Skip to main content
Glama
Sign In
tycenjmccann

open-media-mcp

by tycenjmccann
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

open-media-mcp

Open-weight video generation as MCP tools on Amazon Bedrock AgentCore Gateway, rendered on a scale-to-zero spot GPU in your own AWS account.

Any MCP agent (Claude Code, Kiro, Strands, LangGraph, your own) calls generate_video, gets a job_id back in under a second, and polls get_job for a presigned mp4, a poster frame and a provenance manifest. One cdk deploy builds everything, including the Gateway. Nothing listens on the internet, and no GPU bills while the queue is empty.

architecture

Interactive source: docs/architecture.html. Solid orange = the required job loop; dashed purple = optional add-ons.

Models

Backend

What it makes

License

wan2.2 (default)

Wan 2.2 A14B: text-to-video and first-frame (optionally first+last frame) to video, 720p, 16 fps, 2-8 s, silent. A soundtrack can be muxed on.

Apache-2.0

h3 (opt-in: you need a MiniMax license in the US, EU, UK and Korea)

MiniMax-H3 / Singularity v1.3: picture and stereo sound in one pass, up to 9 reference images, reference video and audio, keyframes on the timeline, lip-sync to a real song or VO, 768p, 24 fps, 5-15 s.

MiniMax H3 Community License: not licensed in the US, EU, UK or Republic of Korea without MiniMax's written license. Read THIRD_PARTY_MODELS.md.

Adding a model is one Python file in worker/backends/ (weights list + ComfyUI graph) and one JSON entry of limits. See Adding a backend.

Related MCP server: flickies

What enterprises get

Need

How the stack handles it

No public endpoint

AgentCore Gateway with AWS_IAM (SigV4) or CUSTOM_JWT (Cognito, Okta, Entra ID). There is no unauthenticated mode. GPU workers have no inbound rules; they pull from SQS.

Who did what

A Gateway REQUEST interceptor stamps the verified caller (tenant, principal) on every tool call; clients can't forge it. Jobs, uploads, quotas and list_jobs are tenant-scoped. Every call and job transition is logged.

Content safety

Prompts pass an Amazon Bedrock Guardrail (created for you with MEDIUM filters, tunable per category, or bring your own) before any GPU time is spent. Input images and 6 sampled output frames pass Amazon Rekognition moderation; flagged renders go to quarantine/ with no download URL (BLOCKED).

Provenance

Each output gets a manifest: input SHA-256s, backend, model files, seed, steps, ComfyUI commit, worker instance, guardrail and moderation results, output SHA-256. The mp4 carries ai_generated=true metadata and the license notice where one is required.

Chargeback

Every job records its GPU seconds, the live price of the instance it ran on, and its USD cost. cost_report totals it per label for showback or chargeback; the fleet total shows boot and idle overhead separately.

Cost control

Spot first across GPU types and AZs (on-demand switch is one tool call), one GPU per outstanding job up to maxInstances, workers remove themselves after idleMinutes with an empty queue, per-caller daily job cap, global queue cap.

Reliability

SQS lease heartbeats while rendering; a spot interruption notice hands the job straight back; crashed jobs retry 3 times, then dead-letter (alarmed). A 2-minute reconciler guarantees queued work always has a GPU coming. Idempotency keys make agent retries safe.

Data protection

Customer managed KMS key (rotation on) for the bucket, table, queues and topic; TLS-only bucket with public access blocked; encrypted EBS; IMDSv2; lifecycle expiry for inputs (7 d), outputs (30 d) and job records (90 d).

Networking

New VPC with no inbound path by default; privateNetworking=true puts workers in private subnets behind a NAT; or bring your own VPC and subnets. S3 and DynamoDB gateway endpoints included.

Operations

CloudWatch dashboard (queue, jobs, render time, GPUs, tool calls), alarms for dead letters, failure bursts and a stuck queue (SNS email), EventBridge Media Job Status Changed events for your own workflows, Session Manager access to workers.

