What can you do with this server?

This server provides video enhancement and image segmentation capabilities via MCP tools. * create_task – Asynchronously create a video enhancement task by providing a video URL or local file path, with options for target resolution (480p, 540p, 720p, 1080p, 2k). Returns a task_id immediately for later status polling. * get_task_status – Query the status of a previously created video enhancement task using its task_id. Returns the current status (processing, completed, or failed), progress, and the final video URL upon completion. * enhance_video_sync – Synchronously enhance a video, blocking until the task completes or a timeout is reached. Supports URL or local file input, resolution selection, configurable polling interval, and timeout. If the task doesn't finish in time, it returns the task_id so you can follow up with get_task_status. * sam3_predict – Run SAM3 image segmentation on an image provided via local file path, public URL, or base64-encoded data. Requires an English text prompt describing the target object(s). Returns segmentation masks, bounding boxes, and confidence scores for detected objects.

How do I use media-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@media-mcp enhance this video to 1080p: https://example.com/video.mp4" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

de en es ko ru zh

media-mcp

Official

by avclabs

Overview Schema Source Related Servers Discussions(1)Admin

TypeScript

Remote

media-mcp (Node.js)

Chinese | English

npm version Node.js >=18 License: MIT

Video enhancement and image segmentation service based on the MCP protocol, acting as an MCP Client-Server to interact with a backend HTTP Server.

Features

Provides the following MCP Tools:

create_task - Create a video enhancement task (supports URL or local file upload)
get_task_status - Query task status
enhance_video_sync - Synchronous video enhancement (blocks until completion)
sam3_predict - SAM3 image segmentation (supports local path, URL, or Base64 image)

Related MCP server: Grok Imagine Video MCP Server

Prerequisites

Node.js >= 18 (Check: node --version)
API Key (For authentication, please contact the service provider to obtain one)

Easy Installation (Recommended)

If your AI Agent has a defined MCP configuration path, simply copy and send the following sentence to the AI:

帮我安装 npm 包 @avclabs.ai/media-mcp 作为 MCP server。我的 API Key 是：sk-xxxxxxxx。

The AI will automatically:

Detect the MCP client you are using
Find the configuration file path
Write the correct configuration
Prompt you to restart the client

Manual Installation

No installation required; run directly in the MCP client configuration using npx.

1. Claude Code (CLI)

Run in Claude Code:

/mcp

Check the output for the configuration file path corresponding to "User MCPs", then edit that file.

Common paths (if /mcp is unavailable):

Windows: %USERPROFILE%\.claude.json
macOS: ~/.claude.json
Linux: ~/.claude.json
Legacy/Alternative: ~/.claude/mcp.json

Paste the following content (replace your-api-key with your actual API Key):

{
  "mcpServers": {
    "video-enhancement": {
      "command": "npx",
      "args": ["-y", "@avclabs.ai/media-mcp@latest"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

After saving, run /mcp to verify if it loaded successfully.

2. Cursor

Go to Settings > Tools & MCPs > Add New MCP Server:

Name: video-enhancement
Type: command

Command:

env HTTP_API_KEY=your-api-key npx -y @avclabs.ai/media-mcp@latest

Or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "video-enhancement": {
      "command": "npx",
      "args": ["-y", "@avclabs.ai/media-mcp@latest"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Verify Installation

After restarting the client, confirm if the tools loaded successfully:

Or ask the AI directly: "What tools do you have available?"
You should see: create_task, get_task_status, enhance_video_sync, sam3_predict

Configuration Options

Variable Name	Required	Default Value	Description
`API_KEY`	Yes	-	API authentication key (shared by video enhancement and SAM3)
`HTTP_API_BASE_URL`	No	`https://mcp.avc.ai/enhance`	Video enhancement service interface URL
`SAM3_API_BASE_URL`	No	`https://mcp.avc.ai/sam`	SAM3 service interface URL
`SAM3_POLL_INTERVAL`	No	`2000`	Polling interval (ms)
`SAM3_POLL_MAX_ATTEMPTS`	No	`60`	Maximum polling attempts

Custom Service URL

{
  "env": {
    "HTTP_API_BASE_URL": "https://your-endpoint.com",
    "API_KEY": "your-api-key",
    "SAM3_API_BASE_URL": "http://localhost:8001"
  }
}

Or via command line arguments:

npx -y @avclabs.ai/media-mcp@latest --base-url https://your-endpoint.com --api-key your-api-key --sam3-base-url http://localhost:8001

Usage Examples

Once configured, use natural language to tell the AI:

"Help me enhance this video to 1080p: https://example.com/video.mp4"

"Upscale the video.mp4 on my desktop to 2k quality"

The AI will automatically call the corresponding tool to complete the task.

