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Media Generator — Images, Videos & Audios

by ai.saifs

logo_generator

Input: A text prompt describing the desired logo style.

Output: We will return the URL of the generated logo image.

Functionality: This MCP tool generates a logo using the provided text prompt. It leverages the Logo Generation API to interpret the prompt and create a corresponding logo image.

Steps:

  1. We will get the user_id from the request context.

  2. We will validate the user's generation tokens.

  3. We will call the Logo Generation API with the provided prompt.

  4. This Logo Generation API will return a JSON response with the logo image URL.

  5. We will return the logo image URL to the user.

INSTRUCTION FOR CLIENT MODEL:

  • Extract the required input parameter 'prompt' (type: string) from the user's provided prompt. The prompt should describe the desired logo style or content.

  • Ignore any extraneous information in the user's input.

  • Pass the extracted 'prompt' value to this tool as the 'prompt' parameter.

  • Example: For user input "Create a logo for a vegan bakery with green leaves", extract 'vegan bakery with green leaves' as the prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.3/5.0
Behavior4/5

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 reveals internal steps including user_id retrieval and token validation, informing the agent about prerequisites and potential token consumption. It lacks details on error handling or rate limits, but the procedural transparency is above average.

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 somewhat verbose, repeating the 'generates a logo' concept and including step-by-step internal details that could be condensed. However, it is structured with clear sections and states the purpose early, making it readable but not maximally concise.

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?

The tool is simple with one parameter, and the description covers input, output, and the API call flow, including token validation. It does not detail error scenarios or prompt constraints, but for a generation tool this is sufficient for an agent to invoke it correctly.

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

Parameters5/5

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

The schema coverage is 0%, so the description must compensate. It defines the prompt as describing the desired logo style and provides an explicit extraction example, fully resolving parameter ambiguity. This is a model example of compensating for a bare schema.

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 generates a logo from a text prompt and returns the URL. The verb 'generates' and resource 'logo' are specific, and the focus on logos distinguishes it from sibling tools like generate_image and edit_image_with_text.

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 includes an 'INSTRUCTION FOR CLIENT MODEL' section that explains exactly how to extract the 'prompt' parameter and provides an example, which is clear usage guidance. However, it does not explicitly contrast with alternative tools or mention when not to use it, earning a 4 rather than 5.

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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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