---

name: nvidia-video-search-and-summarization-video-analytics description: Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). This includes incidents, alerts, sensor data, and metrics. Use for any question about violations, alerts, incidents, object counts, speeds, occupancy, or anything that requires looking up recorded events. This is the primary way to answer a question that requires incidents, alerts and other metrics such as people counts and violations. license: Apache-2.0 metadata: version: 3.1.0 github-url: https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization tags: nvidia blueprint operational author: NVIDIA

Video Analytics (VA-MCP)

Queries incidents, alerts, and metrics stored in Elasticsearch via MCP JSON-RPC at port 9901.

ALWAYS run the commands below yourself and relay results to the user. Do NOT guess or describe — actually execute and report back.

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Deployment prerequisite

This skill reads from the Elasticsearch/VA-MCP stack brought up by the VSS alerts profile (either verification or real-time mode). Before any query:

1. Probe the VA-MCP endpoint:

   curl -sf --max-time 5 "http://${HOST_IP}:9901/mcp" >/dev/null 2>&1 || \
     curl -sf --max-time 5 "http://${HOST_IP}:9901/" >/dev/null
   

2. If the probe fails, ask the user:

   "The VSS alerts profile isn't running on $HOST_IP (VA-MCP unreachable). Which mode should I deploy — verification (CV) or real-time (VLM)?"

   • Answer → hand off to the /deploy skill with -p alerts -m <mode>. Return here once it succeeds.
   • If the user declines → stop. No incidents/alerts/metrics to query without the alerts stack up.

   (If your caller has granted explicit pre-authorization to deploy autonomously — e.g. the request says "pre-authorized to deploy prerequisites", or you are running in a non-interactive evaluation harness with that permission — skip the confirmation and invoke /deploy directly. Default the mode to verification unless the request specifies otherwise.)

3. If the probe passes, proceed.

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REQUIRED: Two-Step Pattern (copy this exactly)

Every query requires two shell commands run in sequence:

# Step 1: initialize — get session ID from response HEADER
SESSION_ID=$(curl -si -X POST http://localhost:9901/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"cli","version":"1.0"}},"id":0}' \
  | grep -i "mcp-session-id" | awk '{print $2}' | tr -d '\r')

# Step 2: call the tool using the session ID in the header
curl -s -X POST http://localhost:9901/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "mcp-session-id: $SESSION_ID" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"max_count":10}},"id":1}' \
  | grep '^data:' | sed 's/^data: //' | jq -r '.result.content[0].text'

The session ID comes from the response header mcp-session-id, not the body. Skipping Step 1 always results in Bad Request: Missing session ID.

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Tool Reference

Replace the -d payload in Step 2 with any of the following.

video_analytics__get_incidents

Parameter	Type	Description
source	string	Sensor ID or place name (optional)
source_type	string	sensor or place
start_time	string	ISO 8601: YYYY-MM-DDTHH:MM:SS.sssZ
end_time	string	ISO 8601
max_count	int	Max results (default: 10)
includes	list	Extra fields: objectIds, info
vlm_verdict	string	confirmed, rejected, or unverified

# Recent incidents (all sensors)
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"max_count":10}},"id":1}'

# For a specific sensor
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"source":"<sensor-id>","source_type":"sensor","max_count":20}},"id":1}'

# Confirmed (VLM-verified) only
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"vlm_verdict":"confirmed","max_count":10}},"id":1}'

video_analytics__get_incident

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incident","arguments":{"id":"<incident-id>","includes":["objectIds","info"]}},"id":1}'

video_analytics__get_sensor_ids

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_sensor_ids","arguments":{}},"id":1}'

video_analytics__get_places

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_places","arguments":{}},"id":1}'

video_analytics__get_fov_histogram

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_fov_histogram","arguments":{"source":"<sensor-id>","source_type":"sensor","start_time":"<ISO>","end_time":"<ISO>","object_type":"Person","bucket_count":10}},"id":1}'

video_analytics__analyze

analysis_type: max_min_incidents, average_speed, avg_num_people, avg_num_vehicles

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__analyze","arguments":{"source":"<sensor-id>","source_type":"sensor","start_time":"<ISO>","end_time":"<ISO>","analysis_type":"avg_num_people"}},"id":1}'

vst_sensor_list

-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"vst_sensor_list","arguments":{}},"id":1}'