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edu_video_gen

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Python
Hybrid

Scenext MCP Server

English | 中文

An MCP server that integrates with Scenext AI video generation platform to create educational explanation videos based on topics.

一个集成 Scenext AI 视频生成平台的 MCP 服务器，可以根据题目生成教学讲解视频。

Quick Start / 快速开始

本地接入（uvx模式）：

{
  "mcpServers": {
    "scenext": {
      "command": "uvx", 
      "args": ["scenext-mcp"],
      "env": {
        "SCENEXT_API_KEY": "your_actual_api_key_here"
      }
    }
  }
}

远程接入（streamable-http）：

{
  "mcpServers": {
    "scenext": {
      "type": "streamable-http",
      "url": "https://mcp.scenext.cn/mcp/",
      "headers": {
        "Authorization": "Bearer your_actual_api_key_here"
      }
    }
  }
}

Some clients do not support the headers field. You can manually configure and add the request header "Authorization=Bearer your_actual_api_key_here".

For detailed documentation:

Related MCP server: mcp-server-test

License / 许可证

MIT License

Available Tools

2 tools
gen_videoC

生成教学讲解视频,生成时间大约需要10到20分钟，根据题目难度来定

Args: question: 问题内容（文本形式），question和questionImages中至少输入一个 answer: 参考答案（文本形式）：确保生成的讲解内容准确（可选） question_images: 问题内容（图片形式），输入图片的URL或者base64（可选） answer_images: 参考答案（图片形式），输入图片的URL或者base64（可选） quality: 视频质量，可选值：l(低)、m(中)、h(高)，默认为配置的默认质量

Returns: 包含任务ID的字典

ParametersJSON Schema
NameRequiredDescriptionDefault
questionNo
answerNo
question_imagesNo
answer_imagesNo
qualityNom

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals the generation time (10-20 minutes depending on question difficulty), which is valuable operational context. However, it doesn't mention authentication needs, rate limits, error conditions, or what happens during processing. The description doesn't contradict annotations (none exist), but provides only basic behavioral information.

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 reasonably concise but not optimally structured. The first sentence states the purpose, followed by time estimate, then parameter explanations in a separate 'Args' section, and finally return information. While not excessively verbose, the mixing of languages (Chinese purpose/English parameter labels) and the separation of parameter details from the main description could be more integrated. Every sentence adds value but the organization could be improved.

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?

Given the tool's complexity (video generation with 5 parameters, no annotations, but with output schema), the description is moderately complete. The output schema existence means return values don't need explanation in the description. However, for a generative tool with significant processing time and multiple input options, the description should provide more context about error handling, format requirements, and usage scenarios. It covers basics but leaves important gaps.

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

Parameters2/5

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

With 0% schema description coverage and 5 parameters, the description must compensate but does so inadequately. It explains that 'question' and 'question_images' require at least one input, clarifies 'answer' helps ensure accuracy, and lists quality options. However, it doesn't explain parameter interactions, format requirements (e.g., what constitutes valid URLs/base64), or the significance of defaults. The description adds some meaning but leaves many parameter details unclear.

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 clearly states the tool's purpose: '生成教学讲解视频' (generate instructional explanation videos). It specifies the domain (educational) and output type (video), though it doesn't explicitly differentiate from the sibling tool 'query_video_status' beyond their different functions. The verb '生成' (generate) is specific and the resource '教学讲解视频' (instructional explanation video) is well-defined.

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. While it mentions the sibling tool 'query_video_status' exists, there's no explicit comparison or context about when to choose one over the other. The only usage hint is the time estimate (10-20 minutes), but no when/when-not criteria or prerequisites are stated.

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

query_video_statusA

查询视频生成状态

Args: task_id: 视频生成任务ID

Returns: 包含任务状态信息与任务结果的字典，任务结果中的视频链接请用"链接文本"形式展示给用户，图片链接请用"链接文本"形式展示给用户

ParametersJSON Schema
NameRequiredDescriptionDefault
task_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.6/5.0
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 describes the return format and how to present links, which adds useful context beyond basic functionality. However, it doesn't mention error handling, rate limits, authentication needs, or whether this is a read-only operation (though implied by '查询' - query).

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 appropriately sized with three sentences: purpose statement, parameter explanation, and return format guidance. It's front-loaded with the core purpose and efficiently covers necessary information without wasted words. The bilingual presentation is slightly less structured but remains clear.

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 moderate complexity (status query with one parameter), no annotations, but with output schema present, the description provides good coverage. It explains the parameter meaning and return format specifics (including link presentation instructions), which complements the structured output schema well. The main gap is lack of error/edge case handling information.

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?

The schema description coverage is 0%, so the description must compensate. It clearly explains that 'task_id' is a '视频生成任务ID' (video generation task ID), adding essential semantic meaning not present in the bare schema. This adequately documents the single parameter's purpose.

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 clearly states the tool's purpose as '查询视频生成状态' (query video generation status), which is a specific verb+resource combination. It distinguishes from the sibling 'gen_video' by focusing on status checking rather than video generation. However, it doesn't explicitly contrast with the sibling tool in the description text itself.

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 context through the parameter 'task_id' and the mention of '视频生成任务ID' (video generation task ID), suggesting this tool should be used after initiating a video generation task. However, it doesn't provide explicit guidance on when to use this versus alternatives or any prerequisites beyond needing a task ID.

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.

  1. 2 tool updatesv1.0.0
    • Changedgen_video5 fields changed
      • removedInput schema / properties / answer / title
        Removed value: -"Answer"
      • removedInput schema / properties / answer_images / title
        Removed value: -"Answer Images"
      • removedInput schema / properties / quality / title
        Removed value: -"Quality"
      • removedInput schema / properties / question / title
        Removed value: -"Question"
      • removedInput schema / properties / question_images / title
        Removed value: -"Question Images"
    • Changedquery_video_status1 field changed
      • removedInput schema / properties / task_id / title
        Removed value: -"Task Id"
  2. 2 tool updates
    • First observedgen_video
    • First observedquery_video_status
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TDQS

A3.5/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: gen_video creates educational videos, while query_video_status checks the status of those video generation tasks. There is no overlap in functionality, and an agent would never confuse one for the other.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern: gen_video and query_video_status. The naming is clear, predictable, and uses the same snake_case convention throughout.

Tool Count3/5

With only 2 tools, the server feels thin for a video generation service. While the core create+query workflow is covered, typical video generation domains might include additional operations like listing tasks, canceling tasks, or managing templates. The count is borderline but functional.

Completeness4/5

The server covers the essential create and query operations for video generation, forming a complete lifecycle. However, there are minor gaps: no tool to list or manage existing tasks, and no ability to cancel a task or adjust video parameters after submission. Agents can work around this, but it's not fully comprehensive.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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