by contextualizing the 'paging' parameter through the 'multi-page support' mention, though parameter specifics are adequately covered by the schema itself.

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 explicitly states the extraction targets (article text, author, date, tags, sentiment, images) and resource type (blog or news URL), distinguishing it from sibling tools like diffbot.products.extract (products) and diffbot.pages.analyze (general page structure).

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 implies usage context by specifying 'blog or news URL,' indicating when to use this tool, but lacks explicit guidance on when to prefer alternatives like diffbot.pages.analyze or exclusions for non-article content.

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