Agent-Skills.md

Agent Skills: Video Analytics (VA-MCP)

Query video analytics data and metrics from Elastic search via the VA-MCP

nvidiablueprintoperational
UncategorizedID: autohandai/community-skills/nvidia-video-search-and-summarization-video-analytics

Repository

autohandai/community-skills
autohandaiLicense: Apache-2.0
103

Install this agent skill to your local

pnpm dlx add-skill https://github.com/autohandai/community-skills/tree/HEAD/nvidia-video-search-and-summarization-video-analytics

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nvidia-video-search-and-summarization-video-analytics/SKILL.md

Skill Metadata

Name
nvidia-video-search-and-summarization-video-analytics
Description
Query video analytics data and metrics from Elastic search via the VA-MCP

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.

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.

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.

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}'