fix(mcp): Fix update_task signature and MCP instructions
Resolves #420 - Tasks being duplicated instead of updated Changes: 1. Fixed update_task function signature to use individual optional parameters - Changed from TypedDict to explicit parameters (title, status, etc.) - Consistent with update_project and update_document patterns - Builds update_fields dict internally from provided parameters 2. Updated MCP instructions with correct function names - Replaced non-existent manage_task with actual functions - Added complete function signatures for all tools - Improved workflow documentation with concrete examples This fixes the issue where AI agents were confused by: - Wrong function names in instructions (manage_task vs update_task) - Inconsistent parameter patterns across update functions - TypedDict magic that wasn't clearly documented 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Rasmus Widing
Wirasm
parent
45750c3d31
commit
28eede38b5
@ -7,7 +7,7 @@ Mirrors the functionality of the original manage_task tool but with individual t
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import json
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import logging
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from typing import Any, Dict, List, Optional, TypedDict
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from typing import Any, Dict, List, Optional
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from urllib.parse import urljoin
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import httpx
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@ -20,19 +20,6 @@ from src.server.config.service_discovery import get_api_url
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logger = logging.getLogger(__name__)
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class TaskUpdateFields(TypedDict, total=False):
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"""Valid fields that can be updated on a task."""
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title: str
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description: str
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status: str # "todo" | "doing" | "review" | "done"
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assignee: str # "User" | "Archon" | "AI IDE Agent" | "prp-executor" | "prp-validator"
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task_order: int # 0-100, higher = more priority
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feature: Optional[str]
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sources: Optional[List[Dict[str, str]]]
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code_examples: Optional[List[Dict[str, str]]]
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def register_task_tools(mcp: FastMCP):
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"""Register individual task management tools with the MCP server."""
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@ -315,26 +302,66 @@ def register_task_tools(mcp: FastMCP):
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async def update_task(
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ctx: Context,
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task_id: str,
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update_fields: TaskUpdateFields,
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title: Optional[str] = None,
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description: Optional[str] = None,
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status: Optional[str] = None,
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assignee: Optional[str] = None,
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task_order: Optional[int] = None,
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feature: Optional[str] = None,
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sources: Optional[List[Dict[str, str]]] = None,
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code_examples: Optional[List[Dict[str, str]]] = None,
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) -> str:
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"""
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Update a task's properties.
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Args:
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task_id: UUID of the task to update
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update_fields: Dict of fields to update (e.g., {"status": "doing", "assignee": "AI IDE Agent"})
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title: New title (optional)
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description: New description (optional)
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status: New status - "todo" | "doing" | "review" | "done" (optional)
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assignee: New assignee (optional)
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task_order: New priority order (optional)
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feature: New feature label (optional)
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sources: New source references (optional)
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code_examples: New code examples (optional)
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Returns:
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JSON with updated task details
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Examples:
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update_task(task_id="uuid", update_fields={"status": "doing"})
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update_task(task_id="uuid", update_fields={"title": "New Title", "description": "Updated description"})
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update_task(task_id="uuid", status="doing")
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update_task(task_id="uuid", title="New Title", description="Updated description")
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"""
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try:
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api_url = get_api_url()
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timeout = get_default_timeout()
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# Build update_fields dict from provided parameters
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update_fields = {}
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if title is not None:
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update_fields["title"] = title
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if description is not None:
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update_fields["description"] = description
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if status is not None:
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update_fields["status"] = status
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if assignee is not None:
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update_fields["assignee"] = assignee
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if task_order is not None:
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update_fields["task_order"] = task_order
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if feature is not None:
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update_fields["feature"] = feature
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if sources is not None:
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update_fields["sources"] = sources
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if code_examples is not None:
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update_fields["code_examples"] = code_examples
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if not update_fields:
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return MCPErrorFormatter.format_error(
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error_type="validation_error",
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message="No fields to update",
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suggestion="Provide at least one field to update",
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)
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async with httpx.AsyncClient(timeout=timeout) as client:
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response = await client.put(
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urljoin(api_url, f"/api/tasks/{task_id}"), json=update_fields
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@ -192,57 +192,74 @@ MCP_INSTRUCTIONS = """
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# Archon MCP Server Instructions
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## 🚨 CRITICAL RULES (ALWAYS FOLLOW)
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1. **Task Management**: ALWAYS use Archon MCP tools for task management,
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You can combine them with your TODO tools but always make sure that the first todo is to update archon
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and the last todo is to update archon.