"Help me analyze this image and find all objects in it: C:\Users\xxx\photo.png"
"Use SAM3 to segment this image, the prompt is 'find all cars'"

Provided Tools

create_task

Create a video enhancement task (asynchronous).

Parameter	Type	Required	Default Value	Description
`video_source`	string	Yes	-	Video URL or local file path (URL must be publicly accessible; links requiring login or signatures are not supported)
`type`	string	No	`url`	`url` or `local`
`resolution`	string	No	`720p`	`480p`, `540p`, `720p`, `1080p`, `2k`

Return Value:

{
  "success": true,
  "task_id": "xxx",
  "status": "wait"
}

get_task_status

Query task status.

Parameter	Type	Required
`task_id`	string	Yes

Return Value:

{
  "success": true,
  "task_id": "xxx",
  "status": "completed",
  "progress": 100,
  "video_url": "https://..."
}

enhance_video_sync

Synchronous video enhancement (blocks until completion).

Parameter	Type	Required	Default Value	Description
`video_source`	string	Yes	-	Video URL or local file path (URL must be publicly accessible; links requiring login or signatures are not supported)
`type`	string	No	`url`	`url` or `local`
`resolution`	string	No	`720p`	Target resolution
`poll_interval`	number	No	`5`	Polling interval (seconds)
`timeout`	number	No	`600`	Timeout (seconds)

sam3_predict

Use the SAM3 segmentation API to analyze an image and generate inference results (masks, boxes, scores).

Parameters:

Image input (choose one of the three, one must be provided):

imagePath (string): Absolute path to a local image. Supports common image formats (e.g., PNG, JPG, JPEG).
- Example: "C:\\Users\\xxx\\photo.png", "/home/user/images/cat.jpg"
- Use case: User explicitly provided a local file path
imageUrl (string): Publicly accessible image URL.
- Example: "https://example.com/photo.jpg"
- Use case: Image is already online, user provided a link
- Note: URL must be publicly accessible; links requiring login or signatures are not supported
imageBase64 (string): Base64 encoded image data.
- Example: "iVBORw0KGgoAAAANSUhEUgAA..."
- Use case: User dragged or uploaded an image attachment, Agent encodes the image to base64 and passes it
- Note: Base64 data for large images can be quite large, transmission time may be slightly longer

Other parameters:

prompt (string, required): English text prompt used to specify the target object to segment in the image. For example, "person", "car", "a cat sitting on a sofa". Since the SAM3 model only accepts English prompts, it is recommended to pass English descriptions. If the user provides Chinese or other non-English text, the Agent will automatically translate it to English before calling.

Return:

After inference is complete, a JSON string is returned directly. The JSON contains the following three fields:

masks: Two-dimensional array. Each element is a binary mask (values 0 or 1) with the same dimensions as the input image, used to mark the pixel-level location of the detected object in the image. The i-th mask in the array corresponds to the i-th detected object instance.
boxes: Two-dimensional array. Each element is a bounding box coordinate in [x1, y1, x2, y2] format, representing the rectangular area of the detected object in the image. x1, y1 are the top-left coordinates, and x2, y2 are the bottom-right coordinates.
Coordinate system explanation: The origin (0, 0) is the top-left corner of the image, the x axis increases to the right, and the y axis increases downwards, in pixels. For example, [120, 80, 300, 450] means the object area starts 120px from the left edge and 80px from the top edge, ending at 300px from the left edge and 450px from the top edge, with a width of x2 - x1 = 180px and a height of y2 - y1 = 370px.
scores: One-dimensional array. Each element is the confidence score for the corresponding detection result, ranging from 0 to 1. A higher score indicates the model is more certain about the detection result.

Example of result JSON content:

{
  "masks": [
    [[0, 0, 1, ...], [0, 1, 1, ...], ...],
    [[0, 0, 0, ...], [0, 0, 1, ...], ...]
  ],
  "boxes": [
    [120, 80, 300, 450],
    [400, 200, 600, 500]
  ],
  "scores": [0.95, 0.87]
}

FAQ

Prompted that file cannot be found after dragging an attachment?

This is a known limitation of stdio MCP. When dragging or uploading attachments via the Agent interface, the file path is usually not automatically passed to the MCP Server.

Solution:

Provide the path as well (Recommended): After dragging the image, add the local absolute path of the image in the text:
"Please process this image D:\photos\cat.jpg, find the cat in it"
Wait for automatic encoding: Claude may automatically encode the image to base64 and pass it. If successful, no further action is needed.
Answer path inquiry: If Claude asks for the image path, simply reply with the local absolute path.

Is there a priority for the three input methods?

There is no strict priority. Claude will automatically choose the most appropriate method based on the conversation context:

You provided a local path → Use imagePath
You provided a web link → Use imageUrl
You dragged an attachment and there is no path → Try imageBase64

Which image formats are supported?

