get_thread

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

Without annotations, the description carries the burden of transparency. It discloses the exact return shape (root and replies structure), which is valuable behavioral information. It doesn't address edge cases like missing tweets or truncated replies, but the details provided are sufficient for a read-only operation.

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 two sentences, front-loaded with the purpose and output shape, followed by practical use cases. No unnecessary text.

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 simple get tool with one parameter and no output schema, the description provides the full output structure and use cases. It doesn't mention pagination or limitations, but these are not critical for basic usage.

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 no description for tweet_id, and the tool description only implies it's the tweet identifier. It doesn't specify the format or how to obtain the ID. With 0% schema coverage, the description should compensate more explicitly.

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 tool retrieves a tweet's engagement and replies, with a specific verb and resource. It distinguishes from siblings like get_tweet by focusing on engagement metrics and reply content.

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?

It provides explicit use cases: 'Use this to check likes on a tweet or read the replies people left.' This gives clear context, though it doesn't mention exclusions or alternative tools.

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