# Host MCP Server with Flask: Stored Input and Output Artifacts

## Summary
Deploy a mcp server built with Flask on Ample using the generated downloads pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Flask: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the mcp server-specific behavior as your application code.

## Representative Queries
- Host mcp server with Flask: stored input and output artifacts
- Where can I host MCP server built with Flask?
- I need an input/output artifact contract, object metadata and access boundaries.

## Resource Requirements
- primitive:compute
- primitive:postgres
- primitive:s3-compatible-object-storage

## Infrastructure Requirements
1. **Compute**
   - **Status:** verified  
   - **Summary:** Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
   - **More Info:** [Compute](/content/discover/primitive/compute/index.html)

2. **Postgres**
   - **Status:** verified  
   - **Summary:** Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
   - **More Info:** [Postgres](/content/discover/primitive/postgres/index.html)

3. **S3-compatible object storage**
   - **Status:** verified  
   - **Summary:** Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.
   - **More Info:** [S3](/content/discover/primitive/s3-compatible-object-storage/index.html)

## Prerequisites
- A Flask project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template)
- A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
- A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables
- An Ample account token with servers:write, databases:read, buckets:read

## 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

## Workflow Steps
1. **Build and start**  
   pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.

2. **Implement the pattern on PostgreSQL**  
   The fixture's module implements generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.

3. **Wire object storage**  
   Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing. Keep private data in an unpublished bucket.

4. **Deploy**  
   Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op.  
   ```  
   ample deploy . --name <name> --public --start "python3 app.py" --env S3_ENDPOINT=<endpoint> --env S3_REGION=<region> --env S3_BUCKET=<bucket> --env S3_ACCESS_KEY_ID=<access_key> --env S3_SECRET_ACCESS_KEY=<secret_key>  
   ```

5. **Verify**  
   Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.  
   ```  
   ample logs  --kind build  
   ```

## Success Checks
1. **App Responds**  
   - **Kind:** http_get  
   - **Path:** /  
   - **Expect:** ample canary flask patterns

2. **Generated Downloads Self-test**  
   - **Kind:** http_get  
   - **Path:** /p/generated-downloads  
   - **Expect:** export=ok rows=3 (with ?token=...; 401 without)

## Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Flask major versions are not verified.  
- The mcp server itself (your export formats, retention policy and entitlement model) is application code and was not separately tested; the pattern checks are what was verified.

## Cost Estimate
- **Currency:** USD  
- **Monthly Amount:** 10.0  
- **Basis:** size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty

### Cost Components
1. App server   
   - Size: s-1vcpu-1gb   
   - Quantity: 1.0   
   - Monthly Amount: 5.0

2. Managed PostgreSQL database  
   - Size: s-1vcpu-1gb   
   - Quantity: 1.0   
   - Monthly Amount: 5.0

### Additional Note
Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

## Evidence Summary
- **Canary Run:** Flask pattern fixture deployed on Ample (pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template); checks passed for generated-downloads and configuration-secrets. Pattern proof: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3).

## Next Actions
- **[Browse the catalog index](https://api.ample.computer/v1/catalog)**  
- **[Search published recipes](https://api.ample.computer/v1/catalog/search)**  
- **[Prepare a side-effect-free deployment plan](https://api.ample.computer/v1/catalog/plan)**