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BlockRun MCP

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by BlockRunAI

blockrun_video

Turns text prompts into AI-generated MP4 videos. Optionally uses a seed image; polls until video is ready, with payment only on successful completion.

Instructions

Generate short AI videos via BlockRun x402 (async, client-polled).

Turns a text prompt (and optional seed image) into a short MP4 clip. The tool submits the job, then polls until the video is ready (typical total wall-time 60-180s; 5 min hard cap). Payment is settled only when upstream returns a finished video — if the job fails or we give up, you are not charged.

Models (Seedance defaults bumped to 720p + synced audio on the gateway):

  • azure/sora-2 ($0.10/sec, 720p + synced audio, text-to-video) — OpenAI Sora 2 via Azure AI Foundry. duration_seconds must be 4, 8, or 12 (4s default -> ~$0.40/clip). No image_url / RealFace.

  • xai/grok-imagine-video ($0.05/sec, 8s default -> $0.40/clip) — stylized, fast

  • bytedance/seedance-1.5-pro (~$0.092/sec, 720p + audio t2v, 5s default up to 10s) — cheapest Seedance, token-priced upstream

  • bytedance/seedance-2.0-fast (~$0.238/sec text · ~$0.140/sec image-to-video, 720p + audio, ~60-80s gen) — sweet-spot price/quality; supports BytePlus RealFace assets

  • bytedance/seedance-2.0 (~$0.298/sec text · ~$0.183/sec image-to-video, 720p + audio Pro) — highest quality; supports BytePlus RealFace assets

RealFace: to generate video of a SPECIFIC real person, first enroll them with blockrun_realface (returns a ta_xxxx asset id), then pass real_face_asset_id here with a Seedance 2.0 model. Mutually exclusive with image_url.

Returns a permanent blockrun-hosted MP4 URL (the gateway mirrors the asset to GCS so URLs don't expire).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoVideo model to usexai/grok-imagine-video
promptYesText description of the video to generate. E.g. 'a red apple slowly spinning on a wooden table', 'a hummingbird hovering near a red flower, ultra slow motion'
agent_idNoAgent identifier for budget tracking and enforcement.
image_urlNoOptional seed image URL for image-to-video generation
resolutionNoSeedance only: output resolution. Defaults to 720p. Higher resolutions cost more (token-priced upstream) — the final price is set by the 402 challenge, so the up-front estimate may understate 1080p/4K. Ignored by xAI/Sora.
aspect_ratioNoSeedance only: output aspect ratio, e.g. '9:16' for vertical/mobile, '16:9' for landscape. Defaults to the model's own default. Ignored by xAI/Sora.
generate_audioNoSeedance only: whether to generate a synced audio track. Defaults ON for text-to-video and OFF for image/RealFace-conditioned. The auto-generated audio is occasionally rejected by upstream moderation ('output audio may contain sensitive information') even for benign prompts — pass false to skip audio and avoid that failure. Ignored by xAI/Sora.
last_frame_urlNoSeedance only: first-and-last-frame interpolation. A second image URL that seeds the FINAL frame so the model tweens from image_url (first frame) → last_frame_url (last frame). Requires image_url; mutually exclusive with real_face_asset_id. Priced as image-to-video.
duration_secondsNoDuration to bill for (defaults to the model's default — 8s for xAI, 5s for Seedance; Seedance supports up to 10s).
real_face_asset_idNoBytePlus RealFace asset id (from blockrun_realface enroll/list) to generate video of a specific real person. Seedance 2.0 / 2.0-fast only. Mutually exclusive with image_url.
Behavior5/5

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

No annotations are provided, so the description fully covers behavioral traits: async polling, 5-minute hard cap, payment only on success, permanent URL. It also discloses failure modes like audio rejection by upstream moderation.

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 well-structured and front-loaded with purpose, then details. It is slightly verbose with pricing details that could be in schema descriptions, but remains clear and earns its length.

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?

No output schema exists, but the description explains the return value (permanent MP4 URL). It covers process, models, parameters, edge cases (RealFace, audio failure), and payment policy, making it complete for a complex video generation tool.

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?

With 100% schema coverage, baseline is 3, but the description adds significant meaning: model pricing, default durations, constraints (e.g., RealFace mutually exclusive with image_url), resolution/audio behavior, and which parameters are ignored by certain models.

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 generates short AI videos from a text prompt and optional seed image, outputting an MP4 clip. It distinguishes itself from sibling tools like blockrun_image (image generation) and blockrun_realface (face enrollment).

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?

The description provides explicit guidance on when to use the tool (to generate video) and when to use alternatives (e.g., blockrun_realface for enrolling faces first). It also gives model selection advice based on price/quality and feature compatibility, such as RealFace only with Seedance 2.0.

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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