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MichaelYangjson

Ghibli Video MCP Server

by MichaelYangjson
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

mcp-server-ghibli MCP Server

A TypeScript-based MCP server that provides AI image and video generation capabilities through a simple interface.

Note: This server requires an API key from GPT4O Image Generator. Please visit the website to obtain your API key before using this service.

Features

Tools

1. Image to Video Conversion

  • image_to_video - Convert static images into animated videos

    • Required parameters:

      • image: Base64 encoded image or image URL

      • api_key: Authentication key

    • Optional parameters:

      • prompt: Text prompt to guide video generation (default: "in the style of ghibli")

      • aspect_ratio: Output video aspect ratio (default: "9:16")

      • negative_prompt: Negative prompt to guide generation (default: "bad prompt")

2. Points Management

  • get_points - Check remaining API credits

    • Required parameters:

      • api_key: Authentication key

3. Task Management

  • get_task_result - Check the status of a video generation task

    • Required parameters:

      • taskId: Task ID returned from image_to_video

      • api_key: Authentication key

Related MCP server: HeyBeauty MCP Server

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mcp-server-ghibli-video": {
      "command": "npx",
      "args": ["-y", "@openmcprouter/mcp-server-ghibli-video"],
      "env": {
        "Ghibli_API_URL": "https://www.gpt4oimg.com"
      }
    }
  }
}

Installing via Smithery

To install mcp-server-ghibli MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @MichaelYangjson/mcp-ghibli-video --client claude

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Available Tools

3 tools
get_pointsC

Get remaining points

ParametersJSON Schema
NameRequiredDescriptionDefault
api_keyYesAPI key for authentication

TDQS

C2.3/5.0
Behavior2/5

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. 'Get remaining points' implies a read-only operation, but it doesn't specify whether this requires authentication (though the schema shows an api_key parameter), what format the response returns, whether there are rate limits, or any side effects. The description adds minimal behavioral context beyond what's implied by the verb 'Get'.

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?

The description is extremely concise at just three words. It's front-loaded with the core action ('Get remaining points') and contains no unnecessary verbiage or redundant information. Every word serves a purpose in conveying the basic function.

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

Completeness2/5

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

For a tool with no annotations, no output schema, and a single parameter, the description is inadequate. It doesn't explain what 'points' are, what system they belong to, what format the response will have, or any behavioral characteristics. The agent would need to guess about the tool's purpose and output based on minimal information.

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?

The schema has 100% description coverage (the api_key parameter is fully documented in the schema), so the baseline is 3. The description doesn't add any parameter-specific information beyond what's already in the schema - it doesn't explain how the api_key relates to 'remaining points' or provide additional context about parameter usage.

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

Purpose2/5

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

The description 'Get remaining points' is a tautology that essentially restates the tool name 'get_points' without adding meaningful specificity. It doesn't specify what kind of points (e.g., API usage points, user points, reward points) or from what system/context. While it includes a verb ('Get'), it lacks the resource specificity needed for clear differentiation from sibling tools like 'get_task_result' and 'image_to_video'.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or relationship to sibling tools. There's no indication of whether this should be used before/after other operations or under what conditions 'remaining points' would be relevant.

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

get_task_resultC

Get task result

ParametersJSON Schema
NameRequiredDescriptionDefault
taskIdYesTask ID
api_keyYesAPI key for authentication

TDQS

C2.1/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get task result' implies a read operation, but it does not specify whether this is safe, requires authentication (though the schema includes an api_key), involves side effects, or details response behavior. The description lacks any behavioral traits beyond the basic action, making it inadequate for a tool with no annotation support.

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?

The description is extremely concise with just three words, 'Get task result', which is front-loaded and wastes no space. Every word directly contributes to stating the tool's purpose, making it efficient and well-structured for its minimal content.

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

Completeness2/5

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

Given the tool's complexity (a read operation with authentication) and lack of annotations and output schema, the description is incomplete. It does not explain what a 'task result' is, how results are returned, or any behavioral context. The schema covers parameters, but the overall tool behavior remains underspecified, leaving significant gaps for agent understanding.

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?

The input schema has 100% description coverage, with clear documentation for 'taskId' and 'api_key'. The description adds no additional meaning beyond what the schema provides, as it does not explain parameter usage or constraints. According to the rules, with high schema coverage (>80%), the baseline score is 3 when no param info is added in the description.

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

Purpose2/5

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

The description 'Get task result' is a tautology that essentially restates the tool name 'get_task_result'. It specifies the verb 'get' and resource 'task result', but provides no additional context about what a 'task result' entails or how this differs from sibling tools like 'get_points' or 'image_to_video'. The purpose is minimally stated but lacks specificity and differentiation.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. There are no indications of context, prerequisites, or exclusions, and it does not reference sibling tools or suggest when this tool is appropriate. This leaves the agent with no usage instructions beyond the tool name.

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

image_to_videoC

Convert image to animated video

ParametersJSON Schema
NameRequiredDescriptionDefault
imageYesBase64 encoded source image or image URL
promptNoOptional prompt for video generation
aspect_ratioNoAspect ratio of the output video (e.g. '9:16')9:16
negative_promptNoNegative prompt to guide generationbad prompt
api_keyYesAPI key for authentication

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Convert image to animated video' implies a generative or processing operation, but it doesn't disclose critical traits like whether it's a read-only or destructive operation, authentication requirements (though hinted by api_key parameter), rate limits, or output format. For a tool with no annotations, this is a significant gap in transparency.

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?

The description is extremely concise with a single sentence 'Convert image to animated video', which is front-loaded and wastes no words. Every part of the sentence directly contributes to understanding the tool's purpose, making it efficient and well-structured.

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

Completeness2/5

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

Given the complexity of a generative video tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral aspects, output format, error handling, and usage context. The description should do more to compensate for the absence of structured data, making it inadequate for full agent understanding.

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 description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or providing usage examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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?

The description 'Convert image to animated video' clearly states the verb ('Convert') and resource ('image to animated video'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools (get_points, get_task_result), which are unrelated to media conversion, so it doesn't need sibling differentiation but could be more specific about the type of conversion.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for use, or exclusions, leaving the agent to infer usage based on the tool name alone. This lack of explicit guidance reduces its effectiveness in tool selection.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updates
    • First observedget_points
    • First observedget_task_result
    • First observedimage_to_video
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TDQS

C2.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: get_points retrieves points, get_task_result fetches task results, and image_to_video handles video conversion. An agent can easily differentiate them based on their specific functions.

Naming Consistency4/5

The tools follow a consistent verb_noun pattern (get_points, get_task_result, image_to_video), with minor deviations in verb usage (get vs. image_to). The naming is readable and mostly predictable, though not perfectly uniform.

Tool Count2/5

With only 3 tools, the set feels thin for a video server's apparent scope, lacking operations like video management, task creation, or point usage. This minimal count may limit agent workflows and indicate incomplete coverage.

Completeness2/5

There are significant gaps in the tool surface for a video server domain: no tools for creating tasks, managing videos, or using points, and core operations like video playback or editing are missing. This will likely cause agent failures in handling typical video-related workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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