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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly mentions the underlying models (Grounding DINO and Bria Eraser), the cost (130 sats per image), the payment flow (Bitcoin Lightning, create_payment prerequisite), and the expected behavior (object erased and filled convincingly). This is substantial, though it does omit potential failure modes or output format.

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 and remains reasonably concise. It includes some technical details (e.g., 'Grounding DINO detection (ECCV 2024)') that are informative but not strictly necessary for an AI agent to select or invoke the tool. Overall, every sentence serves a function: purpose, model context, examples, pricing/auth, and prerequisite.

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 tool's complexity (5 params, no output schema, no annotations), the description covers payment, usage, and cost well, but it does not explain what the tool returns or whether the operation is asynchronous (e.g., whether the agent must poll with await_result or get_job_result). This is a significant gap for an agent that needs to handle the result 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?

The input schema already covers all 5 parameters with descriptions (100% coverage), so baseline is 3. The description adds value by giving concrete examples for the 'query' parameter ('person', 'car', 'watermark') and explicitly linking 'paymentId' to the create_payment tool. It does not address box_threshold or text_threshold, but these are already well-defined in the schema.

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 clearly states the tool's function: removing unwanted objects from images using natural language descriptions, with no mask needed. It specifies the action ('remove'), the resource ('objects from images'), and the input method ('describing what to remove'). This distinguishes it from sibling tools like remove_background, which targets the background rather than arbitrary objects.

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 provides clear usage context: describe the object to remove (with examples 'person', 'car', 'watermark'), pay via create_payment with toolName='remove_object', and no API key or signup needed. However, it does not explicitly contrast with alternative image-editing tools in the sibling set, such as edit_image or deblur_image, so the agent must infer when this tool is specifically appropriate.

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