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

With no annotations, the description carries the full behavioral burden and does so well. It discloses that posts are video/images behind tokenised share links, that a long video may not finish in one call, and that the agent should poll with job_id to collect the result. This goes far beyond the schema.

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, then efficiently covers platform support, output contents, exclusions, and the async retry pattern. No sentence is wasted; the length is justified by the non-trivial job_id behavior.

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 having no output schema, the description adequately explains what the tool returns and how to handle incomplete results. It also covers platform limitations and the polling workflow, making the tool usable end-to-end from a single description.

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 baseline is 3. The description adds extra meaning by clarifying that url includes share tokens and, more importantly, that job_id must be used instead of url to avoid restarting the work. That is genuine semantic value beyond the schema's field descriptions.

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 ('Turn a social media URL into LLM-ready context') and immediately names the supported platforms and the concrete outputs (transcript, on-screen text, image descriptions, caption, metadata). It also explains why this tool exists — content behind tokenised share links — which fully clarifies its purpose.

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?

Gives explicit direction: 'Use this whenever you are given a social media link.' It also lists unsupported platforms (Bilibili, Instagram, Facebook) and explains the job_id workflow for long videos, including the instruction to avoid re-sending the URL. This is strong when-to-use guidance even without sibling tools.

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