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

Beyond the annotations, the description discloses key interaction behavior: this tool is not just a simple API call but initiates a browser-based flow, returns a dashboard URL, requires ending the turn, and should not be followed by other tool calls until the user signals completion. This is the kind of behavioral context annotations alone do not convey.

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 compact yet information-dense. It front-loads the core purpose, then packs essential operational warnings — end the turn, don't call other tools, respect llmContext — into a few well-structured sentences. No sentence is wasted.

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 interaction complexity, the description covers purpose, return value, user flow, tool ordering, and result handling. The output schema exists, so detailed return fields need not be repeated. The description is complete enough for an agent to invoke and manage this 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%, and each parameter (message, maxFiles, environment, workspaceId) already has a clear schema description including examples, defaults, and grant requirements. The tool description does not add additional parameter-level meaning, so the baseline score of 3 is appropriate.

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 states a specific action — 'Create an upload link for the user to add files from their computer' — and clarifies the overall flow: returns a dashboard URL, the user uploads in the browser, and files land in the target workspace. It also distinguishes this tool from the sibling get_file_upload by explicitly instructing the agent not to call that tool until upload is complete.

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 when-to-use guidance: 'Call this last in your turn: write the returned link in your reply, then END THE TURN'. It also names the alternative to avoid (get_file_upload) and the condition for continuing, and instructs the agent to follow any llmContext guidance in results. This leaves little ambiguity about ordering and turn-taking.

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