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Cloudwright

by xmpuspusversion 1.10.0i★ –iRuns on your computer
Registry nameio.github.xmpuspus/cloudwright

Install

Overview

Description from the maker (original English)

Cloud architecture and migration planning with costs, controls, evidence, and deployable IaC.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "cloudwright": {
      "command": "uvx",
      "args": [
        "cloudwright-ai-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add cloudwright -- uvx cloudwright-ai-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "cloudwright": {
      "command": "uvx",
      "args": [
        "cloudwright-ai-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "cloudwright": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "cloudwright-ai-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account xmpuspus, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32461 servers in this category
  • Published to the registry more recently than 30% of the other 32461 servers in this category
  • Basic checks equal to or better than 100% of the other 32461 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Cloudwright safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.10.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, dotnet-mcp

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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dotnet-mcp

by Skymly

MCP server for .NET symbols, XAML, source generators, and restricted workspace edit

Runs on your computerUpdated today
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Cloudwright

*Design a cloud architecture or plan a migration. Get costs, controls, code, and checked evidence.*

pip install 'cloudwright-ai[cli]'

export ANTHROPIC_API_KEY=sk-ant-... cloudwright design "HIPAA healthcare API on AWS with Postgres and Redis"

Cloudwright turns one line of English into a typed spec, a cost breakdown, a control-mapped compliance report, and infrastructure code. A source estate and target become ordered migration waves, explicit costs, and evidence gates. It covers AWS, GCP, Azure and Databricks across 114 service keys. Only design, modify, chat and adr call a model. Every other command runs offline and needs no API key.

Quickstart · Migrations · Compliance · Agents · Docs · Changelog

A prompt produces a spec, a cost, a control-mapped report, and Terraform

  • Spec. Typed YAML you commit, diff and review. Everything below reads from it.
  • Cost. Per component and region-aware, with a confidence flag on every line.
  • Compliance. HIPAA, SOC 2, PCI-DSS, FedRAMP, GDPR, ISO 27001 and NIST 800-53 control IDs.
  • Infrastructure code. Terraform, OpenTofu, Pulumi (TypeScript or Python) and CloudFormation.
  • Diagrams. ASCII, Mermaid, D2, and a web canvas you can edit by hand.
  • An MCP server, so any coding agent runs the same checks inside its own loop.

Exports carry safe defaults. S3 gets a public-access block, SSE and versioning. RDS gets encryption, multi-AZ and deletion protection. EC2 gets IMDSv2. A compliance framework overrides the workload profile, and always forces encryption and high availability.

Quickstart

cloudwright design "HIPAA healthcare API on AWS with Postgres and Redis"

cloudwright cost spec.yaml --workload-profile medium cloudwright compliance spec.yaml --frameworks hipaa,soc2 cloudwright export spec.yaml --format terraform -o ./infra cloudwright plan spec.yaml --target terraform # proves it deploys, never applies cloudwright migrate demo # packaged migration proof, fully offline cloudwright chat --web # canvas at http://localhost:8765

Add --json before any subcommand for machine-readable output, or --stream to watch tokens arrive. Set ANTHROPIC_API_KEY or OPENAI_API_KEY for the four commands that need a model.

Migration plans stop when evidence is missing

The migration model covers infrastructure, applications, data, platforms, networks, facilities, and business services in one dependency graph. It works for on-premises, cloud, cross-cloud, hybrid, data-center, and application moves. It plans and checks work. It never copies data, applies infrastructure, switches traffic, or runs a cutover.

cloudwright migrate plan examples/migrations/ph-telco-project.yaml -o assessment.yaml

cloudwright migrate verify examples/migrations/ph-telco-project.yaml examples/migrations/ph-telco-evidence.yaml

The core has no telco fields. The first proof project selects an external ph_telco pack for subscriber, billing, usage-record, number-porting, privacy, recovery, and source-shutdown gates. A manufacturing ERP fixture proves the same planner works without that pack. Missing blocking evidence changes the result to closed: false and makes migrate verify exit with code 2. Each evidence file carries the deterministic assessment ID, so observations from an older target or cutover plan cannot close a revised assessment.

MCP clients use the same engine through plan_migration and verify_migration in the migration tool group.

See Migration planning and evidence for the file contract, Python API, HTTP routes, domain-pack format, limits, and recording commands.

