Observe GitHub MCP traffic with Kong AI Gateway

Uses: Kong Gateway AI Gateway decK

Deployment Platform:

Prerequisites

Series Prerequisites

This page is part of the Secure, govern and observe MCP traffic with Kong AI Gateway series.

Complete the previous page, Use Kong AI Gateway to govern GitHub MCP traffic before completing this page.

decK

decK is a CLI tool for managing Kong Gateway declaratively with state files. To complete this tutorial you will first need to install decK.

Reconfigure the AI Proxy Advanced plugin

Now, you can enable detailed logging for all configured models in the AI Proxy Advanced plugin. This captures request/response payloads and performance statistics. You can then scrape those statistics using the Prometheus plugin for monitoring and analysis. Apply the configuration below to enable logging for both models.

echo '
_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    config:
      balancer:
        algorithm: round-robin
      targets:
      - model:
          name: gpt-4
          provider: openai
          options:
            max_tokens: 512
            temperature: 1.0
        route_type: llm/v1/responses
        logging:
          log_payloads: true
          log_statistics: true
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        weight: 50
      - model:
          name: gpt-4o
          provider: openai
          options:
            max_tokens: 512
            temperature: 1.0
        route_type: llm/v1/responses
        logging:
          log_payloads: true
          log_statistics: true
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        weight: 50
' | deck gateway apply -

Enable the Prometheus plugin

Before you configure Prometheus, enable the Prometheus plugin on Kong Gateway:

echo '
_format_version: "3.0"
plugins:
  - name: prometheus
    config:
      status_code_metrics: true
      ai_metrics: true
' | deck gateway apply -

Configure Prometheus

Create a prometheus.yml file:

touch prometheus.yml

Now, add the following to the prometheus.yml file to configure Prometheus to scrape Kong Gateway metrics:

scrape_configs:
 - job_name: 'kong'
   scrape_interval: 5s
   static_configs:
     - targets: ['kong-quickstart-gateway:8001']

scrape_configs:
 - job_name: 'kong'
   scrape_interval: 5s
   static_configs:
     - targets: ['kong-quickstart-gateway:8100']

Run a Prometheus server, and pass it the configuration file created in the previous step:

docker run -d --name kong-quickstart-prometheus \
  --network=kong-quickstart-net -p 9090:9090 \
  -v $(PWD)/prometheus.yml:/etc/prometheus/prometheus.yml \
  prom/prometheus:latest

Prometheus will begin to scrape metrics data from Kong Gateway.

Validate

You can validate that the plugin is collecting metrics by generating traffic to the example service.

Now, we can run the script from the previous tutorial again:

for i in {1..5}; do
  echo -n "Request #$i — Model: "
  curl -s -X POST "http://localhost:8000/anything" \
    -H "Accept: application/json" \
    -H "apikey: hello_world" \
    -H "Content-Type: application/json" \
    --json "{
      \"tools\": [
        {
          \"type\": \"mcp\",
          \"server_label\": \"gitmcp\",
          \"server_url\": \"https://api.githubcopilot.com/mcp/x/repos\",
          \"require_approval\": \"never\",
          \"headers\": {
            \"Authorization\": \"Bearer $GITHUB_PAT\"
          }
        }
      ],
      \"input\": \"test\"
    }" | jq -r '.model'
  sleep 10
done

Run the following to query the collected kong_ai_llm_requests_total metric data:

curl -s 'localhost:9090/api/v1/query?query=kong_ai_llm_requests_total'

This should return something like the following:

{"status":"success","data":{"resultType":"vector","result":[{"metric":{"__name__":"kong_ai_llm_requests_total","ai_model":"gpt-4o","ai_provider":"openai","instance":"kong-quickstart-gateway:8001","job":"kong","workspace":"default"},"value":[1750768729.300,"21"]},{"metric":{"__name__":"kong_ai_llm_requests_total","ai_model":"gpt-4","ai_provider":"openai","instance":"kong-quickstart-gateway:8001","job":"kong","workspace":"default"},"value":[1750768729.300,"19"]},{"metric":{"__name__":"kong_ai_llm_requests_total","ai_model":"gpt-4.1","ai_provider":"UNSPECIFIED","instance":"kong-quickstart-gateway:8001","job":"kong","workspace":"default"},"value":[1750768729.300,"1"]}]}}

You can also view the Prometheus expression viewer by opening http://localhost:9090/graph in a browser.

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