nps

# NPS Skill -- National Park Service Queries You can query the **NPS Agent** for information about U.S. national parks. The NPS Agent is an AI assistant running in the `nps-agent` namespace that has access to the National Park Service API. It can answer questions about parks, alerts, campgrounds, events, and visitor centers. ## How It Works The NPS Agent runs as a standalone service with its own model and MCP tools. You send questions via HTTP and receive natural language answers. Authentication is handled transparently by the AuthBridge -- you just make the call. ## Query the NPS Agent ```bash RESPONSE=$(curl -s --max-time 300 -X POST \ http://nps-agent.nps-agent.svc.cluster.local:8080/invocations \ -H "Content-Type: application/json" \ -d '{"input": [{"role": "user", "content": "Your question about national parks here"}]}') echo "$RESPONSE" | python3 -c "import sys,json; o=json.load(sys.stdin)['output']; print(next(c['text'] for m in reversed(o) for c in m.get('content',[]) if 'text' in c))" ``` **Important:** The NPS Agent may take up to 60 seconds on the first request (cold start). Use `--max-time 300` to allow for this. ## Input Format The `/invocations` endpoint accepts JSON with an `input` array of messages: ```json {"input": [{"role": "user", "content": "What national parks are in California?"}]} ``` ## Output Format The response follows the MLflow ResponsesAgent format: ```json { "output": [ { "type": "message", "role": "assistant", "content": [ { "type": "output_text", "text": "California has nine national parks..." } ] } ] } ``` Extract the answer: ```bash echo "$RESPONSE" | python3 -c "import sys,json; o=json.load(sys.stdin)['output']; print(next(c['text'] for m in reversed(o) for c in m.get('content',[]) if 'text' in c))" ``` ## What the NPS Agent Can Answer The agent has 5 MCP tools connected to the NPS API: | Tool | What It Does | Example Question | |------|-------------|-----------------| | `search_parks` | Find parks by state, code, or keyword | "What parks are in Utah?" | | `get_park_alerts` | Current alerts and hazards | "Are there any alerts for Yellowstone?" | | `get_park_campgrounds` | Campground info and amenities | "What campgrounds are at Grand Canyon?" | | `get_park_events` | Upcoming events and activities | "What events are happening at Acadia?" | | `get_visitor_centers` | Visitor center locations and hours | "Where are the visitor centers at Zion?" | ## Examples ### Find parks in a state ```bash RESPONSE=$(curl -s --max-time 300 -X POST \ http://nps-agent.nps-agent.svc.cluster.local:8080/invocations \ -H "Content-Type: application/json" \ -d '{"input": [{"role": "user", "content": "What national parks are in Colorado?"}]}') echo "$RESPONSE" | python3 -c "import sys,json; o=json.load(sys.stdin)['output']; print(next(c['text'] for m in reversed(o) for c in m.get('content',[]) if 'text' in c))" ``` ### Check park alerts ```bash RESPONSE=$(curl -s --max-time 300 -X POST \ http://nps-agent.nps-agent.svc.cluster.local:8080/invocations \ -H "Content-Type: application/json" \ -d '{"input": [{"role": "user", "content": "Are there any current alerts or closures at Yellowstone National Park?"}]}') echo "$RESPONSE" | python3 -c "import sys,json; o=json.load(sys.stdin)['output']; print(next(c['text'] for m in reversed(o) for c in m.get('content',[]) if 'text' in c))" ``` ### Get campground info ```bash RESPONSE=$(curl -s --max-time 300 -X POST \ http://nps-agent.nps-agent.svc.cluster.local:8080/invocations \ -H "Content-Type: application/json" \ -d '{"input": [{"role": "user", "content": "What campgrounds are available at the Grand Canyon and what amenities do they have?"}]}') echo "$RESPONSE" | python3 -c "import sys,json; o=json.load(sys.stdin)['output']; print(next(c['text'] for m in reversed(o) for c in m.get('content',[]) if 'text' in c))" ``` ## Health Check Verify the NPS Agent is running: ```bash curl -s http://nps-agent.nps-agent.svc.cluster.local:8080/ping ``` Returns `200 OK` if healthy. ## Run Agent Evaluation You can trigger an evaluation of the NPS Agent. This runs 6 test cases (parks by state, park codes, campgrounds, alerts, visitor centers) and checks that expected facts appear in the responses. ### Trigger an eval run ```bash oc create job nps-eval-$(date +%s) --from=cronjob/nps-eval -n nps-agent ``` ### Check eval status ```bash oc get jobs -n nps-agent -l component=eval --sort-by='{.metadata.creationTimestamp}' ``` ### Read eval results ```bash JOB_NAME=$(oc get jobs -n nps-agent -l component=eval --sort-by='{.metadata.creationTimestamp}' -o jsonpath='{.items[-1].metadata.name}') oc logs -l job-name=$JOB_NAME -n nps-agent ``` The eval output shows pass/fail for each test case, expected facts found, latency per query, and an overall summary. Results are also logged to the NPSAgent experiment in MLflow. ### Quick eval (3 test cases only) To run a faster eval, create the job manually: ```bash oc create job nps-eval-quick -n nps-agent --image=image-registry.openshift-image-registry.svc:5000/nps-agent/nps-agent:latest -- python3 /eval/run_eval.py --quick --standalone ``` ## Error Handling | Error | Meaning | Action | |-------|---------|--------| | Connection refused | NPS Agent pod is down or not deployed | Check `oc get pods -n nps-agent` | | Timeout (>300s) | Agent is processing a complex query or cold starting | Retry with a simpler question | | Empty response | Agent couldn't find relevant data | Try a more specific query (include park name or state code) | | 500 error | Agent encountered an internal error | Check NPS Agent logs: `oc logs deployment/nps-agent -n nps-agent` |