# Migrating Prompts to FastMCP 2.9.0+ Patterns
If you have existing prompts generated with older templates, follow these steps:
## Step 1: Update Type Annotations
**Before**:
```python
from typing import Annotated
@mcp.prompt()
def my_prompt(
data: Annotated[dict, Field(description="...")],
) -> str:
```
**After**:
```python
@mcp.prompt
def my_prompt(
data: dict[str, str] = Field(description="..."),
) -> str:
```
**Changes**:
1. Add type parameters to dict: `dict` → `dict[str, str]` or `dict[str, Any]`
2. Remove `Annotated[]` wrapper
3. Use `Field()` directly with equals sign
4. Remove `()` from `@mcp.prompt` decorator
## Step 2: Update Optional Parameters
**Before**:
```python
format: Annotated[
str | None,
Field(description="...", default="json"),
] = "json",
```
**After**:
```python
format: str = Field(
default="json",
description="..."
),
```
**Changes**:
1. Move default into Field()
2. Remove redundant `= "json"` after annotation
3. Type becomes just `str` (not `str | None`) when default is provided
## Step 3: Update Imports
Remove unused imports:
- `from typing import Annotated` (if not used elsewhere)
- `from typing import Literal` (if not used)
## Step 4: Test Changes
```bash
# Run tests
pytest tests/prompts/test_your_prompt.py -v
# Test locally with cmcp
make test-local
# Deploy and verify
make deploy
```
## Step 5: Verify MCP Arguments
```bash
# Connect to your server
cmcp 'your-server-url' prompts/list
# Should show arguments array populated:
# "arguments": [
# {"name": "data", "description": "...", "required": true},
# ...
# ]
```
## Example: Complete Before/After
### Before (Old Pattern)
```python
from typing import Annotated
from pydantic import Field
from core.app import mcp
@mcp.prompt()
def weather_report(
weather_data: Annotated[
dict,
Field(description="Weather data as a dictionary"),
],
output_format: str = "narrative",
) -> str:
"""Generate weather report."""
return f"..."
```
### After (New Pattern)
```python
from pydantic import Field
from core.app import mcp
@mcp.prompt
def weather_report(
weather_data: dict[str, str] = Field(
description="Weather data as a dictionary with string keys and values"
),
output_format: str = Field(
default="narrative",
description="Desired output format: 'narrative', 'structured', or 'brief'"
),
) -> str:
"""Generate weather report."""
return f"..."
```
## Automated Refactoring
Consider using `ruff` or `black` for consistent formatting:
```bash
# Format updated prompts
ruff format src/prompts/
# Check for issues
ruff check src/prompts/
```
