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Glama

remove_object

Remove unwanted objects from images by describing them. No mask required. Detects and inpaints objects like people, cars, or watermarks.

Instructions

Remove unwanted objects from images by describing what to remove — no mask needed. Combines Grounding DINO detection (ECCV 2024) with Bria Eraser inpainting. Just say 'person', 'car', or 'watermark' and the object is erased and filled convincingly. 15 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='remove_object'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentIdYesValid payment ID (must be paid)
imageBase64YesBase64-encoded image (PNG, JPEG, WEBP) or data URI
queryYesWhat to remove (e.g. 'person', 'car', 'watermark', 'text')
box_thresholdNoDetection confidence threshold (0-1, default 0.25)
text_thresholdNoText matching threshold (0-1, default 0.25)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses use of Grounding DINO and Bria Eraser, cost of 15 sats, pay-per-request with Bitcoin Lightning, no API key/signup needed, and requirement for create_payment. However, it doesn't mention image limitations or response format.

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?

The description is three sentences plus a payment instruction. It is concise but packs necessary details. Could be slightly tighter, but overall well-structured and front-loaded.

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?

Tool has 5 parameters but no output schema. The description does not mention what the tool returns (likely base64 image) or if there is a job ID. Error handling and rate limits are absent. Given the complexity, more detail on return value and potential errors would be beneficial.

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%. Description adds value by giving examples for 'query', stating default thresholds of 0.25, and explaining that paymentId must be paid. This supplements the schema descriptions.

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

Purpose5/5

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

The description clearly states 'Remove unwanted objects from images' with specific verb and resource. Examples like 'person', 'car', 'watermark' illustrate usage. It distinguishes from sibling tools like 'remove_background' by specifying text-based removal without masks.

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?

The description provides when to use: 'just describe what to remove', and mentions no mask needed. It also explains pricing and payment flow. However, it does not explicitly state when not to use it (e.g., for complex edits) or compare with alternatives like 'edit_image'.

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