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

readOnlyHint=true already establishes this is a safe read, so the description is not carrying the full burden. It adds one useful behavioral detail — the image is sent in the POST body — but says nothing about what the neural network returns (labels only? boxes? confidence?) or any size/latency constraints 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?

Two short sentences, purpose front-loaded. The second sentence about the POST body is marginally useful rather than essential, but nothing is bloated.

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 single-parameter detection tool with no output schema, the description is functional but leaves the return shape undefined. An agent cannot tell whether it gets class labels, bounding boxes, or confidence scores, which is meaningful missing context for a detection tool.

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 the single 'image' parameter is fully documented with format and dimension constraints. The description's only addition, that the image goes in the POST body, is transport detail already implied; baseline 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?

States a specific verb+resource: 'Detects and labels the objects present in an image using a neural network.' This clearly distinguishes it from detectFaces and extractTextFromImage by the resource (generic objects vs faces vs text), though it never names those siblings explicitly.

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?

No when-to-use or when-not-to-use guidance is given. With close siblings like detectFaces and extractTextFromImage available, the description should say which task this covers, but it offers no routing cues or exclusions.

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