Remove an image's background, return a transparent PNG URL. $0.03 USDC/call. Paid: calling without a payment payload returns a structured 402 with payment options — that response is the documented behavior, not a failure.
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | Public image URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses a critical behavioral trait not covered by the sparse annotations (only destructiveHint: false): the payment requirement and the documented 402 response for unpaid calls. This adds significant value beyond the annotation, though it doesn't mention other potential behaviors like image size limits or failure modes.
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 two concise sentences, front-loaded with the core function and immediately followed by the crucial payment note. Every sentence earns its place, with no redundant or filler words.
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 low complexity (one parameter, no output schema), the description covers all essential aspects: the operation, the return value (transparent PNG URL), and the payment behavior with a clear explanation of the 402 response. It is sufficiently complete for an agent to correctly invoke the 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?
The input schema has 100% coverage for the single parameter 'image_url' with a clear description ('Public image URL.'). The tool description adds no additional meaning about the parameter format or constraints, so the schema bears the full burden. Baseline of 3 applies.
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 with a specific verb ('Remove') and resource ('an image's background'), and specifies the output format ('transparent PNG URL'). This unambiguously distinguishes it from all sibling tools, none of which perform image processing.
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 implies usage by stating the core function, and there are no competing sibling tools for background removal. However, it does not explicitly provide 'when to use' or 'when not to use' guidance beyond the basic function, so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in fetching and processing web content (fetch_extract, fetch_html, fetch_metadata, html_to_markdown), which could confuse an agent. However, descriptions clarify output types, so most tools are distinguishable.
Names follow mixed conventions: verb_noun (fetch_html, remove_background), noun_verb (csv_query, rss_parse), and noun_noun (tool_catalog, screenshot_url). Each name is descriptive, but the lack of a consistent pattern makes it harder to guess tool names.
With 17 tools, the count is slightly above the ideal 3-15 range but still manageable. The inclusion of 5 meta-tools (pricing, tool_catalog, task_recipes, memory_snippet, use_tool) inflates the count but serves a discovery purpose.
The toolkit covers a broad range of web and data tasks (fetch, parse, query, convert, image, SEO). Minor gaps exist (e.g., no OCR, no image editing), but use_tool can dynamically access additional tools, mitigating incompleteness.
