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Media Toolkit MCP Server

Media Toolkit MCP Server

by kevinten-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

remove_background

Remove the background from any image using local AI processing. No cloud API required—the model downloads automatically on first use.

Instructions

Remove the background from an image using AI (rembg). Works locally — no cloud API needed. The model (~170MB) is downloaded automatically on first use.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_path": "<image_path>"
    },
    "name": "remove_background"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_pathYesPath to the input image file
output_pathNoPath to save the output image (optional, auto-generated if omitted)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

Despite no annotations, the description discloses key behavioral traits: works locally (no cloud API), model downloaded automatically on first use, and model size (~170MB). This provides transparency about resource usage and offline operation.

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?

Two sentences efficiently convey purpose, technical details, and model behavior. No unnecessary words, front-loaded with key information.

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?

For a tool with no output schema, the description covers operation and parameters well. However, it does not explain what the tool returns (e.g., saved file path or success signal), which could be helpful for the agent.

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?

Schema coverage is 100% and both parameters have descriptions in the schema. The tool description does not add additional meaning beyond what is already in the schema, so baseline 3 is appropriate.

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 the tool removes background from an image using AI (rembg). It distinguishes from siblings as none perform background removal, making it unambiguous.

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

Usage Guidelines3/5

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

The description implies usage for background removal and mentions local operation, but does not explicitly specify when to use this tool versus alternatives like resize_image or create_collage. No when-not-to-use 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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