pylon_get_issue_messages

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns conversation history with timestamps and sender info, which is useful behavioral context. However, it lacks details on potential limitations like pagination, rate limits, authentication requirements, or error conditions, leaving gaps for a read operation with no annotation coverage.

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 front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value (e.g., specifying return content and usage guidance), with no redundant or wasted words, making it efficiently structured and appropriately sized.

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?

Given the tool's moderate complexity (a read operation with one parameter) and no annotations or output schema, the description is adequate but incomplete. It explains what the tool does and its purpose, but lacks details on output format (beyond high-level content), error handling, or system constraints, which could hinder an agent's ability to use it effectively without trial.

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 input schema has 100% description coverage (the issue_id parameter is fully documented), so the baseline is 3. The description does not add any parameter-specific information beyond what the schema provides, such as format constraints or examples, but it doesn't need to compensate for gaps since schema coverage is high.

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 specific action ('Get the conversation history') and resource ('for a specific support issue'), distinguishing it from sibling tools like pylon_get_issue (which likely returns issue metadata) or pylon_search_issues (which searches across issues). It explicitly mentions the content returned ('all messages between customer and support team, including timestamps and sender information'), making the purpose unambiguous.

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 clear context for when to use this tool ('to understand the context and progress of an issue'), which implicitly suggests it's for detailed message retrieval rather than high-level issue overviews. However, it does not explicitly state when not to use it or name specific alternatives among the siblings (e.g., pylon_get_issue for basic issue details), which prevents a perfect score.

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