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Add gevals action (#2)
* feat: add initial eval tasks Signed-off-by: Calum Murray <[email protected]> * feat: add initial gevals workflow Signed-off-by: Calum Murray <[email protected]> --------- Signed-off-by: Calum Murray <[email protected]>
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‎.github/workflows/gevals.yaml‎

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name: Gevals MCP Evaluation
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on:
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# Weekly schedule - runs every Monday at 9 AM UTC
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schedule:
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- cron: '0 9 * * 1'
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# Manual trigger via PR comments
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issue_comment:
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types: [created]
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# Allow manual workflow dispatch for testing
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workflow_dispatch:
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inputs:
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task-filter:
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description: 'Regular expression to filter tasks (optional)'
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required: false
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default: ''
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verbose:
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description: 'Enable verbose output'
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required: false
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type: boolean
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default: false
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concurrency:
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# Only run once for latest commit per ref and cancel other (previous) runs.
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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env:
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GO_VERSION: 1.25
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defaults:
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run:
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shell: bash
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jobs:
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# Check if workflow should run based on trigger
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check-trigger:
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name: Check if evaluation should run
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runs-on: ubuntu-latest
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# Only run on PR comments in the main repository
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if: |
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github.event_name == 'schedule' ||
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github.event_name == 'workflow_dispatch' ||
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(github.event_name == 'issue_comment' &&
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github.event.issue.pull_request &&
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contains(github.event.comment.body, '/run-gevals'))
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outputs:
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should-run: ${{ steps.check.outputs.should-run }}
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pr-number: ${{ steps.check.outputs.pr-number }}
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steps:
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- name: Check trigger conditions
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id: check
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run: |
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if [[ "${{ github.event_name }}" == "issue_comment" ]]; then
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# Check if commenter is a maintainer (has write access)
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PERMISSION=$(curl -s -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
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"https://api.github.com/repos/${{ github.repository }}/collaborators/${{ github.event.comment.user.login }}/permission" \
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| jq -r '.permission')
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if [[ "$PERMISSION" == "admin" || "$PERMISSION" == "write" ]]; then
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echo "should-run=true" >> $GITHUB_OUTPUT
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echo "pr-number=${{ github.event.issue.number }}" >> $GITHUB_OUTPUT
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else
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echo "should-run=false" >> $GITHUB_OUTPUT
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echo "User ${{ github.event.comment.user.login }} does not have permission to trigger evaluations"
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fi
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else
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echo "should-run=true" >> $GITHUB_OUTPUT
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fi
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# Setup local Kind cluster for testing
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setup-cluster:
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name: Setup Kind cluster
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needs: check-trigger
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if: needs.check-trigger.outputs.should-run == 'true'
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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# Checkout PR branch if triggered by comment
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ref: ${{ github.event_name == 'issue_comment' && format('refs/pull/{0}/head', needs.check-trigger.outputs.pr-number) || github.ref }}
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- name: Setup Kind
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run: |
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# Install Kind if not already available
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if ! command -v kind &> /dev/null; then
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curl -Lo ./kind https://kind.sigs.k8s.io/dl/latest/kind-linux-amd64
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chmod +x ./kind
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sudo mv ./kind /usr/local/bin/kind
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fi
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# Create Kind cluster
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kind create cluster --name mcp-eval-cluster --wait 5m
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- name: Verify cluster
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run: |
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kubectl cluster-info
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kubectl get nodes
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# Keep cluster info for next job
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- name: Save kubeconfig
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run: |
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mkdir -p ${{ runner.temp }}/kubeconfig
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kind get kubeconfig --name mcp-eval-cluster > ${{ runner.temp }}/kubeconfig/config
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- name: Upload kubeconfig
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uses: actions/upload-artifact@v4
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with:
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name: kubeconfig
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path: ${{ runner.temp }}/kubeconfig/config
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retention-days: 1
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# Run gevals evaluation
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run-evaluation:
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name: Run MCP Evaluation
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needs: [check-trigger, setup-cluster]
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if: needs.check-trigger.outputs.should-run == 'true'
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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# Checkout PR branch if triggered by comment
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ref: ${{ github.event_name == 'issue_comment' && format('refs/pull/{0}/head', needs.check-trigger.outputs.pr-number) || github.ref }}
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- name: Download kubeconfig
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uses: actions/download-artifact@v4
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with:
