@floomhq/mcp-server
Floom MCP server — deploy Python apps instantly with auto-generated UI, REST API, and shareable URLs. 32 tools for AI agents.
README
floom
The production layer for AI agents. Deploy Python scripts as cloud automations with a shareable web UI, REST API, and MCP endpoint. No Docker, no infra, no YAML.
Tell your AI agent "deploy on floom" and your script is live in seconds.
What floom does
You write a Python script. floom turns it into:
- A web UI with typed inputs and outputs anyone can use
- A REST API other services can call
- An MCP endpoint AI agents can discover
- Managed secrets, version history, and scheduling
No Docker. No CI/CD. No infrastructure. Just Python in, production out.
Who it's for
- Developers who build Python automations and need to share them with non-technical people
- AI agencies deploying tools for clients
- Anyone using Claude Code or Cursor who wants to go from localhost to production instantly
Quick start
Install the Claude Code skill
git clone https://github.com/floomhq/floom.git ~/tmp/floom && ~/tmp/floom/scripts/setup
Deploy your first script
- Type
/floomin Claude Code - Point it at any Python script
- It adapts, tests in a sandbox, and deploys
- You get a live URL to share
Or tell any MCP-capable agent: "deploy this on floom"
Write a script from scratch
import os
from google import genai
def run(url: str) -> dict:
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
response = client.models.generate_content(
model="gemini-2.0-flash",
contents=f"Summarize the company at {url} in 3 sentences."
)
return {"summary": response.text}
That's it. floom handles the rest: sandbox testing, deployment, UI generation, secret injection.
How it works
- Write a Python script with a
run()function - Deploy via Claude Code (
/floom), Cursor, or any MCP-capable agent - Test runs automatically in an E2B sandbox (EU-hosted, SOC 2 certified)
- Share the live URL with anyone
See BUILDING.md for the full protocol: manifest format, input/output types, API reference.
API
All endpoints at dashboard.floom.dev/api/. Full reference in BUILDING.md.
# Upload code
curl -X POST https://dashboard.floom.dev/api/artifacts \
-H "Authorization: Bearer $API_KEY" \
-d '{"code": "def run(x): return {\"result\": x}", "manifest": {...}}'
# Test in sandbox
curl -X POST https://dashboard.floom.dev/api/test \
-d '{"artifactId": "...", "inputs": {"x": "hello"}}'
# Deploy
curl -X POST https://dashboard.floom.dev/api/deploy \
-d '{"artifactId": "..."}'
Features
| Feature | Status |
|---|---|
| Deploy from any AI agent | Available |
| Web UI with typed inputs/outputs | Available |
| REST API per automation | Available |
| Managed secrets | Available |
| Version history and rollback | Available |
| Sandbox testing (E2B) | Available |
| Scheduling (cron) | Available |
| File uploads (PDF, CSV, XLSX) | Available |
| MCP endpoint | Available (@floomhq/mcp-server) |
| Multi-file projects | Coming soon |
| Workspaces and teams | Planned |
Compared to
| floom | n8n | Vercel | Modal | |
|---|---|---|---|---|
| Deploy from AI agent | Yes | No | No | No |
| No-signup first deploy | Yes | No | No | No |
| Auto-generated UI | Yes | No | No | No |
| Python-native | Yes | Partial | No | Yes |
| Sandbox testing | Yes | No | No | No |
| 10-second deploy | Yes | No | Partial | No |
Stack
- Runtime: E2B sandboxes (EU-hosted, SOC 2 Type II)
- Backend: Convex
- Auth: Clerk
- Storage: Cloudflare R2
- Frontend: Next.js on Vercel
Links
- Dashboard: dashboard.floom.dev
- Docs: docs.floom.dev
- Protocol reference: BUILDING.md
FAQ
What is floom?
floom is the production layer for AI agents. You write a Python script, and floom turns it into a cloud automation with a shareable web UI, REST API, and MCP endpoint. No Docker, no infra, no YAML. Tell your AI agent "deploy on floom" and your script is live in seconds.
How is floom different from n8n, Zapier, or Make?
n8n, Zapier, and Make are visual workflow builders where you connect nodes in a GUI. floom is code-first: you write a Python function, and the platform generates the UI, API, and infrastructure automatically. There is no drag-and-drop editor. You deploy from your terminal or AI agent, and non-technical teammates use the generated web UI to run it.
How is floom different from Vercel or Modal?
Vercel deploys full web apps. Modal deploys Python functions but requires their SDK and decorators. floom deploys a single Python run() function and automatically generates a web UI with typed inputs and outputs, so anyone on your team can use it without writing code or calling an API.
Does floom work with Claude Code?
Yes. Install the skill with git clone https://github.com/floomhq/floom.git ~/tmp/floom && ~/tmp/floom/scripts/setup, then type /floom in Claude Code. It adapts your script, tests it in a sandbox, and deploys it.
Does floom work with Cursor, Windsurf, or other AI agents?
Yes. Any MCP-capable agent can deploy to floom. The protocol is a standard REST API: upload code + manifest, test, deploy. See BUILDING.md for the full API reference.
Is my data safe?
Code runs in E2B sandboxes, which are EU-hosted and SOC 2 Type II certified. Each run gets an isolated sandbox that is destroyed after execution. Secrets are encrypted at rest and injected at runtime, never exposed in logs or UI.
How much does it cost?
floom is free to start. See dashboard.floom.dev for current pricing details.
What languages does floom support?
Python. Your script needs a run() function that takes typed parameters and returns a dict. Dependencies are pip-installed automatically from the manifest.
Can I schedule automations?
Yes. Add a schedule field (cron syntax) to your manifest. For example, "schedule": "0 9 * * 1" runs every Monday at 9am. You can also set scheduleInputs with default values for scheduled runs.
Can I upload files (PDF, CSV, Excel)?
Yes. Use the file input type in your manifest. The user uploads a file through the web UI, and your run() function receives an R2 URL string. Download it with requests.get() or httpx.get() inside your function.
License
Open source. See LICENSE for details.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。