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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is fully covered. The description adds the return metric list but no additional behavioral context such as authentication requirements or rate limits. No contradiction exists between description and annotations.

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, with the core purpose stated first, followed by return fields and usage triggers. Every sentence contributes useful information with no repetition of schema details.

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?

Despite lacking an output schema, the description explains what the tool returns with a concrete metric list. Combined with full schema coverage and strong read-only annotations, the definition gives an agent everything needed to call this simple analytics tool correctly.

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 both required parameters (tweet_id, companyId) and the optional pipeline_output_id are already documented in the input schema. The description does not add parameter-level meaning beyond what the schema provides, which matches the baseline of 3.

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 and resource: 'Get engagement metrics for a tweet on X (Twitter)', and enumerates exactly which metrics are returned (impressions, likes, retweets, replies, quotes, bookmarks). This clearly distinguishes it from sibling analytics tools like get_x_ads_performance or list_operator_x_posts.

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 gives explicit usage context with example user phrasings ('how did my post do?', 'check my tweet analytics', 'evaluate content performance'). It lacks explicit when-not-to-use guidance or named alternatives, but the trigger conditions are clear enough for an agent to route correctly.

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