agentic_issue_analyze

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. While it mentions the tool performs AI analysis and automatic label assignment, it doesn't describe what happens during execution - whether it makes changes to the issue, what permissions are required, whether it's a read-only analysis tool or modifies the issue, what happens if analysis fails, or what the typical response looks like. For a tool that appears to modify issues (label assignment implies mutation), this is a significant gap in behavioral 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 extremely brief (one phrase) and mixes Japanese and English terms ('IssueAgent実行', 'Label自動付与'), which may hinder clarity for English-only agents. While concise, it's arguably too brief - it doesn't provide enough context for the tool's purpose and behavior. The information is front-loaded but insufficiently detailed.

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 tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, what side effects it has (particularly important given it mentions 'automatic label assignment'), or provide enough context about the AI analysis process. The description leaves too many unanswered questions about how the tool behaves and what results to expect.

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 schema has 100% description coverage, with all three parameters clearly documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema - it doesn't explain how the parameters interact, provide examples of valid inputs, or clarify any constraints not captured in the schema. With complete schema coverage, the baseline score of 3 is appropriate.

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 states the tool performs 'AI analysis' and 'automatic label assignment' on issue content, which provides a general purpose. However, it's somewhat vague about the specific nature of the analysis (e.g., what type of AI analysis, what labels are assigned) and doesn't clearly distinguish this tool from potential siblings like 'agentic_review_execute' which might also involve issue analysis. The description mixes Japanese and English terms, which could cause confusion.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or specific scenarios where this tool is appropriate compared to sibling tools like 'agentic_review_execute' or 'agentic_pr_create'. There's no indication of when NOT to use this tool or what alternatives exist for similar functionality.

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