update_analysis

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

With no annotations provided, the description carries the full burden of disclosing behavior. It lists common update scenarios but does not explain partial-update semantics, whether existing fields are preserved, required permissions, reversibility, or what happens with invalid or unknown update keys. For a mutation tool, this leaves significant behavioral ambiguity.

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 concise, front-loaded with the core purpose, and followed by a compact use-case list. Every sentence adds useful information and there is no redundancy or filler. It is ideally sized for an AI agent to process quickly.

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 tool has no output schema, no annotations, and a low-coverage schema, so the description must carry more weight. While the use cases help, the description does not specify return behavior, error cases, whether updates replace or merge the existing object, or constraints on the free-form updates object. This is incomplete for an update operation with free-form input.

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 0%, so the description must compensate. It partially does by giving concrete example fields such as PR link, commit hash, verdict, and confluence_page_ids, which helps infer what the free-form 'updates' object may contain. However, it does not clarify expected key names, value formats, or how ticket_id is used beyond being a required identifier.

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 a specific verb and resource: 'Update fields on an existing analysis record.' It distinguishes from create/store and read/delete siblings by emphasizing 'existing' and listing update-specific use cases. However, it does not explicitly name or contrast the closest sibling, store_analysis, leaving some differentiation to inference.

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 'Common use' examples imply when to call this tool: after a fix is merged, after further investigation, or when enriching with confluence_page_ids. These examples provide contextual guidance but no explicit when-not-to-use guidance or alternatives. The usage conditions are implied rather than stated as rules.

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