assign_expert

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

With no annotations, the description carries full burden. It describes analysis and selection but does not disclose whether the tool performs an actual assignment (side effect) or is read-only. No information about required permissions, data persistence, or side effects is provided.

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 sentence that is straightforward and free of unnecessary words. It could benefit from a more structured format (e.g., bullet points) but is not verbose.

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?

The description covers the core functionality but lacks usage context and behavioral details. Given an output schema exists, return values are likely documented elsewhere, but the absence of when-to-use guidance and side-effect disclosure leaves gaps for an AI agent.

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?

The sole parameter 'description' has 0% schema description, so the tool description's mention of 'problem description' clarifies its purpose. However, no additional constraints like expected length, format, or examples are given, meaning the description adds minimal value beyond the parameter name.

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 analyzes a problem description, classifies it into categories, and chooses an expert. This gives a specific verb-resource pairing. However, it does not explicitly differentiate from sibling tools like 'add_issue' or 'ai_try_solve', missing context on how assignment differs from issue creation or AI solving.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, scenarios where this tool is appropriate, or cases where another sibling tool should be used instead.

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