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name: kubeconfig
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path: ${{ runner.temp }}/kubeconfig
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- name: Setup Go
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uses: actions/setup-go@v5
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with:
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go-version: ${{ env.GO_VERSION }}
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- name: Build MCP server
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run: make build
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- name: Start MCP server
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run: |
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export KUBECONFIG=${{ runner.temp }}/kubeconfig/config
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# Start MCP server in background
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./kubernetes-mcp-server --http --port 8080 &
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MCP_PID=$!
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echo "MCP_PID=$MCP_PID" >> $GITHUB_ENV
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# Wait for server to be ready
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for i in {1..30}; do
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if curl -s http://localhost:8080/health > /dev/null 2>&1; then
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echo "MCP server is ready"
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break
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fi
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echo "Waiting for MCP server to start... ($i/30)"
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sleep 2
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done
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- name: Run gevals evaluation
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uses: genmcp/gevals/.github/actions/gevals-action@main
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with:
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eval-config: 'evals/claude-code/eval.yaml'
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gevals-version: 'latest'
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task-filter: ${{ github.event.inputs.task-filter || '' }}
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output-format: 'json'
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verbose: ${{ github.event.inputs.verbose || 'false' }}
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upload-artifacts: 'true'
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artifact-name: 'gevals-results'
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fail-on-error: 'false'
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task-pass-threshold: '0.8'
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assertion-pass-threshold: '0.8'
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working-directory: '.'
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env:
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KUBECONFIG: ${{ runner.temp }}/kubeconfig/config
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# LLM Judge configuration
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JUDGE_BASE_URL: ${{ secrets.JUDGE_BASE_URL }}
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JUDGE_API_KEY: ${{ secrets.JUDGE_API_KEY }}
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JUDGE_MODEL_NAME: ${{ secrets.JUDGE_MODEL_NAME }}
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- name: Stop MCP server
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if: always()
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run: |
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if [ -n "$MCP_PID" ]; then
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kill $MCP_PID || true
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fi
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- name: Post results comment on PR
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if: github.event_name == 'issue_comment' && always()
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uses: actions/github-script@v7
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with:
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script: |
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const fs = require('fs');
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const path = require('path');
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// Find the results file
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const resultsPattern = /gevals-.*-out\.json/;
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const files = fs.readdirSync('.');
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const resultsFile = files.find(f => resultsPattern.test(f));
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if (!resultsFile) {
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await github.rest.issues.createComment({
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owner: context.repo.owner,
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repo: context.repo.repo,
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issue_number: ${{ needs.check-trigger.outputs.pr-number }},
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body: '❌ Gevals evaluation completed but no results file was found.'
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});
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return;
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}
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// Read and parse results
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const results = JSON.parse(fs.readFileSync(resultsFile, 'utf8'));
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// Calculate summary stats
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const totalTasks = results.length;
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const passedTasks = results.filter(r => r.taskPassed && r.allAssertionsPassed).length;
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const failedTasks = totalTasks - passedTasks;
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// Build comment body
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let comment = '## 🤖 Gevals MCP Evaluation Results\n\n';
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comment += `**Summary:** ${passedTasks}/${totalTasks} tasks passed\n\n`;
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if (failedTasks > 0) {
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comment += '### ❌ Failed Tasks\n\n';
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results.filter(r => !r.taskPassed || !r.allAssertionsPassed).forEach(task => {
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comment += `- **${task.taskName}**\n`;
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comment += ` - Task Passed: ${task.taskPassed ? '✅' : '❌'}\n`;
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comment += ` - Assertions Passed: ${task.allAssertionsPassed ? '✅' : '❌'}\n`;
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});
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comment += '\n';
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}
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comment += '### ✅ Passed Tasks\n\n';
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results.filter(r => r.taskPassed && r.allAssertionsPassed).forEach(task => {
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comment += `- ${task.taskName}\n`;
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});
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comment += `\n[View full results](https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }})`;
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await github.rest.issues.createComment({
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owner: context.repo.owner,
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repo: context.repo.repo,
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issue_number: ${{ needs.check-trigger.outputs.pr-number }},
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body: comment
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});
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# Cleanup
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cleanup:
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name: Cleanup Kind cluster
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needs: [setup-cluster, run-evaluation]
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if: always()
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runs-on: ubuntu-latest
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steps:
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- name: Delete Kind cluster
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run: |
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if command -v kind &> /dev/null; then
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kind delete cluster --name mcp-eval-cluster || true
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fi