The tools

Tool

Does

get_capabilities

Enabled backends with limits, prompt tag syntax, render estimates, safety settings, live GPU state.

create_upload

Presigned PUT URL (15 min) for an input image, video or audio; returns the key to pass to generate_video.

generate_video

Validate, screen the prompt, record, enqueue; returns job_id, eta_s, and whether a GPU is warm.

get_job

Status (QUEUED → STARTING → RUNNING → SUCCEEDED / FAILED / BLOCKED / CANCELLED), phase, timings, presigned video_url, poster_url, manifest_url.

list_jobs

Your recent jobs, newest first, optional status filter.

cancel_job

Cancel queued or running work (a running render is interrupted on the GPU).

gpu_capacity

status, warm (skip the cold start), release (scale to zero), use_on_demand / use_spot.

cost_report

Chargeback: exact GPU cost per job (time on the GPU x the live spot or on-demand price), USD per second of video, grouped by backend or by any job label (project, cost_center), plus the fleet's total instance-hours including boot and idle.

Deploy

Prerequisites: an AWS account with spot or on-demand G-instance quota (g7e.4xlarge = 16 vCPUs; check "All G and VT Spot Instance Requests" in Service Quotas), Node 18+, AWS CDK v2, Python 3.11+ for the client. Bedrock AgentCore Gateway must be available in the region (us-east-1 and us-west-2 have both the gateway and g7e).

git clone https://github.com/tycenjmccann/open-media-mcp && cd open-media-mcp
npm ci
npx cdk bootstrap            # once per account/region
npx cdk deploy               # ~8 min; prints GatewayUrl

Common options (all in cdk.json, override with -c key=value):

npx cdk deploy -c media:maxInstances=3 -c media:idleMinutes=30          # fan out to 3 GPUs, stay warm longer
npx cdk deploy -c media:auth=jwt \
  -c media:jwtDiscoveryUrl=https://cognito-idp.us-east-1.amazonaws.com/<pool>/.well-known/openid-configuration \
  -c media:jwtAllowedClients=<app-client-id>                            # your IdP instead of SigV4
npx cdk deploy -c media:privateNetworking=true                          # private subnets + NAT
npx cdk deploy -c media:guardrail=<guardrail-id>:<version>              # your company guardrail
npx cdk deploy -c media:alarmEmail=oncall@example.com
npx cdk deploy -c media:backends=wan2.2,h3        # add MiniMax-H3: license required in the US, EU, UK, KR (THIRD_PARTY_MODELS.md)

First boot downloads the weights from Hugging Face (Wan 2.2 is about 70 GB) and caches them in the stack's bucket, so every later boot, including spot replacements, comes from S3 in a few minutes.

Connect an agent

Claude Code / any stdio MCP client through AWS's SigV4 proxy:

{
  "mcpServers": {
    "open-media": {
      "command": "uvx",
      "args": ["mcp-proxy-for-aws@latest", "<GatewayUrl>", "--service", "bedrock-agentcore", "--region", "us-east-1"]
    }
  }
}

Python (the repo's client, also the smoke test):

pip install "mcp>=1.9" httpx botocore
export GATEWAY_URL=<GatewayUrl>
python examples/mcp_client.py tools
python examples/mcp_client.py generate --prompt "A lighthouse keeper climbs a spiral stair at dusk, lantern swinging" --out clip.mp4
python examples/mcp_client.py generate --first-frame still.png --prompt "She turns to camera and smiles" --aspect 9:16 --out c2.mp4

An agent running in AgentCore Runtime already has an execution role: grant it bedrock-agentcore:InvokeGateway on the gateway ARN and sign with its ambient credentials.

Speed and cost

Measured on one g7e.4xlarge (RTX PRO 6000 Blackwell, 96 GB), 5 s clips:

Backend

Quality

Render

wan2.2 720p

draft4 (lightx2v 4-step LoRAs)

79 s

wan2.2 720p

full (20 steps, cfg 3.5)

669 s (about 11 min)

h3 768p

full (20 steps)

about 3 min (a 15 s clip: about 19.5 min)

h3 768p

draft4

about 1 min

draft4, 79 s

full, 669 s

draft4

full

Same prompt and seed, both rendered through the MCP (frame at 2.5 s).

