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peypey84

Agnes Media MCP Server

by peypey84

agnes_generate_video

Create videos from text prompts, image URLs, or multiple keyframe images. Handles long generation asynchronously with status polling.

Instructions

Start an Agnes Video V2.0 generation (async). Text-to-video by default; pass image_url for image-to-video, or keyframe_urls (2+) for keyframe transitions. Video generation takes minutes, longer than an MCP request can block. This tool creates the task and waits up to wait_seconds for it to finish: if it completes in time, the mp4 is downloaded locally and its path returned; otherwise it returns the video_id immediately — poll agnes_get_video_status with that id (download:true) to fetch the result when ready. Duration = num_frames / frame_rate. num_frames must be <=441 and of the form 8n+1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoRandom seed for reproducible results.
widthNoVideo width (default 1152).
heightNoVideo height (default 768).
promptYesText description of the video content / motion.
image_urlNoImage URL for image-to-video.
frame_rateNoFrames per second (1-60).
num_framesNoFrame count (<=441, 8n+1). 81≈3s, 121≈5s, 241≈10s, 441≈18s at 24fps.
wait_secondsNoHow long to wait inline for completion before handing off (0-50s). Kept under the MCP client request timeout; if the video isn't ready, the video_id is returned to poll.
keyframe_urlsNo2+ keyframe image URLs for keyframe-transition mode.
negative_promptNoWhat to avoid in the video.
Behavior5/5

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

With no annotations, the description fully discloses async nature, blocking duration (wait_seconds), return behavior (local path vs. video_id), and constraints on num_frames (<=441, 8n+1). No contradictions.

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?

Single paragraph is functional and every sentence adds value, but could be more structured (e.g., bullet points for modes). Not too long, well-focused.

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 complex tool with 10 params and no output schema, description covers async flow, constraints, mode selection, and return values. Lacks explicit error handling details but sufficient for invocation.

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% (baseline 3), but description adds significant value: explains mode parameter usage in prose, clarifies wait_seconds logic, and provides frame count examples (81≈3s, etc.) 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?

Description clearly states it starts async video generation (Agnes V2.0), enumerates three modes (text-to-video, image-to-video, keyframe transitions), and distinguishes from siblings (agnes_get_video_status for polling, agnes_generate_image for images).

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

Usage Guidelines5/5

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

Explicitly explains when to use each mode, describes wait_seconds inline behavior, and directs polling with agnes_get_video_status upon timeout. Provides clear when-to-use and when-not-to-use guidance.

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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