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

Annotations already indicate idempotent, non-destructive, and readOnlyHint false. The description adds valuable context by disclosing that server_side_ai triggers a POST to the Anthropic API, explaining why the tool is not purely read-only and highlighting potential external side effects. No contradiction with annotations exists.

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?

Three sentences each contribute useful information: protocol context, retrieval details, and the server-side AI capability. It is efficient, though the protocol framing at the start delays the core verb; overall it is appropriately sized and structured.

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?

With no output schema, the description gives a reasonable summary of the returned data (worker info, message thread, project history, similar issues) and explains the behavior for both default and server_side_ai modes. It covers the tool's complexity well, though exact response formatting is not detailed.

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 baseline is 3. The description reinforces the server_side_ai parameter's behavior (raw data vs. direct AI analysis) but does not introduce new semantic meaning beyond what the schema already provides.

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 explicitly states the tool retrieves full issue context, listing specific data types (worker info, message thread, project history, similar issues), and also mentions the server-side AI analysis option. It clearly distinguishes itself from sibling tools like list_issues or auto_resolve by framing it as Step 1 of the Closed-Loop Autonomous Operations Protocol.

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 usage context by labeling it as 'Step 1 of the Closed-Loop Autonomous Operations Protocol' and states its intended purpose ('Use this data to reason about the root cause and generate a remediation plan'). However, it does not explicitly mention alternatives or when not to use this tool, so it lacks full exclusion guidance.

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