Media Generator — Images, Videos & Audios

edit_image_with_text

Input: An image URL and a text prompt describing the desired edit (e.g., "change background to blue").

Output: The URL of the edited image.

Functionality: This MCP tool edits an existing image based on a text instruction. It's useful for making changes like altering colors, adding or removing objects, or changing the style of an image.

Steps:

Get the user_id from the request context and validate tokens.
Call the Edit Image API with the provided image URL and text prompt.
The API will return a JSON response containing the URL of the newly edited image.
Return the edited image's URL to the user.

INSTRUCTION FOR CLIENT MODEL:

Extract the required input parameters 'prompt' (type: string, describing the desired edit) and 'image_url' (type: string, URL) from the user's prompt.
Ignore any extraneous information in the user's input.
Pass the extracted values to this tool as 'prompt' and 'image_url'.
Example: For user input "Change the background of https://example.com/image.jpg to blue", extract 'image_url' as 'https://example.com/image.jpg' and 'prompt' as 'change background to blue'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes
`image_url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It outlines the steps and states the tool edits an existing image and returns a URL, but it does not mention error handling, permission requirements, rate limits, or potential side effects. The transparency is adequate but not comprehensive for a mutation tool.

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

Conciseness3/5

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

The description is front-loaded with purpose and examples, but the 'Steps' section is procedural and somewhat redundant for an MCP tool description. The 'INSTRUCTION FOR CLIENT MODEL' section improves parameter clarity but could be more succinct. Overall it earns its place but is more verbose than necessary.

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?

Given the tool's simplicity (two string params), the description covers functionality, extraction guidance, and output shape (URL). The presence of an output schema reduces the need to explain return values. Missing details like failure modes are a gap, but the provided context is largely sufficient for an agent to invoke the tool correctly.

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 description coverage is 0%, so the description must compensate. It explicitly defines 'prompt' as 'text prompt describing the desired edit' and 'image_url' as 'URL', and provides an extraction example. This adds meaningful semantic detail beyond the raw schema, though it doesn't cover constraints like file format or length limits.

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's function: 'edits an existing image based on a text instruction' with concrete examples like 'change background to blue'. This specific verb+resource phrasing distinguishes it from siblings such as generate_image or remove_background.

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 clear context for when to use the tool ('useful for making changes like altering colors, adding or removing objects, or changing the style of an image'), but it does not explicitly mention when not to use it or name alternatives. This is a solid use-case guideline but lacks exclusionary guidance.

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

All Media Generator — Images, Videos & Audios tools Inspect

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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, such as generate_image, remove_background, and upscale_image. However, logo_generator and generate_image both accept a text prompt and return an image URL, which could cause confusion, and music_generator and song_generator overlap in audio generation, though their inputs differ.

Naming Consistency3/5

Tool names are a mix of verb_noun (generate_image, remove_background), noun_generator (logo_generator, song_generator), and simple verbs (fetch, search, health). While readable, the lack of a single consistent pattern makes it less predictable for an agent.

Tool Count4/5

With 17 tools, the server is slightly above the ideal 3-15 range but not excessively over. Each tool serves a distinct function for generating or manipulating media, so the count feels justified for the broad scope.

Completeness4/5

The toolset covers the core media generation lifecycle: image generation, editing, background removal, upscaling, video generation, image-to-video, music and song generation, and text-to-speech. Minor gaps like video editing or managing past generations exist, but they are not critical for standard workflows.

Resources

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