Give your agent eyes on video.

Connect a client

claude mcp add --transport http supervision https://platform.endlessai.com/mcp \
  --header "X-Api-Key: svk_..."

{
  "mcpServers": {
    "supervision": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://platform.endlessai.com/mcp",
               "--header", "X-Api-Key: svk_..."]
    }
  }
}

from agents.mcp import MCPServerStreamableHttp

server = MCPServerStreamableHttp(params={
    "url": "https://platform.endlessai.com/mcp",
    "headers": {"X-Api-Key": "svk_..."},
})

import { StreamableHTTPClientTransport } from
  "@modelcontextprotocol/sdk/client/streamableHttp.js";

const transport = new StreamableHTTPClientTransport(
  new URL("https://platform.endlessai.com/mcp"),
  { requestInit: { headers: { "X-Api-Key": "svk_..." } } },
);

list_projects

list accessible projects

create_project

create a project (svt_ only)

get_project_config

read whitelisted analysis config (smart_scan, first_pass.auto_trigger, strategy.watch_for)

update_project_config

read-modify-write update of that same whitelisted config

list_videos

paginated video list for a project

get_video_status

single video processing status

upload_video_from_urlspends tokens

ingest video from a public URL; polls to terminal status; resume with get_video_status

upload_video_filespends tokens

upload a local file path (stdio-only, multipart; not on hosted transport)

trigger_smart_scanspends tokens

run a Smart Scan on a video: scene descriptions and summaries (dry_run:true = plan only, nothing is spent; returns run_id if the plan outlasts wait_seconds, default 45s)

get_smart_scan_results

scan status + summary merged (by video_id), or a dry-run plan (by run_id)

search_frames

find moments in a project's videos from a text description, or moments that look like a reference frame

search_scenes

text search over a project's scenes

list_face_persons

list face-gallery persons for a project

label_face_person

set/clear a face-gallery person's label

trigger_face_scanspends tokens

find the faces in a video and add them to the project's face gallery

get_usage

token/cost usage for a project, optional per-event list

ask_video_agentspends tokens

expensive, can take several minutes; the video agent answers a question about the project's videos with multi-step reasoning; returns answer or run_id

get_agent_run

poll/resume a prior ask_video_agent run by run_id; a completed run may also carry `correction` (a self-check found the run's own evidence contradicts the answer — the correction wins)

get_conclusions

site knowledge recorded for a project — claims from prior analyses with status (verified/proposed/refuted/stale), provenance and evidence refs; a claim is not a fact, quote `text` (returns enabled=false when site knowledge is off)