Common formats are supported: PNG, JPG, JPEG, BMP, WebP, etc. It is recommended to prioritize PNG or JPG.

What if URL image download fails?

Ensure the URL is publicly accessible and does not require login, cookies, or signatures. If the image is on a service that requires authentication (e.g., private S3 Bucket, image hosting requiring login), please download it locally first and use imagePath.

What if the Base64 image is too large?

If the image is very large (e.g., 4K resolution), the base64 encoded data will be very large, which may cause slow transmission. It is recommended to:

Use imagePath instead
Or compress the image before encoding

File Upload Instructions

When type is "local", the MCP Server will:

Read the local file
Upload directly to TOS object storage via a pre-signed URL
Maximum file size: 100MB

Troubleshooting

"command not found: npx"

Install Node.js >= 18: https://nodejs.org/

"Error: --api-key must be provided or API_KEY must be set"

API Key is missing, please check env.API_KEY in the configuration.

MCP Server shows red/error in client

Check logs:

Claude Desktop macOS: ~/Library/Logs/Claude/mcp*.log
Claude Desktop Windows: %APPDATA%\Claude\logs\mcp*.log
Cursor: Output panel > MCP

"TOS upload failed"

Usually due to a signature mismatch; please confirm that HTTP_API_BASE_URL and HTTP_API_KEY are correct and valid.

Global Installation (Optional)

If you don't want to use npx every time:

npm install -g @avclabs.ai/media-mcp

Then use "command": "media-mcp" with "args": ["--api-key", "your-api-key"] in the configuration.

License

MIT License - See LICENSE file for details

Available Tools

4 tools

create_taskB

创建视频增强任务（异步）

支持两种上传方式：

URL 上传：提供视频 URL
本地上传：提供本地文件路径，MCP Server 自动上传到 TOS 对象存储

参数说明：

video_source: 视频 URL 或本地文件路径
type: "url" 或 "local"
resolution: 目标分辨率

ParametersJSON Schema

Name	Required	Description	Default
`video_source`	Yes	视频URL地址或本地文件路径（URL必须公网可访问，不支持需要登录或签名的链接）
`type`	No	上传类型：url=网络视频，local=本地文件	url
`resolution`	No	目标分辨率，默认720p	720p

TDQS

B3.4/5.0

Behavior2/5

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

No annotations provided, so the description must carry full burden. It notes async behavior and TOS upload but omits side effects, permissions, failure modes, or rate limits. The description only partially discloses behavioral traits.

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 concise with three bullet points and front-loaded purpose. Every sentence earns its place, but structure could be slightly improved with clearer differentiation from siblings.

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?

The description covers input parameters well but lacks output schema explanation (e.g., task ID or status). With no annotations and multiple siblings, more context on post-creation steps would improve completeness.

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?

Input schema has 100% description coverage, so baseline is 3. The description groups parameters and explains the two upload modes, but does not add new information beyond the schema's existing parameter 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 the tool creates an async video enhancement task and distinguishes between two upload methods (URL and local). It uses specific verbs and resources, and is not a tautology.

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 explains when to use each upload type (URL vs local) but does not explicitly guide when to use this async tool over its sync sibling (enhance_video_sync) or other tools like get_task_status.

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

enhance_video_syncA

同步增强视频（阻塞等待完成）

支持两种上传方式：

URL 上传：提供视频 URL
本地上传：提供本地文件路径，MCP Server 自动上传到 TOS 对象存储

参数说明：

video_source: 视频 URL 或本地文件路径
type: "url" 或 "local"
resolution: 目标分辨率
poll_interval: 轮询间隔（秒）
timeout: 超时时间（秒）

ParametersJSON Schema

Name	Required	Description	Default
`video_source`	Yes	视频URL地址或本地文件路径（URL必须公网可访问，不支持需要登录或签名的链接）
`type`	No	上传类型：url=网络视频，local=本地文件	url
`resolution`	No	目标分辨率，默认720p	720p
`poll_interval`	No	轮询间隔（秒），默认5
`timeout`	No	超时时间（秒），默认600

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It explicitly states 'blocking wait for completion', explains the automatic upload of local files to TOS storage, and mentions polling parameters, giving good transparency. It does not mention side effects, but given the nature, none are expected.

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 well-structured with bullet points and clear categorization of upload methods and parameters. Every sentence serves a purpose, and there is no redundancy or unnecessary detail.

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 description covers the blocking nature, upload methods, and all parameters thoroughly. It lacks an explicit description of the return value, but given the synchronous nature, it likely returns the enhanced video. Overall, it is fairly complete for a tool without an output schema.

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%, so the baseline is 3. The description adds context beyond the schema, such as the automatic upload process for local files and that URLs must be publicly accessible. This extra information enhances understanding of parameter usage.

