Get Performance Bottlenecks

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

With no annotations provided, the description discloses key behaviors: it fetches Insights data per workflow and per job, and sorts by total_credits_used. This adds valuable context beyond a mere operation, though it lacks mentions of rate limits or permissions.

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 two sentences long, front-loading the core purpose in the first sentence and providing usage context in the second, with no wasted words.

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?

While lacking an output schema, the description explains the sorted result based on total_credits_used, which is sufficient for an agent to understand the tool's return. It could specify output structure but remains adequate.

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%, so the schema already documents each parameter. The description adds high-level purpose but does not elaborate on parameter semantics beyond what the schema provides, meeting the baseline.

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 starts with a specific verb-resource pair ('Identify the slowest and most credit-intensive jobs') and clearly distinguishes this tool from siblings like get_config, get_dag, etc., which cover other aspects of a project.

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 states concrete use cases ('find resource_class over-provisioning, unnecessary serialisation, and credit hot-spots'), providing clear guidance on when to employ the tool, though it omits explicit when-not-to-use scenarios.

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