Every finding carries the control ID it violates

Other tools scan infrastructure after you deploy it. Cloudwright maps each finding to its control before any resource exists. The fix then costs a spec edit instead of a change ticket. HIPAA 164.312(a)(2)(iv), SOC 2 CC6.1 and FedRAMP SC-28 come from the built-in scanner, with no extra tooling. Checkov folds into the same report when it sits on your PATH.

  • --oscal writes an OSCAL 1.1.2 component-definition with deterministic UUIDs.
  • --traceability prints the chain from component to resource to control to status.
  • cloudwright plan runs terraform validate against the export, and never applies.

The review needs no API key and no network

cloudwright review runs the scorer, the linter and the validator over a spec, and returns one severity-ranked report. The same three critics run inside cloudwright design. When blocking findings survive generation, the architect repairs the spec once and records the change in spec.metadata.critique. Pass Architect(repair=False) to turn that off.

Canvas edits never call the model, so they are instant and free

Add, drag, connect, edit and delete are deterministic frontend mutations. The Catalog drawer serves the resource list per provider and five approved multi-resource modules. Its standards check flags orphan connections, partial modules and missing tags. An intact module exports as a single Terraform module block, with the catalog's pinned source and version.

One MCP server reaches 11 coding agents

cloudwright integrate --harness claude-code       # exact wiring, in that client's format

cloudwright integrate --harness cursor --write # merge it into the right file cloudwright integrate --rules --agent-file claude # a gate block for CLAUDE.md

Do not hand-write the config. cloudwright integrate emits it for Claude Code, Cursor, Cline, Windsurf, GitHub Copilot, Zed, Codex CLI, Junie, Kiro and Antigravity. Aider gets a CLI-pipe recipe instead, because it speaks no MCP. Every client wants a different shape: Zed wants context_servers, Copilot wants servers, and Codex wants a TOML table.

The server exposes 24 tools in 10 groups: design, cost, validate, analyze, export, session, review, compliance, plan and migration. Full matrix in docs/integrations.md.

Offline commands grade, scan, compare, and plan

lint runs 10 anti-pattern checks. score grades 5 dimensions. analyze reports blast radius and single points of failure. policy enforces policy-as-code with 9 built-in rules. security scans the spec and the exported HCL. drift compares a design against a tfstate, and --remediate turns the gap into a cost, compliance and plan preview.

review, compliance and plan are above. See docs/cli-reference.md.

Python API

from cloudwright import ArchSpec

from cloudwright.cost import CostEngine from cloudwright.validator import Validator from cloudwright.exporter import export_spec

spec = ArchSpec.from_file("spec.yaml") priced = CostEngine().estimate(spec, workload_profile="medium") findings = Validator().validate(spec, compliance=["hipaa", "pci-dss"]) hcl = export_spec(spec, "terraform", output_dir="./infra")

v1.10.0 adds migration planning with evidence-based closure

  • One model covers the full estate. Infrastructure, data, applications, platforms, networks, facilities, and business services share one dependency graph.
  • Dependencies determine the waves. The planner schedules prerequisites first, rejects cycles, checks rollback paths, and reports unresolved mappings.
  • Closure needs evidence. Missing or failed blocking observations prevent closure and make migrate verify return exit code 2.
  • Industry rules stay outside the engine. Optional YAML packs add acceptance gates without adding industry fields to the core.
  • PH telco is the first proof. The product remains industry-neutral. A manufacturing ERP fixture runs through the same planner with no domain pack.

Earlier releases added control-ID mapping and plan (v1.5.0), the self-correcting architect and OSCAL (v1.6.0), cloudwright integrate (v1.7.0), the responsive dark-theme canvas (v1.8.0), and measured canvas interaction fixes (v1.9.0). Full history in CHANGELOG.md.

Compatibility

  • Python 3.12+
  • Models: Anthropic (Claude Sonnet, Haiku) and OpenAI (GPT-5+ family), auto-detected from env.
  • Clouds: AWS, GCP, Azure, Databricks. 114 service keys total.
  • Install variants: cloudwright-ai[cli], cloudwright-ai[web], cloudwright-ai-mcp.

Contributing, license, changelog

  • Contributing guide: CONTRIBUTING.md
  • License: MIT, see LICENSE
  • Full release history: CHANGELOG.md
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