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 purpose: to enhance video synchronously, with a blocking wait. It details two upload methods (URL and local), distinguishing it from sibling tools that handle different operations like task creation or status checking.

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 explains the two upload methods and the blocking nature, implicitly indicating when to use the tool. However, it does not explicitly contrast with siblings like create_task (likely async) or provide when-not-to-use guidance, making usage guidelines less explicit.

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

get_task_statusA

查询视频增强任务状态

ParametersJSON Schema

Name	Required	Description	Default
`task_id`	Yes	任务ID

TDQS

A3.6/5.0

Behavior2/5

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

No annotations provided, and the description only states the purpose without disclosing any behavioral traits such as polling requirements, rate limits, or expected response behavior.

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?

Single sentence with no wasted words; efficient and to the point.

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 simple status query with one parameter, the description is mostly complete but could benefit from mentioning possible return statuses or output format since no output schema is provided.

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%, and the description adds no additional meaning beyond what is already in the input 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 verb 'query' and the resource 'video enhancement task status', distinguishing from sibling tools 'create_task' and 'enhance_video_sync' which have different actions.

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?

No explicit guidance on when to use this tool versus alternatives, but the context of sibling tools implies it is for checking status after creation or enhancement.

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

sam3_predictA

Analyze an image using the SAM3 segmentation API to generate inference results (masks, boxes, scores). The image can be provided in one of three ways:

imagePath: Absolute path of a local image file (e.g. C:\Users\xxx\photo.png). Use this when the user provides a local file path.
imageUrl: Publicly accessible URL of the image (e.g. https://example.com/photo.jpg). Use this when the user provides a web link.
imageBase64: Base64-encoded image data. Use this when the user uploads or drags-and-drops an image as an attachment and no local path is available. In this case, encode the image content as base64 and pass it via this parameter. If the user mentions an uploaded image but does not provide a path, URL, or base64 data, ask the user for the local absolute path. Prompt must be in English. If the user provides Chinese or other non-English text, translate it to English before calling this tool.

ParametersJSON Schema

Name	Required	Description
`imagePath`	No	Absolute path of a local image file (e.g. C:\\Users\\xxx\\photo.png)
`imageUrl`	No	Publicly accessible URL of the image to process
`imageBase64`	No	Base64-encoded image data. Use this when the image is provided as an attachment without a local path
`prompt`	Yes	Text prompt for mask generation. Must be in English. If the user provides Chinese or other non-English text, translate it to English before calling this tool

TDQS

A4.4/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It discloses that it calls an external API and generates masks, boxes, scores. However, it lacks details on potential side effects, authentication, error handling, or rate limits. It does not contradict annotations.

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 well-structured with bullet points, front-loading the main purpose. Every sentence serves a purpose, explaining input methods and prompt requirements without redundancy. It is concise yet comprehensive.

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

Completeness5/5

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

The description covers what the tool does (segmentation analysis), how to provide input (three methods), and what outputs are generated (masks, boxes, scores). Even without an output schema, it gives sufficient information for an agent to use the tool 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?

Although schema coverage is 100%, the description adds significant value by explaining usage contexts for each image parameter and specifying that the prompt must be in English, requiring translation if needed. This goes beyond 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 the tool's function: 'Analyze an image using the SAM3 segmentation API to generate inference results (masks, boxes, scores).' This specifies the verb (analyze), resource (image via SAM3 API), and output, effectively distinguishing it from siblings like create_task.

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 explicit guidance on when to use each image input method (imagePath, imageUrl, imageBase64) and includes instructions for handling non-English prompts. However, it does not explicitly mention when not to use this tool or compare it to alternatives.

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.

4 tool updatesv0.1.0May 8, 2026
- First observedcreate_task
- First observedenhance_video_sync
- First observedget_task_status
- First observedsam3_predict

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored 2026-05-08 09:50 across 4 tools

Disambiguation2/5

The first three tools are about video enhancement tasks with overlapping functionality (create_task and enhance_video_sync both appear to initiate enhancement), and the fourth tool (sam3_predict) is for image segmentation, a completely different domain. The descriptions are not clear enough to distinguish which tool to use for a given task, causing confusion.

Naming Consistency3/5

Tool names partially follow a verb_noun pattern (create_task, get_task_status), but 'enhance_video_sync' is awkward and 'sam3_predict' mixes model name with verb, introducing inconsistency.

Tool Count4/5

With 4 tools, the count is reasonable for a focused server, but the server actually combines two unrelated capabilities (video enhancement and image segmentation), making the scope unclear but the number itself is not extreme.

Completeness2/5

For video enhancement, there are create, sync enhance, and status query, but missing cancel, list, or delete operations. For image segmentation, only a single predict tool exists. The surface is incomplete for both domains.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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