‎evals/README.md‎

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# Kubernetes MCP Server Test Examples
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This directory contains examples for testing the **same Kubernetes MCP server** using different AI agents.
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## Structure
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```
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kube-mcp-server/
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├── README.md # This file
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├── mcp-config.yaml # Shared MCP server configuration
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├── tasks/ # Shared test tasks
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│ ├── create-pod.yaml
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│ ├── setup.sh
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│ ├── verify.sh
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│ └── cleanup.sh
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├── claude-code/ # Claude Code agent configuration
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│ ├── agent.yaml
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│ ├── eval.yaml
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│ └── eval-inline.yaml
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└── openai-agent/ # OpenAI-compatible agent configuration
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├── agent.yaml
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├── eval.yaml
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└── eval-inline.yaml
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```
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## What This Tests
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Both examples test the **same Kubernetes MCP server** using **shared task definitions**:
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- Creates an nginx pod named `web-server` in the `create-pod-test` namespace
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- Verifies the pod is running
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- Validates that the agent called appropriate Kubernetes tools
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- Cleans up resources
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The tasks and MCP configuration are shared - only the agent configuration differs.
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## Prerequisites
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- Kubernetes cluster (kind, minikube, or any cluster)
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- kubectl configured
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- Kubernetes MCP server running at `http://localhost:8080/mcp`
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- Built binaries: `gevals` and `agent`
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## Running Examples
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### Option 1: Claude Code
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```bash
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./gevals eval examples/kube-mcp-server/claude-code/eval.yaml
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```
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**Requirements:**
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- Claude Code installed and in PATH
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**Tool Usage:**
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- Claude typically uses pod-specific tools like `pods_run`, `pods_create`
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---
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### Option 2: OpenAI-Compatible Agent (Built-in)
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```bash
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# Set your model credentials
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export MODEL_BASE_URL='https://your-api-endpoint.com/v1'
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export MODEL_KEY='your-api-key'
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export MODEL_NAME='your-model-name'
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# Run the test
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./gevals eval examples/kube-mcp-server/openai-agent/eval.yaml
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```
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**Note:** Different AI models may choose different tools from the MCP server (`pods_*` or `resources_*`) to accomplish the same task. Both approaches work correctly.
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## Assertions
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Both examples use flexible assertions that accept either tool approach:
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```yaml
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toolPattern: "(pods_.*|resources_.*)" # Accepts both pod-specific and generic resource tools
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```
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This makes the tests robust across different AI models that may prefer different tools.
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## Key Difference: Agent Configuration
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### Claude Code (claude-code/agent.yaml)
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```yaml
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commands:
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argTemplateMcpServer: "--mcp-config {{ .File }}"
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argTemplateAllowedTools: "mcp__{{ .ServerName }}__{{ .ToolName }}"
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runPrompt: |-
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claude {{ .McpServerFileArgs }} --print "{{ .Prompt }}"
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```
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### OpenAI Agent (openai-agent/agent.yaml)
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```yaml
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builtin:
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type: "openai-agent"
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model: "gpt-4"
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```
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Uses the built-in OpenAI agent with model configuration.
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## Expected Results
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Both examples should produce:
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- ✅ Task passed - pod created successfully
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- ✅ Assertions passed - appropriate tools were called
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- ✅ Verification passed - pod exists and is running
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Results saved to: `gevals-<eval-name>-out.json`

‎evals/claude-code/agent.yaml‎

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kind: Agent
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metadata:
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name: "claude-code"
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builtin:
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type: "claude-code"

‎evals/claude-code/eval-inline.yaml‎

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kind: Eval
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metadata:
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name: "kubernetes-basic-operations"
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config:
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# Inline agent configuration - no separate agent.yaml file needed
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agent:
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type: "builtin.claude-code"
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mcpConfigFile: ../mcp-config.yaml
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llmJudge:
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env:
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baseUrlKey: JUDGE_BASE_URL
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apiKeyKey: JUDGE_API_KEY
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modelNameKey: JUDGE_MODEL_NAME
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taskSets:
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- glob: ../tasks/*/*.yaml
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assertions:
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toolsUsed:
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- server: kubernetes
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toolPattern: ".*"
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minToolCalls: 1
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maxToolCalls: 20

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