44 tools across 4 MCP servers for ARM architecture reference, documentation search, cloud migration, and edge AI deployment.
Plan and execute x86-to-ARM cloud migrations with confidence. 7 tools covering dependency analysis, instance selection, Docker compatibility, CI/CD generation, migration planning, Dockerfile generation, and performance benchmarking.
Server entry point: arm-cloud-migration-mcp
| Tool | Description |
|---|---|
scan_x86_dependencies(language, dependencies) |
Analyze a comma-separated dependency list for ARM compatibility. Reports which packages have arm64 support, which are x86-only, and suggests alternatives. Languages: python, nodejs, java, cpp, rust, go. Returns a migration readiness score. |
suggest_arm_cloud_instance(workload_profile, provider?) |
Recommend ARM instances across AWS (Graviton), Azure (Cobalt), GCP (Axion), and Oracle (Ampere). Workloads: web_server, database, ml_inference, ci_cd, hpc, general. Includes vCPUs, RAM, pricing, and monthly cost estimates. |
check_docker_arm_support(image_name) |
Check if a Docker base image supports arm64/aarch64. Reports multi-arch manifest status, ARM-specific performance notes, known issues, and pull commands. |
generate_ci_matrix(ci_platform, language?) |
Generate cross-architecture CI config for building and testing on both x86 and ARM. Platforms: github_actions, gitlab_ci, circleci, jenkins. Includes multi-arch Docker build steps. |
estimate_migration_effort(codebase_profile) |
Assess migration complexity with a phased timeline, risk areas, checklist, common blockers and solutions, quick wins, testing strategy, and rollback plan. Profiles: python_web, java_enterprise, cpp_native, nodejs_api, go_microservice, rust_systems. |
generate_arm_dockerfile(language) |
Generate a multi-stage ARM-optimized Dockerfile. Languages: python, nodejs/node, java, go, rust. Includes ARM base images, platform-specific build flags, and deployment best practices. |
compare_arm_vs_x86_perf(workload) |
Real-world benchmark comparisons for ARM vs x86. Workloads: web_server, database, ml_inference, ci_cd, hpc. Shows specific instances, metrics, performance ratios, and cost savings. |
> scan_x86_dependencies("python", "numpy,scipy,intel-mkl,tensorflow,pandas")
# x86 Dependency Scan: python (5 packages)
## Fully Compatible (4 packages)
numpy -- Native arm64 wheels available (uses OpenBLAS on ARM)
scipy -- Native arm64 wheels available
tensorflow -- ARM-optimized builds available (tensorflow-aarch64)
pandas -- Native arm64 wheels available
## x86-Only (1 package)
intel-mkl -- x86-only (Intel proprietary)
Alternatives: OpenBLAS, Arm Performance Libraries (ArmPL), BLIS
## Summary
Compatible: 4/5 (80%)
Needs work: 1/5 (20%)
Migration Readiness: 80/100 (HIGH)
> suggest_arm_cloud_instance("database", provider="aws")
# ARM Cloud Instances: database (AWS)
## Recommended Instances
r7g.large -- 2 vCPU, 16 GB RAM, $0.1008/hr (~$73/mo)
Graviton3, great for memory-intensive DB workloads
r7g.xlarge -- 4 vCPU, 32 GB RAM, $0.2016/hr (~$147/mo)
Mid-size production databases
r7g.2xlarge -- 8 vCPU, 64 GB RAM, $0.4032/hr (~$294/mo)
Large production workloads
im4gn.large -- 2 vCPU, 8 GB RAM + NVMe SSD, $0.1002/hr
I/O-intensive: Redis, Elasticsearch
## Cost Savings vs x86
20-40% lower cost vs equivalent r6i (Intel) instances
> check_docker_arm_support("postgres")
# Docker ARM Support: postgres
## Status: SUPPORTED
Multi-arch manifest: Yes (linux/amd64, linux/arm64, linux/arm/v7)
ARM base image: arm64v8/postgres
## Performance Notes
Up to 30% better price-performance on Graviton3 vs x86
## Known Issues
Extensions requiring native compilation may need ARM build tools
## Pull Commands
docker pull postgres # auto-selects platform
docker pull --platform linux/arm64 postgres # force ARM
> generate_ci_matrix("github_actions", language="python")
# GitHub Actions CI Matrix (Python + ARM)
name: CI
on: [push, pull_request]
jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, ubuntu-24.04-arm]
python-version: ['3.12']
runs-on: $
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: $
- run: pip install -e ".[test]"
- run: pytest
docker:
runs-on: ubuntu-latest
steps:
- uses: docker/setup-qemu-action@v3
- uses: docker/setup-buildx-action@v3
- uses: docker/build-push-action@v5
with:
platforms: linux/amd64,linux/arm64
push: false
> estimate_migration_effort("python_web")
# Migration Assessment: python_web
Complexity: LOW
Timeline: 6-11 days
## Phase Breakdown
1. Assessment (1-2 days) -- Dependency audit, compatibility check
2. Environment Setup (1 day) -- ARM CI runner, Docker multi-arch
3. Dependencies (2-3 days) -- Replace x86-only packages
4. Testing (1-3 days) -- Functional + performance validation
5. Deployment (1-2 days) -- Staged rollout with rollback plan
## Quick Wins
- Most Python packages have arm64 wheels
- Docker multi-arch builds handle most cases
- 20-40% cost savings on Graviton instances
> compare_arm_vs_x86_perf("web_server")
# ARM vs x86 Performance: web_server
## Benchmark: Nginx Static Throughput
ARM: c7g.xlarge (Graviton3) -- 145,000 RPS
x86: c6i.xlarge (Ice Lake) -- 116,000 RPS
Ratio: 1.25x (ARM 25% faster)
Cost: ARM $0.145/hr vs x86 $0.170/hr (15% cheaper)
## Benchmark: Node.js HTTP
ARM: c7g.xlarge -- 52,000 RPS
x86: c6i.xlarge -- 40,000 RPS
Ratio: 1.30x (ARM 30% faster)
## Key Insight
Web workloads see strong gains on Graviton3 due to
higher memory bandwidth and efficient branch prediction.
20-40% total cost savings when combining perf + pricing.
claude mcp add --transport stdio arm-cloud-migration -- \
uvx --from "git+https://github.com/yerry262/arm-reference-mcp.git" arm-cloud-migration-mcp
See the full Installation Guide for other editors and clients.