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create_project

Create a new project in Todoist to organize tasks, with options for nesting, color coding, and favorites.

Instructions

Create a new project in Todoist.

Args:
    name: The name of the project
    parent_id: Parent project ID (for nested projects)
    color: Project color
    is_favorite: Whether to mark as favorite

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
parent_idNo
colorNo
is_favoriteNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'create_project': registers the tool, defines input schema via arguments, checks client, prepares kwargs, calls TodoistClient.create_project, and returns formatted success message with project details.
    @mcp.tool()
    async def create_project(
        name: str,
        parent_id: Optional[str] = None,
        color: Optional[str] = None,
        is_favorite: bool = False
    ) -> str:
        """Create a new project in Todoist.
        
        Args:
            name: The name of the project
            parent_id: Parent project ID (for nested projects)
            color: Project color
            is_favorite: Whether to mark as favorite
        """
        _check_client()
        
        kwargs = {"name": name}
        if parent_id:
            kwargs["parent_id"] = parent_id
        if color:
            kwargs["color"] = color
        if is_favorite:
            kwargs["is_favorite"] = is_favorite
            
        project = await todoist_client.create_project(**kwargs)
        
        return (
            f"Project created successfully!\n"
            f"ID: {project.id}\n"
            f"Name: {project.name}\n"
            f"URL: {project.url}"
        )
  • Core implementation in TodoistClient: constructs payload from name and kwargs, makes POST request to Todoist API /projects endpoint, parses response into TodoistProject model.
    async def create_project(self, name: str, **kwargs) -> TodoistProject:
        """Create a new project."""
        payload = {"name": name, **kwargs}
        data = await self._request("POST", "/projects", json=payload)
        return TodoistProject(**data)
  • Pydantic model for TodoistProject used to validate and type the project data returned from API.
    class TodoistProject(BaseModel):
        """Represents a Todoist project."""
        id: str
        name: str
        comment_count: int = 0
        order: int = 0
        color: str = "grey"
        is_shared: bool = False
        is_favorite: bool = False
        is_inbox_project: bool = False
        is_team_inbox: bool = False
        view_style: str = "list"
        url: str = ""
        parent_id: Optional[str] = None
  • Registration decorator @mcp.tool() that registers the create_project function as an MCP tool.
    @mcp.tool()
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a creation operation but doesn't mention permissions required, whether projects are permanent or deletable, rate limits, or what the output looks like. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

Conciseness5/5

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

The description is efficiently structured with a clear purpose statement followed by a bulleted list of parameters. Every sentence earns its place, and there's no redundant or verbose language. It's appropriately sized for a tool with 4 parameters.

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

Completeness3/5

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

Given that there's an output schema (which handles return values), 0% schema description coverage, and no annotations, the description does an adequate job explaining parameters but falls short on behavioral context. For a creation tool in a Todoist system with sibling tools, it should provide more guidance on usage and implications.

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

Parameters4/5

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

The description adds meaningful context for all 4 parameters beyond the schema, which has 0% description coverage. It explains that 'parent_id' is for nested projects and 'is_favorite' marks projects as favorites, providing semantic value that the schema titles alone don't convey. However, it doesn't specify format details like valid color values or parent_id constraints.

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?

The description clearly states the action ('Create a new project') and resource ('in Todoist'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'create_task' or explain why one would create a project versus a task, which prevents a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'create_task' or 'update_task', nor does it mention prerequisites or context for project creation. It simply lists parameters without explaining the tool's role in the broader workflow.

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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