Iterate on draft4, then re-run the keeper on full with the same seed. The first ever boot (weights from Hugging Face, about 70 GB for Wan 2.2) reached its first render in about 6 minutes; later boots load from S3.

What a clip costs

GPU cost per 5 s clip on spot g7e.4xlarge (USD 1.27/hour in us-east-1 on 2026-11-02; on-demand is USD 4.00):

Here (your account)

Render

Cost per 5 s clip

Wan 2.2 720p, draft4

79 s

USD 0.03

Wan 2.2 720p, full

669 s

USD 0.24

MiniMax-H3 768p with audio, full

about 3 min

USD 0.06

Each warm-up adds a cold start (about USD 0.13) and the idle tail before scale-in (20 min, about USD 0.42), so the more clips per session, the cheaper each one gets. Fixed cost while idle: the KMS key (USD 1/month) and the S3 weight cache (about USD 1.6/month for Wan 2.2).

Hosted API list prices for a 5 s clip, read from each pricing page on 2026-11-02:

Hosted API

Tier

Per 5 s clip

fal, Wan 2.2 A14B (same model as wan2.2)

720p

USD 0.40

Replicate, Wan 2.2 A14B image-to-video

720p

about USD 1.00

fal, MiniMax H3-max

768p

USD 0.40

Google Vertex, Veo 3.1 / Fast / Lite (with audio)

720p

USD 2.00 / 0.50 / 0.25

Google, Omni Flash 1.1

720p

USD 0.51

xAI, Grok Imagine Video 1.5 / Lite

720p

USD 0.70 / 0.15

fal, Kling 2.6 Pro (audio on) / Kling 4.0

720p

USD 0.70 / 0.79

fal, Seedance 2.5

720p

USD 2.37

Runway Gen-4.5 · Luma Ray3.2

720p

USD 0.60 · 0.30

When this wins: volume and iteration (50 drafts in one session is about USD 2 all in), data that must stay in your account, one governed endpoint for many agents and teams. When an API wins: a handful of one-off clips (a single full Wan clip with a cold start costs about USD 0.79 here vs USD 0.40 on fal), or when you need the top closed models (Veo 3.1, Kling 4.0) for quality.

Repo layout

bin/app.ts, lib/open-media-mcp-stack.ts   CDK stack (everything, incl. AgentCore Gateway + target + interceptor)
tools.json                                 MCP tool schemas (the Gateway target's inline schema)
lambda/tools/                              the 8 tools + per-backend limits (backends.json)
lambda/interceptor/                        Gateway REQUEST interceptor: verified caller identity
worker/setup.sh                            per-boot bootstrap: uv venv, torch, ComfyUI, weights, systemd
worker/worker.py                           SQS loop, media prep, ComfyUI, moderation, provenance, S3, events
worker/backends/                           wan22.py, h3.py (add yours here)
examples/mcp_client.py                     SigV4 MCP client + end-to-end smoke test
examples/batch.py                          submit a JSON list of jobs, wait, download, write results + cost
docs/architecture.html                     the diagram above

Teardown

npx cdk destroy

The bucket, table and KMS key are retained by default (media:retainData), so outputs and the weight cache survive. Empty and delete them by hand, or deploy with -c media:retainData=false first.

Status

v0.1. Wan 2.2 is the tested default; the H3 backend's ComfyUI graph is a 1:1 port of the client we used for production music-video takes (unit-tested for graph equality). Next backends: Wan 2.2 S2V (audio-driven lip-sync, Apache-2.0), ACE-Step (music, Apache-2.0), Qwen-Image (stills for start frames, Apache-2.0).

License

Apache-2.0 for this code. Model weights are under their own licenses: see THIRD_PARTY_MODELS.md.

This server cannot be deployed

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

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers