deploy-mcp — for Claude Code deploy-mcp, futuresearch-python, community, for Claude Code, ide skills, GitHub Actions workflow, Helm chart management, Kubernetes scaling, AI coding workflow automation, Semantic analysis, Claude Code

v1.0.0
GitHub

About this Skill

Perfect for AI Coding Agents needing streamlined MCP server deployment and management for GitHub Actions and Helm workflows. deploy-mcp is a GitHub Actions-based AI agent skill for automating MCP server deployment and scaling, designed for seamless integration with Claude Code and AI coding workflows.

Features

Deploy MCP server using GitHub Actions
Scale replicas via Helm values or kubectl
Monitor deployment with GitHub Actions and kubectl
Automate build and push of Docker images
Manage secrets with SOPS-decrypted values

# Core Topics

for Claude Code GitHub Actions workflow Helm chart management Kubernetes scaling AI coding workflow automation Semantic analysis
futuresearch futuresearch
[18]
[3]
Updated: 3/19/2026

Agent Capability Analysis

The deploy-mcp skill by futuresearch is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for for Claude Code, GitHub Actions workflow, Helm chart management.

Ideal Agent Persona

Perfect for AI Coding Agents needing streamlined MCP server deployment and management for GitHub Actions and Helm workflows.

Core Value

Empowers agents to automate deployment, scaling, and monitoring of MCP servers using GitHub Actions and Helm, with features like layered values for staging and production environments, and SOPS-decrypted secrets management for secure API key handling.

↓ Capabilities Granted for deploy-mcp

Automating MCP server deployment to staging and production environments
Scaling replicas via Helm values or kubectl for efficient resource allocation
Monitoring and debugging MCP server deployments using GitHub Actions and kubectl

! Prerequisites & Limits

  • Requires GitHub Actions and Helm setup
  • Limited to MCP server deployment and management
  • Needs SOPS for secrets management
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deploy-mcp

Unlock efficient AI research with deploy-mcp, an AI agent skill for Claude Code. Automate MCP server deployment and scaling for seamless AI coding...

SKILL.md
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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.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is deploy-mcp?

Perfect for AI Coding Agents needing streamlined MCP server deployment and management for GitHub Actions and Helm workflows. deploy-mcp is a GitHub Actions-based AI agent skill for automating MCP server deployment and scaling, designed for seamless integration with Claude Code and AI coding workflows.

How do I install deploy-mcp?

Run the command: npx killer-skills add futuresearch/futuresearch-python/deploy-mcp. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for deploy-mcp?

Key use cases include: Automating MCP server deployment to staging and production environments, Scaling replicas via Helm values or kubectl for efficient resource allocation, Monitoring and debugging MCP server deployments using GitHub Actions and kubectl.

Which IDEs are compatible with deploy-mcp?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for deploy-mcp?

Requires GitHub Actions and Helm setup. Limited to MCP server deployment and management. Needs SOPS for secrets management.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add futuresearch/futuresearch-python/deploy-mcp. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use deploy-mcp immediately in the current project.

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