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poof-bg

Poof Background Removal MCP Server

by poof-bg

remove_background

Remove backgrounds from images to isolate subjects. Accepts URLs or base64 input, supports multiple formats and size presets, and returns processed images as base64.

Instructions

Remove the background from an image. Returns the processed image as base64. Accepts URL or base64-encoded image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesImage input: base64-encoded image data or a URL to an image
formatNoOutput image formatpng
channelsNoOutput color channels. Use 'rgba' for transparency, 'rgb' for opaque backgroundrgba
bg_colorNoBackground color (hex, rgb, or color name). Only applies when channels is 'rgb'. Example: '#ffffff'
sizeNoOutput image size presetfull
cropNoWhether to crop the image to the subject bounds
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that it 'Returns the processed image as base64,' which adds some context about the output format. However, it lacks details on performance (e.g., processing time, rate limits), error handling, or side effects (e.g., whether the original image is modified or stored). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of only two sentences that directly state the tool's function and key input/output details. Every sentence earns its place by covering essential information without any redundancy or unnecessary elaboration, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a 6-parameter image processing tool with no annotations and no output schema, the description is somewhat incomplete. It covers the basic purpose and input/output formats but lacks details on behavioral aspects, usage context, and output specifics beyond base64 encoding. While it's minimal, it provides enough to understand the core function, but more context would be beneficial for full comprehension.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, meaning all parameters are well-documented in the input schema. The description adds minimal value beyond the schema by mentioning that it 'Accepts URL or base64-encoded image,' which relates to the 'image' parameter but doesn't provide additional semantics. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Remove the background from an image.' It specifies the verb ('remove') and resource ('background from an image'), making the function unambiguous. However, it doesn't differentiate from the sibling tool 'get_account', which is unrelated, so it doesn't need sibling differentiation but could be more specific about the processing method.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions that it 'Accepts URL or base64-encoded image,' but this is about input formats, not usage context. There's no indication of prerequisites, limitations, or scenarios where this tool is preferred over other image processing methods.

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

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