Remove an image's background, isolating the subject (BiRefNet AI segmentation). $0.02 per call. Input: an image via source URL. Params: background (optional fill color for the removed area, e.g. 'white'; omit for a transparent cutout — use a PNG/WebP output to keep transparency). Output: the cut-out image as a short-lived signed URL.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes | ||
| source | Yes | Public https:// URL of the source image to process. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost, mutative nature (removes background), output expiration (short-lived signed URL). Adds value beyond annotations (readOnlyHint=false, openWorldHint=true) by explaining output behavior and transparency with PNG/WebP.
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?
Three sentences, front-loaded with purpose, no redundant information. Every sentence adds value.
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?
Covers all aspects: input, parameters, cost, output format and lifespan. No output schema, but description explains return value adequately.
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?
Adds meaning beyond schema: explains source as public HTTPS URL, describes background parameter with examples and behavior (omit for transparent). Compensates for 50% schema description coverage.
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?
Clearly states 'Remove an image's background, isolating the subject' with specific model reference (BiRefNet). Distinguishes from sibling tools like resize and upscale.
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?
Provides usage context: $0.02 per call, input via source URL, optional background fill, output as short-lived URL. Does not explicitly exclude alternatives but context implies when to use.
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.
Each tool targets a distinct operation: optimization, pipeline processing, background removal, resizing, upscaling, and service discovery. No two tools overlap in purpose.
All tool names follow the consistent verb_noun pattern in lowercase snake_case (optimize, process, remove_background, resize, service_info, upscale).
With 6 tools, the server is well-scoped for an image processing service. Each tool earns its place, covering core operations, a pipeline composer, and metadata discovery.
The tool set directly offers essential transformations (compress, resize, background removal, upscale) and a pipeline tool (process) that can invoke additional ops via API, covering most common workflows. Missing direct tools for rotation or filters are minor gaps.