vicsee-mcp-serverofficial
A
licenseA
qualityB
maintenanceEnables AI agents to generate, edit, and upscale videos and images using VicSee's API, with support for multiple models and asynchronous task polling.
7
80
MIT
Agnes Video MCP Server
by kira4094
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agnes Video MCP ServerGenerate a 10-second video of a cat on a beach at sunset."
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.
Agnes Video MCP Server
MCP server for Agnes Video v2.0 (text-to-video / image-to-video generation), via OpenAI-compatible API.
Features
Text-to-Video: Generate videos from text prompts
Image-to-Video: Generate videos from a starting image or keyframes
Async workflow: Submit task → poll for result
Custom resolution, frame count, frame rate, duration, seed
Related MCP server: Sisif AI MCP Server
Requirements
Node.js >= 18
Agnes AI API key (free at https://platform.agnes-ai.com)
Environment Variables
Variable | Required | Default | Description |
| ✅ | — | Agnes AI API key |
|
| Model name |
Tools
agnes_video_create — Submit a video generation task
Parameter | Type | Required | Default | Description |
| string | ✅ | — | Text description |
| number | 1152 | Video width | |
| number | 768 | Video height | |
| number | 121 | Frames (8n+1: 121, 241, 361, 441) | |
| number | 24 | Frame rate | |
| number | Duration in seconds | ||
| number | Reproducible seed | ||
| string | Starting image URL | ||
| string |
|
agnes_video_poll — Check task progress
Parameter | Type | Required | Description |
| string | ✅ | Task ID returned from |
Usage
1. agnes_video_create → "a cat walking on beach at sunset"
→ Returns task_id: "task_xxx"
2. agnes_video_poll → task_id: "task_xxx"
→ Returns status + progress %, or video URL when doneLicense
MIT
Available Tools
2 toolsagnes_video_createA
Create a video generation task using Agnes Video v2.0. Video generation is ASYNCHRONOUS — submit a task, then use agnes_video_poll to check results. Supports text-to-video (prompt only) and image-to-video (with imageUrl). num_frames must follow 8n+1 pattern (e.g. 121, 241, 361, 441)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of the video to generate | |
| width | No | Video width in pixels | |
| height | No | Video height in pixels | |
| num_frames | No | Number of frames (8n+1 pattern: 121, 241, 361, 441) | |
| frame_rate | No | Frame rate (fps) | |
| duration | No | Video duration in seconds (alternative to num_frames) | |
| seed | No | Seed for reproducible results | |
| image_url | No | Starting image URL for image-to-video generation (optional) | |
| mode | No | Image-to-video mode (required if image_url is set). ti2vid for single image, keyframes for multi-image |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the asynchronous nature, the constraint on num_frames (8n+1 pattern), and the two modes. It does not mention rate limits, permissions, or the exact return value (e.g., task ID), but the core behavioral traits are well conveyed.
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 four sentences, front-loaded with purpose, and each sentence adds unique value: async nature, supported types, and frame constraint. No verbose or redundant phrasing.
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 9 parameters, no output schema, and the complexity of an async creation tool, the description covers the essential workflow and key constraints. It does not describe the return value (e.g., task ID) or error handling, but the presence of agnes_video_poll mitigates the need for full return details.
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 coverage is 100%, so baseline is 3. The description adds context for num_frames (the 8n+1 pattern) and notes that mode is required if image_url is set, which is not in the schema. However, most parameter meaning is already clear from schema descriptions, so the description adds limited extra value.
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 'Create a video generation task using Agnes Video v2.0', using a specific verb and resource. It distinguishes between text-to-video and image-to-video, and mentions the sibling tool agnes_video_poll for polling results, which clarifies its role in the workflow.
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 explicitly states that video generation is asynchronous and advises to use agnes_video_poll to check results. It also specifies the conditions for text-to-video vs. image-to-video and the required num_frames pattern. However, it does not explicitly state when not to use the tool or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agnes_video_pollA
Poll a video generation task by task_id to check progress. Returns task status and video URL when completed. The task_id is returned by agnes_video_create.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | Task ID returned from agnes_video_create (e.g. task_xxx) |
TDQS
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states returns status and video URL when completed, but does not disclose behavior for in-progress tasks, error handling, or rate limits. Basic transparency but gaps.
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?
Three sentences, front-loaded with verb and resource. No wasted words, each sentence adds essential information.
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?
Covers purpose, input origin, and return value. For a simple polling tool with one parameter and no output schema, it is fairly complete. Lacks details on status values or polling behavior, but adequate.
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 baseline is 3. Description adds value by explaining that task_id is returned by agnes_video_create, providing context beyond the schema's description.
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?
Description clearly states the verb 'poll' and the resource 'video generation task by task_id'. It distinguishes from the sibling 'agnes_video_create' by specifying that this tool checks progress, not creates.
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?
Description explains when to use (after creation) and where to get the input (task_id from agnes_video_create). It implies not to use before creation but lacks explicit alternatives or exclusions.
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.
2 tool updates
v1.0.0- First observed
agnes_video_create - First observed
agnes_video_poll
TDQS
A4.3/5.0
Disambiguation5/5
The two tools have completely distinct purposes: one creates a video generation task, the other polls for its result. There is no overlap or ambiguity.
Naming Consistency5/5
Both tools follow a consistent 'agnesis_video_verb' pattern using snake_case. 'create' and 'poll' are clear and parallel verbs.
Tool Count4/5
With only 2 tools, the server is minimal but sufficient for the core asynchronous workflow of creating and polling a video task. A slight expansion (e.g., cancel or list) could be beneficial but is not necessary.
Completeness4/5
The tool surface covers the essential create-and-poll lifecycle for video generation. However, there is no way to cancel a task or list all tasks, which are minor gaps.
Maintenance
ActivityStale
ResponsivenessSyncing
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
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Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI video generation from text prompts, status monitoring, and video management through the Sisif AI Video API.1MIT
- FlicenseNot gradedqualityDmaintenanceEnables generating videos from images and text prompts using Jimeng Video 3.0 API, supporting 5s/10s durations and automatic result polling.-
- AlicenseAqualityAmaintenanceEnables text-to-image and text-to-video generation via the Agnes AI API, with support for multiple models, async video generation, and automatic file downloads.61MIT
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