vs_error_logs

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true, so the description does not need to repeat safety info. It adds value by specifying the log content (status codes, IPs, URIs, response times) and parameter behavior, which goes beyond annotations.

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 exceptionally concise: two sentences plus an Args section. It is front-loaded with the core purpose, and every sentence adds value without redundancy or fluff.

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 read-only tool with two parameters (one optional) and an output schema present, the description fully covers what the tool does, when to use it, and parameter details. No additional information is needed for correct 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?

Schema coverage is 0%, so the description carries the full burden. It explains 'vs_name' as 'Virtual Service name' (though minimal) and 'since' with examples like '1h', '30m', '2d' and default '1h', adding semantics beyond the schema's type-only definition.

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 shows 'recent request error logs for a Virtual Service' with specific data fields (HTTP status codes, client IPs, URIs, response times), and its diagnostic use case (5xx errors, latency spikes). It distinguishes from sibling tools like vs_status or vs_analytics by focusing on error logs.

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 says 'Use to diagnose 5xx errors or latency spikes.' This provides a clear use case. It does not mention when not to use or name alternatives, but the specific diagnostic context is sufficient for an AI agent to select correctly among siblings.

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