KS
Killer-Skills

deploy-mcp — AI agent skill

v1.0.0
GitHub

About this Skill

Perfect for DevOps Agents needing efficient MCP server deployment and scaling capabilities. deploy-mcp is a skill that automates the deployment and scaling of MCP servers, providing a robust foundation for AI research teams to work efficiently.

Features

Deploying MCP servers using GitHub Actions
Scaling replicas via Helm values
Monitoring deployments with kubectl
Automating build and push of Docker images
Managing secrets with SOPS-decrypted values

# Core Topics

futuresearch futuresearch
[17]
[3]
Updated: 3/13/2026

Quality Score

Top 5%
36
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add futuresearch/everyrow-sdk

Agent Capability Analysis

The deploy-mcp MCP Server by futuresearch is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for AI agent skill, for Claude Code, MCP server deployment.

Ideal Agent Persona

Perfect for DevOps Agents needing efficient MCP server deployment and scaling capabilities.

Core Value

Empowers agents to automate MCP server deployment using GitHub Actions and Helm, streamlining workflow management and improving productivity with features like layered values in `values.yaml` and SOPS-decrypted secrets in `values.secrets.staging.yaml`.

Capabilities Granted for deploy-mcp MCP Server

Automating MCP server deployment to staging and production environments
Scaling replicas via Helm values or temporary adjustments with `kubectl`
Monitoring and debugging deployments with `gh run list` and `kubectl rollout status`

! Prerequisites & Limits

  • Requires GitHub Actions workflow setup
  • Dependent on Helm for deployment
  • Limited to MCP server deployment and scaling
Project
SKILL.md
3.4 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8
SKILL.md
Readonly

Deploying the MCP Server

Quick Deploy

Staging (from main)

bash
1gh workflow run "Deploy MCP Server" -f branch=main -f deploy_staging=true

Production (from main)

bash
1gh workflow run "Deploy MCP Server" -f branch=main -f deploy_production=true

Both environments

bash
1gh workflow run "Deploy MCP Server" -f branch=main -f deploy_staging=true -f deploy_production=true

From a feature branch

bash
1gh workflow run "Deploy MCP Server" -f branch=feat/my-branch -f deploy_staging=true

Monitoring a Deploy

bash
1# Watch the workflow run 2gh run list --workflow="Deploy MCP Server" --limit 3 3gh run watch <run-id> 4 5# Check pod rollout 6kubectl rollout status deploy/futuresearch-mcp-staging -n futuresearch-mcp-staging --timeout=5m 7 8# Verify pods are running 9kubectl get pods -n futuresearch-mcp-staging -o wide

How It Works

The GitHub Actions workflow (.github/workflows/deploy-mcp.yaml) does:

  1. Checks — ruff lint + pytest on the target branch
  2. Build & push — Docker image to GAR, tagged with short SHA (+ latest on main)
  3. Deploy — Helm upgrade with layered values:
    • values.yaml — base config
    • values.staging.yaml — staging overrides (MCP_SERVER_URL, REDIS_DB, replicaCount, host)
    • values.secrets.staging.yaml — SOPS-decrypted secrets (Supabase, API keys)

The deploy uses --atomic so it auto-rolls back on failure.

Scaling Replicas

Via Helm values (persistent)

Edit futuresearch-mcp/deploy/chart/values.staging.yaml:

yaml
1replicaCount: 2 # Change this

Commit, push, and redeploy.

Via kubectl (temporary, resets on next deploy)

bash
1# Staging 2kubectl scale deploy futuresearch-mcp-staging -n futuresearch-mcp-staging --replicas=3 3 4# Take offline 5kubectl scale deploy futuresearch-mcp-staging -n futuresearch-mcp-staging --replicas=0

Environments

EnvironmentNamespaceHostRedis DB
Stagingfuturesearch-mcp-stagingmcp-staging.futuresearch.ai14
Productionfuturesearch-mcpmcp.futuresearch.ai(default in values.yaml)

Both environments hit the same production FutureSearch API — there is no staging API.

Updating Secrets

bash
1# View current secrets 2sops -d futuresearch-mcp/deploy/chart/secrets.staging.enc.yaml 3 4# Update a value 5sops --set '["secrets"]["data"]["KEY_NAME"] "new-value"' futuresearch-mcp/deploy/chart/secrets.staging.enc.yaml

Commit the encrypted file and redeploy.

Key Files

FilePurpose
.github/workflows/deploy-mcp.yamlCI/CD workflow (checks → build → deploy)
futuresearch-mcp/deploy/chart/values.yamlBase Helm values
futuresearch-mcp/deploy/chart/values.staging.yamlStaging overrides
futuresearch-mcp/deploy/chart/secrets.enc.yamlProduction secrets (SOPS)
futuresearch-mcp/deploy/chart/secrets.staging.enc.yamlStaging secrets (SOPS)
futuresearch-mcp/deploy/DockerfileServer container image

Gotchas

  • Branch protection on main: Can't push directly — create a PR and merge first, then deploy from main.
  • SOPS decryption requires GCP IAM: Run gcloud auth application-default login if decryption fails.
  • Concurrent deploys: Workflow uses cancel-in-progress: false — if a deploy is running, the next one queues.
  • Atomic rollback: --atomic means a failed deploy auto-reverts to the previous release. Check helm history if this happens.

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