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What is the Lavendly MCP server?

A Model Context Protocol server that exposes every Lavendly operation as an MCP tool, so any MCP-compatible agent can drive the whole pipeline end to end. It is available two ways: a hosted endpoint at https://mcp.lavendly.ai/mcp (Streamable HTTP, nothing to install) and a local server (npx @lavendly/mcp, stdio). Either way it can:
  • Browse and create workflows (describe shots → grouped scenes)
  • Estimate cost and get approval before spending
  • Generate scenes (one or all) and poll narratable status
  • Attach voiceovers and music tracks (materialized as real canvas nodes) and configure the mix
  • Review with a quality check (ffprobe + vision rubric) and scene thumbnails
  • Render the full video (free - it reuses generated clips) and poll until done
  • Publish or schedule the result to connected social channels

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Connect

Hosted (recommended). Point your agent at the remote endpoint and authenticate with a Bearer API key. Nothing to install, works from any machine: 
https://mcp.lavendly.ai/mcp
Claude Code: 
claude mcp add --transport http lavendly https://mcp.lavendly.ai/mcp \
  --header "Authorization: Bearer lv_live_xxx"
Cursor (~/.cursor/mcp.json): 
{
  "mcpServers": {
    "lavendly": {
      "url": "https://mcp.lavendly.ai/mcp",
      "headers": { "Authorization": "Bearer lv_live_xxx" }
    }
  }
}
Local. Run the server over stdio via npx instead: 
claude mcp add lavendly --env LAVENDLY_API_KEY=lv_live_xxx -- npx -y @lavendly/mcp
Get your key from Dashboard > Settings > API keys. Per-agent walkthroughs:

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Install it in your agent

Claude Code

CLI-native install.

OpenClaw

Autonomous, multi-channel agent.

Claude Desktop

Two-minute setup.

Cursor

Composer-ready.

Custom runtime

Tool-use via the SDK.

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What you get out of the box

The MCP server exposes 27 tools mapped 1:1 to documented API operations. See the full list in Tools reference.
DomainTools
Workflowslist_workflows, get_workflow, create_workflow, update_workflow, delete_workflow
Assetsimport_asset
Generateestimate_cost, generate_scene, get_generation_status
Audioget_workflow_audio, set_clip_native_audio, attach_track, update_track, detach_track
Rendercreate_render, get_render, list_renders, list_all_renders
Reviewquality_check, get_scene_frame
Publishlist_channels, publish_video, schedule_video
Capabilitiesget_status, get_schema, get_ledger, get_monthly_usage

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MCP is the surface, skills are how the agent uses it

The MCP server tells the agent what tools exist. It doesn’t tell the agent how to use them well. That’s what Agent skills are for: short operating manuals (markdown files) that the agent reads alongside the tool catalog. A good Lavendly skill teaches the agent:
  • The canonical call sequence (status → ledger → create → estimate → generate → review → render → publish)
  • When to use idempotency keys
  • How to interpret the cost preview before committing, and that render is free
  • When to regenerate a scene from a failed quality check (using the reroll_hint)
  • Domain-specific recipes (storyteller, multi-clip continuity, localized variants)
You can use the MCP server without a skill, the tool descriptions are self-explanatory enough that a capable agent will figure it out. But giving it a skill turns an “agent that can render videos” into “an agent that produces good videos cheaply.”

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Need an MCP-compatible runtime?

Any agent runtime that supports MCP can load the Lavendly server. Beyond the three first-party guides linked above, see:

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What the server does not do

  • It does not store your API key. The key comes from the MCP client: an env var for the local server, or the Authorization: Bearer header for the hosted endpoint.
  • It does not cache tool results, every call hits the live API so the agent always sees current state.
  • It does not advertise resources or prompts (only tools). Resources aren’t needed for an API wrapper and the absence keeps capability negotiation honest.