guess_datetime_url

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the analysis methods and output format (timestamp with confidence scores), adding value beyond the input schema, but doesn't cover error handling, rate limits, or performance characteristics. It's adequate but lacks depth for a complex analysis tool.

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 appropriately sized and front-loaded, starting with the core purpose and followed by details on methods and output. Every sentence adds value, though it could be slightly more streamlined by reducing the list of methods without losing clarity.

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 tool's complexity (analyzing multiple data sources) and no annotations or output schema, the description is moderately complete—it explains the purpose, methods, and output format. However, it lacks details on confidence score interpretation, error cases, or limitations, leaving gaps for an AI agent to fully understand behavioral nuances.

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%, with the single parameter 'url' well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema, but the baseline is 3 since the schema adequately covers the parameter 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 clearly states the specific verb ('guess') and resource ('last updated or published datetime of a web page'), distinguishing it from sibling tools like 'read_url' or 'parallel_read_url' that fetch content rather than analyze temporal metadata. It explicitly identifies what the tool does without being tautological.

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 detailing the methods used (e.g., HTTP headers, HTML metadata), suggesting it's for web page analysis, but doesn't explicitly state when to use it versus alternatives like 'read_url' for general content extraction. It provides clear intent but lacks explicit sibling differentiation or exclusion criteria.

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