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Example: Use TodoWrite to create a set of new todos
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[]Create the task in archon
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[]Research deeply using archon rag
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[]Research on the web using websearch tools
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[]Deeply look into the codebase patterns and integration points
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[]Update Archon tasks with the findings
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[]Create implementation tasks in Archon
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This is to ensure efficient task management and collaboration.
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Making sure all critical details are in Archon.
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You can think of it as Archon is where you manage the task that needs to be shared with the team
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And your todo is your internal subtasks/todos that does not need to be shared with the team.
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1. **Task Management**: ALWAYS use Archon MCP tools for task management.
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- Combine with your local TODO tools for granular tracking
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- First TODO: Update Archon task status
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- Last TODO: Update Archon with findings/completion
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2. **Research First**: Before implementing, use perform_rag_query and search_code_examples
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3. **Task-Driven Development**: Never code without checking current tasks first
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## 📋 Core Workflow
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For every coding task, follow this cycle:
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1. Check current task: manage_task(action="get", task_id="...")
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2. Research: perform_rag_query() + search_code_examples()
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3. Update to doing: manage_task(action="update", update_fields={"status": "doing"})
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4. Implement based on research findings
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5. Mark for review: manage_task(action="update", update_fields={"status": "review"})
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6. Get next task: manage_task(action="list", filter_by="status", filter_value="todo")
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## 🏗️ Project Initialization
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- New project: manage_project(action="create", title="...", prd={...})
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- Existing project: manage_task(action="list", filter_by="project", filter_value="...")
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- Always create atomic tasks (1-4 hours of work each)
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### Task Management Cycle
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1. **Get current task**: `get_task(task_id="...")`
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2. **Mark as doing**: `update_task(task_id="...", status="doing")`
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3. **Research phase**:
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- `perform_rag_query(query="...", match_count=5)`
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- `search_code_examples(query="...", match_count=3)`
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4. **Implementation**: Code based on research findings
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5. **Mark for review**: `update_task(task_id="...", status="review")`
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6. **Get next task**: `list_tasks(filter_by="status", filter_value="todo")`
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### Available Task Functions
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- `create_task(project_id, title, description, assignee="User", ...)`
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- `list_tasks(filter_by="status", filter_value="todo", project_id=None)`
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- `get_task(task_id)`
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- `update_task(task_id, title=None, status=None, assignee=None, ...)`
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- `delete_task(task_id)`
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## 🏗️ Project Management
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### Project Functions
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- `create_project(title, description, github_repo=None)`
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- `list_projects()`
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- `get_project(project_id)`
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- `update_project(project_id, title=None, description=None, ...)`
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- `delete_project(project_id)`
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### Document Functions
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- `create_document(project_id, title, document_type, content=None, ...)`
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- `list_documents(project_id)`
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- `get_document(project_id, doc_id)`
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- `update_document(project_id, doc_id, title=None, content=None, ...)`
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- `delete_document(project_id, doc_id)`
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## 🔍 Research Patterns
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- Architecture: perform_rag_query(query="[tech] patterns", match_count=5)
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- Implementation: search_code_examples(query="[feature] example", match_count=3)
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- Keep match_count around (5) for focused results
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- Combine RAG with websearch tools for better results
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- **Architecture patterns**: `perform_rag_query(query="[tech] architecture patterns", match_count=5)`
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- **Code examples**: `search_code_examples(query="[feature] implementation", match_count=3)`
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- **Source discovery**: `get_available_sources()`
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- Keep match_count around 3-5 for focused results
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## 📊 Task Status Flow
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todo → doing → review → done
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- Only one task in 'doing' at a time
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`todo` → `doing` → `review` → `done`
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- Only ONE task in 'doing' status at a time
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- Use 'review' for completed work awaiting validation
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- Archive obsolete tasks
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- Mark tasks 'done' only after verification
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## 💾 Version Control
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- All documents auto-versioned on update
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- Use manage_versions to view history or restore
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- Deletions preserve version history
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## 💾 Version Management
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- `create_version(project_id, field_name, content, change_summary)`
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- `list_versions(project_id, field_name=None)`
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- `get_version(project_id, field_name, version_number)`
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- `restore_version(project_id, field_name, version_number)`
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- Field names: "docs", "features", "data", "prd"
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## 🎯 Best Practices
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1. **Atomic Tasks**: Create tasks that take 1-4 hours
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2. **Clear Descriptions**: Include acceptance criteria in task descriptions
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3. **Use Features**: Group related tasks with feature labels
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4. **Add Sources**: Link relevant documentation to tasks
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5. **Track Progress**: Update task status as you work
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"""
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# Initialize the main FastMCP server with fixed configuration
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