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memory_create_todo

Create and manage TODO tasks with priority, status, and category tracking for organized task management within the Memora memory system.

Instructions

Create a new TODO/task memory.

Args: content: Description of the task status: Task status - "open" (default) or "closed" closed_reason: If closed, the reason - "complete" or "not_planned" priority: Task priority - "high", "medium" (default), "low" category: Task category (e.g., "cloud-backend", "graph-visualization", "docs")

Returns: Created TODO memory with auto-assigned tag "memora/todos"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
statusNoopen
closed_reasonNo
priorityNomedium
categoryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Implementation of the MCP tool 'memory_create_todo', which validates input fields (status, closed_reason, priority), builds the metadata, and saves the task with the tag 'memora/todos'.
    async def memory_create_todo(
        content: str,
        status: str = "open",
        closed_reason: Optional[str] = None,
        priority: str = "medium",
        category: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Create a new TODO/task memory.
    
        Args:
            content: Description of the task
            status: Task status - "open" (default) or "closed"
            closed_reason: If closed, the reason - "complete" or "not_planned"
            priority: Task priority - "high", "medium" (default), "low"
            category: Task category (e.g., "cloud-backend", "graph-visualization", "docs")
    
        Returns:
            Created TODO memory with auto-assigned tag "memora/todos"
        """
        # Validate status
        valid_statuses = {"open", "closed"}
        if status not in valid_statuses:
            return {"error": "invalid_status", "message": f"Status must be one of: {', '.join(valid_statuses)}"}
    
        # Validate closed_reason if status is closed
        if status == "closed":
            valid_reasons = {"complete", "not_planned"}
            if not closed_reason:
                return {"error": "missing_closed_reason", "message": "closed_reason required when status is 'closed'"}
            if closed_reason not in valid_reasons:
                return {"error": "invalid_closed_reason", "message": f"closed_reason must be one of: {', '.join(valid_reasons)}"}
    
        # Validate priority
        valid_priorities = {"high", "medium", "low"}
        if priority not in valid_priorities:
            return {"error": "invalid_priority", "message": f"Priority must be one of: {', '.join(valid_priorities)}"}
    
        # Build metadata
        metadata: Dict[str, Any] = {
            "type": "todo",
            "status": status,
            "priority": priority,
        }
        if closed_reason:
            metadata["closed_reason"] = closed_reason
        if category:
            metadata["category"] = category
    
        # Create with auto-tag
        tags = ["memora/todos"]
    
        try:
            record = _create_memory(content.strip(), metadata, tags)
        except ValueError as exc:
            return {"error": "invalid_input", "message": str(exc)}
    
        _schedule_cloud_graph_sync()
        return {"memory": record}
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description carries burden by disclosing auto-assigned tag 'memora/todos' and return type, but omits mutation safety, idempotency, or error handling details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Uses structured Args/Returns format that is front-loaded and scannable. Slightly verbose docstring style but every line provides necessary parameter documentation given the empty schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive given 5 parameters with no schema descriptions. Covers input semantics fully and notes key output behavior (auto-tagging). Lacks only sibling differentiation to be complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by documenting all 5 parameters with semantics, valid values (open/closed, complete/not_planned), defaults, and category examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific action (Create) and resource type (TODO/task memory). The 'TODO/task' qualifier distinguishes it from generic memory_create, though it doesn't explicitly contrast with siblings like memory_create_issue.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides implied usage through Args documentation showing task-specific fields (status, priority, closed_reason), but lacks explicit guidance on when to select this over memory_create or memory_create_issue.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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