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Sats4AI - Bitcoin-Powered AI Tools

remove_background

Remove background from any image, returning transparent PNG. Uses BiRefNet (state-of-the-art, Papers with Code — Sm 0.901 on DIS5K). Handles hair, fur, glass, transparency, and complex edges. Stable endpoint — model upgrades automatically as SOTA evolves. 44 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='remove_background'.

Input Schema

NameRequiredDescriptionDefault
paymentIdYesValid payment ID (must be paid)
resultKeyNoOptional. The resultKey from create_payment. Send it with the same arguments to get this result again after a timeout.
imageBase64YesBase64-encoded image (PNG, JPEG, WEBP) or data URI

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / resultKey
      Added value: +{
      +  "description": "Optional. The resultKey from create_payment. Send it with the same arguments to get this result again after a timeout.",
      +  "type": "string"
      +}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

No annotations, so the description carries the burden and does well: discloses cost (44 sats/image), auth model (Lightning payment, no API key/signup), output format (transparent PNG), and that the endpoint auto-upgrades models — a real behavioral-drift disclosure. Gaps: no latency/timeout expectations, failure handling, or image size limits.

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

Conciseness4/5

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

Purpose and return value are front-loaded, followed by capability and payment constraints — a sensible order. Some benchmark marketing ('Sm 0.901 on DIS5K', 'state-of-the-art') is borderline filler but arguably supports model-choice trust.

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

Completeness4/5

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

No output schema and no annotations, yet the description covers the return artifact (transparent PNG) and the payment prerequisite. It omits how the result is delivered (inline base64 vs URL) and what happens on timeout, leaving a small gap for an agent handling the response.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description adds genuine value by explaining where paymentId comes from (create_payment with toolName='remove_background'), which the schema only calls 'Valid payment ID'. It says nothing about resultKey or accepted input formats beyond what the schema already documents.

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?

States a specific verb and resource (remove background) and a concrete output (transparent PNG), plus the underlying model (BiRefNet). It is clearly distinct from remove_object/edit_image by name and scope, though it never explicitly contrasts with those siblings.

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

Usage Guidelines4/5

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

Gives the concrete prerequisite: 'Requires create_payment with toolName="remove_background"' before invocation, plus payment method detail. It does not explicitly say when to prefer this over remove_object or upscale_image, so it stops short of full routing guidance.

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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