tascan_recommend_fix

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

Annotations are generic (readOnly=false, destructive=false), so the description adds meaningful behavioral context: the recommendation is 'persisted in the AI audit trail' and the tool 'accepts' both text and structured recommendations for auto-dispatch. This clarifies the write side-effect and audit trail integration beyond what annotations state. No contradiction found.

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 compact—three sentences—with the core action and protocol step presented first. Every sentence earns its place: action, parameter flexibility, and persistence behavior. No fluff or redundancy.

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 write-only tool with a nested structured parameter and no output schema, the description sufficiently covers the purpose, optional vs required inputs, and the audit-trail persistence. It could mention response behavior, but the absence of an output schema lowers that burden. The protocol step context further helps the agent decide when to invoke.

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%, giving the baseline of 3. The description adds value by explaining that structured_recommendation contains 'task definitions for auto-dispatch,' which clarifies the semantic purpose of that complex nested parameter beyond the raw format in the schema. This extra context elevates the score.

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 uses a specific verb ('Post') and identifies the exact resource ('AI-generated recommendation to an issue thread'), naming it as 'Step 2 of the Closed-Loop Autonomous Operations Protocol.' This makes the tool's role clear and distinguishes it from sibling tools like tascan_analyze_issue or tascan_auto_resolve by focusing on issuing a recommendation rather than analyzing or directly executing.

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 statement 'Step 2 of the Closed-Loop Autonomous Operations Protocol' provides clear contextual when-to-use guidance, implying it follows issue analysis and precedes auto-dispatch. However, it does not explicitly state when not to use this tool or name alternative tools, leaving minor ambiguity.

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