create_bug

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

With no annotations, the description carries the full burden. It discloses dry-run vs live behavior and multi-provider support, but misses critical details like authentication requirements, rate limits, or confirmation steps (e.g., the 'confirmed' parameter). A score of 3 reflects partial transparency.

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 a single, short sentence that fully captures the tool's primary purpose and key features. It is front-loaded with essential information and contains no filler.

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 14 parameters, nested objects, no output schema, and no annotations, the description is insufficient. It does not explain return values, error handling, dry-run outcomes, or the interaction between parameters like 'autoCreateBug' and 'confirmed'. The complexity demands a more complete description.

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 only 7%. The description adds minimal parameter meaning beyond the schema—only referencing 'from a failed test' which corresponds to required parameters. Many optional parameters (e.g., priority, severity, provider) remain unexplained. The description does not compensate for the low coverage.

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 creates a bug ticket from a failed test, specifies three supported platforms (GitHub, Jira, Azure DevOps), and mentions dry-run/live modes. This distinguishes it from sibling tools like generate_test_cases.

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 after a failed test but does not provide explicit guidance on when to choose this tool over alternatives or when not to use it. It lists supported providers but no criteria for selecting among them.

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