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docs: improve MCP server resource documentation

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- Update subtask 23.10 with details on resource and resource template implementation
- Add resource management section to architecture.mdc with proper directory structure
- Create comprehensive resource implementation guide in mcp.mdc with examples and best practices
- Document proper integration of resources in FastMCP server initialization
This commit is contained in:
Eyal Toledano
2025-03-31 14:38:57 -04:00
parent 17b6ebef6b
commit 5f3d08ef79
4 changed files with 234 additions and 1 deletions
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70
tasks/task_023.txt
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@@ -331,6 +331,76 @@ function listTasks(tasksPath, statusFilter, withSubtasks = false, outputFormat =
7. Add validation for tool inputs using FastMCP's built-in validation
8. Create comprehensive tests for tool registration and resource access
<info added on 2025-03-31T18:35:21.513Z>
Here is additional information to enhance the subtask regarding resources and resource templates in FastMCP:
Resources in FastMCP are used to expose static or dynamic data to LLM clients. For the Task Master MCP server, we should implement resources to provide:
1. Task templates: Predefined task structures that can be used as starting points
2. Workflow definitions: Reusable workflow patterns for common task sequences
3. User preferences: Stored user settings for task management
4. Project metadata: Information about active projects and their attributes
Resource implementation should follow this structure:
```python
@mcp.resource("tasks://templates/{template_id}")
def get_task_template(template_id: str) -> dict:
# Fetch and return the specified task template
...
@mcp.resource("workflows://definitions/{workflow_id}")
def get_workflow_definition(workflow_id: str) -> dict:
# Fetch and return the specified workflow definition
...
@mcp.resource("users://{user_id}/preferences")
def get_user_preferences(user_id: str) -> dict:
# Fetch and return user preferences
...
@mcp.resource("projects://metadata")
def get_project_metadata() -> List[dict]:
# Fetch and return metadata for all active projects
...
```
Resource templates in FastMCP allow for dynamic generation of resources based on patterns. For Task Master, we can implement:
1. Dynamic task creation templates
2. Customizable workflow templates
3. User-specific resource views
Example implementation:
```python
@mcp.resource("tasks://create/{task_type}")
def get_task_creation_template(task_type: str) -> dict:
# Generate and return a task creation template based on task_type
...
@mcp.resource("workflows://custom/{user_id}/{workflow_name}")
def get_custom_workflow_template(user_id: str, workflow_name: str) -> dict:
# Generate and return a custom workflow template for the user
...
@mcp.resource("users://{user_id}/dashboard")
def get_user_dashboard(user_id: str) -> dict:
# Generate and return a personalized dashboard view for the user
...
```
Best practices for integrating resources with Task Master functionality:
1. Use resources to provide context and data for tools
2. Implement caching for frequently accessed resources
3. Ensure proper error handling and not-found cases for all resources
4. Use resource templates to generate dynamic, personalized views of data
5. Implement access control to ensure users only access authorized resources
By properly implementing these resources and resource templates, we can provide rich, contextual data to LLM clients, enhancing the Task Master's capabilities and user experience.
</info added on 2025-03-31T18:35:21.513Z>
## 11. Implement Comprehensive Error Handling [deferred]
### Dependencies: 23.1, 23.3
### Description: Implement robust error handling using FastMCP's MCPError, including custom error types for different categories and standardized error responses.

2
tasks/tasks.json
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@@ -1422,7 +1422,7 @@
1,
"23.8"
],
"details": "1. Update registerTaskMasterTools function to use FastMCP's decorator pattern\n2. Implement @mcp.tool() decorators for all existing tools\n3. Add proper type annotations and documentation for all tools\n4. Create resource handlers for task templates using @mcp.resource()\n5. Implement resource templates for common task patterns\n6. Update the server initialization to properly register all tools and resources\n7. Add validation for tool inputs using FastMCP's built-in validation\n8. Create comprehensive tests for tool registration and resource access",
"details": "1. Update registerTaskMasterTools function to use FastMCP's decorator pattern\n2. Implement @mcp.tool() decorators for all existing tools\n3. Add proper type annotations and documentation for all tools\n4. Create resource handlers for task templates using @mcp.resource()\n5. Implement resource templates for common task patterns\n6. Update the server initialization to properly register all tools and resources\n7. Add validation for tool inputs using FastMCP's built-in validation\n8. Create comprehensive tests for tool registration and resource access\n\n<info added on 2025-03-31T18:35:21.513Z>\nHere is additional information to enhance the subtask regarding resources and resource templates in FastMCP:\n\nResources in FastMCP are used to expose static or dynamic data to LLM clients. For the Task Master MCP server, we should implement resources to provide:\n\n1. Task templates: Predefined task structures that can be used as starting points\n2. Workflow definitions: Reusable workflow patterns for common task sequences\n3. User preferences: Stored user settings for task management\n4. Project metadata: Information about active projects and their attributes\n\nResource implementation should follow this structure:\n\n```python\n@mcp.resource(\"tasks://templates/{template_id}\")\ndef get_task_template(template_id: str) -> dict:\n # Fetch and return the specified task template\n ...\n\n@mcp.resource(\"workflows://definitions/{workflow_id}\")\ndef get_workflow_definition(workflow_id: str) -> dict:\n # Fetch and return the specified workflow definition\n ...\n\n@mcp.resource(\"users://{user_id}/preferences\")\ndef get_user_preferences(user_id: str) -> dict:\n # Fetch and return user preferences\n ...\n\n@mcp.resource(\"projects://metadata\")\ndef get_project_metadata() -> List[dict]:\n # Fetch and return metadata for all active projects\n ...\n```\n\nResource templates in FastMCP allow for dynamic generation of resources based on patterns. For Task Master, we can implement:\n\n1. Dynamic task creation templates\n2. Customizable workflow templates\n3. User-specific resource views\n\nExample implementation:\n\n```python\n@mcp.resource(\"tasks://create/{task_type}\")\ndef get_task_creation_template(task_type: str) -> dict:\n # Generate and return a task creation template based on task_type\n ...\n\n@mcp.resource(\"workflows://custom/{user_id}/{workflow_name}\")\ndef get_custom_workflow_template(user_id: str, workflow_name: str) -> dict:\n # Generate and return a custom workflow template for the user\n ...\n\n@mcp.resource(\"users://{user_id}/dashboard\")\ndef get_user_dashboard(user_id: str) -> dict:\n # Generate and return a personalized dashboard view for the user\n ...\n```\n\nBest practices for integrating resources with Task Master functionality:\n\n1. Use resources to provide context and data for tools\n2. Implement caching for frequently accessed resources\n3. Ensure proper error handling and not-found cases for all resources\n4. Use resource templates to generate dynamic, personalized views of data\n5. Implement access control to ensure users only access authorized resources\n\nBy properly implementing these resources and resource templates, we can provide rich, contextual data to LLM clients, enhancing the Task Master's capabilities and user experience.\n</info added on 2025-03-31T18:35:21.513Z>",
"status": "deferred",
"parentTaskId": 23
},