### Host mcp server with Flask: worker execution and job state

Deploy a mcp server built with Flask as a public web service plus a dedicated worker process on Ample. `ample plan --write` discovers both services and their shared database and writes ample.toml; `ample up` reconciles the topology: a managed PostgreSQL 16 database, a public web microVM and a private worker microVM (kind worker, no public URL), both reading DATABASE_URL. Verified on Flask: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1). Not separately tested: your job payloads, retry policy and scheduling; treat the mcp server-specific behavior as your application code.

#### Representative Queries
- Host mcp server with Flask: worker execution and job state
- Where can I host MCP server built with Flask?
- I need a separately runnable worker, durable job records and idempotent retries; not a managed queue.

#### Resource Requirements
- `primitive:compute`
- `primitive:postgres`

#### Infrastructure Requirements
- **Compute**: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
- **Postgres**: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.

#### Framework
- **Flask**

#### Workload
- **MCP server**

#### Release Status
- Published

#### Support Status
- Verified

#### Execution Status
- Ready

#### Prerequisites
- A repository with the web app and the worker as separate service directories (the fixture uses apps/web and apps/worker), each building and starting with the documented commands.
- Both services read DATABASE_URL at runtime and share one jobs table.
- An Ample account token with servers:write and databases:write.

#### Workflow Steps
1. **Plan the topology**: Run the planner once. It discovers the web and worker services, infers kind = "worker" for the process without a port, declares the database each service needs, and writes ample.toml.
   - Command: `ample plan --write .`
2. **Share one database**: Keep a single [databases.main] with engine = "postgres", drop any per-service database the planner added.
   - Command: `ample plan --offline .`
3. **Apply**: Reconcile the whole manifest in dependency order: the database first, then both services.
   - Command: `ample up .`
4. **Verify**: Fetch the web service URL and its worker status route and confirm the worker processed the enqueued job.
   - Command: `ample logs --kind runtime`

#### Tested Configuration
- **Template**: python-3.12
- **Runtime**: python
- **Size**: s-1vcpu-1gb
- **Install**: `python3 -m pip install --target .ample/python -r requirements.txt`
- **Start**: `PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 app.py`

#### Cost Estimate
- **Currency**: USD
- **Monthly Amount**: 15.0
- **Components**:
  - Web server: monthly amount 5.0
  - Worker server: monthly amount 5.0
  - Managed PostgreSQL database: monthly amount 5.0

#### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb for both services with the example fixture; other sizes, templates, and Flask major versions are not verified.
- The worker is a single long-running process supervised in its own microVM; there is no scheduler, cron, or horizontal worker scaling in this recipe.

#### Success Checks
- Web service responds on its public URL.
- Worker consumed the enqueued job.

#### Evidence Summary
- Flask web service plus a separate Python worker process deployed on Ample. The web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database.