Transcript

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

Adds substantial context beyond annotations: AI processing latency, potential paid credit consumption, confirm=true requirement, and that read-like POST requests do not publish to social platforms. It also states the return shape. Annotations already flag not read-only and open-world, but the description enriches the operational and cost profile.

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?

Front-loaded with purpose, then constraints, return value, performance note, and credit/confirm requirements. Every sentence adds a distinct operational fact with no filler.

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 rich input schema and absence of an output schema, the description covers purpose, input constraints, side effects, return shape, and confirmation needs. Nothing critical for correct invocation is missing.

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 coverage is 100%, so the schema already documents all four parameters. The description adds the non-schema constraint that the URL must point to a Twitter video tweet under 2 minutes and reinforces the confirm=true requirement, though it does not elaborate on account or cache_max_age.

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?

States a specific verb and resource: extracts a transcript from a Twitter video tweet using AI. It clearly distinguishes itself from sibling transcript tools such as youtube_transcript, facebook_transcript, and instagram_transcript by platform and input type.

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 clear constraints: the video must be under 2 minutes, the endpoint is slower, and confirm=true is required. It does not explicitly name alternatives or when-not-to-use cases, but the stated prerequisites effectively scope usage.

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