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remove_work_item_from_module

Remove a work item from a module by providing project, module, and work item IDs.

Instructions

Remove a work item from a module.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesUUID of the project
module_idYesUUID of the module
work_item_idYesUUID of the work item to remove

Implementation Reference

  • The handler function that executes the 'remove_work_item_from_module' tool logic. It calls client.modules.remove_work_item with the workspace slug, project ID, module ID, and work item ID.
    @mcp.tool()
    def remove_work_item_from_module(
        project_id: str,
        module_id: str,
        work_item_id: str,
    ) -> None:
        """
        Remove a work item from a module.
    
        Args:
            workspace_slug: The workspace slug identifier
            project_id: UUID of the project
            module_id: UUID of the module
            work_item_id: UUID of the work item to remove
        """
        client, workspace_slug = get_plane_client_context()
        client.modules.remove_work_item(
            workspace_slug=workspace_slug,
            project_id=project_id,
            module_id=module_id,
            work_item_id=work_item_id,
        )
  • The @mcp.tool() decorator registers this function as an MCP tool. The tool is registered inside register_module_tools(), which is called from register_tools() in plane_mcp/tools/__init__.py.
    @mcp.tool()
    def remove_work_item_from_module(
        project_id: str,
        module_id: str,
        work_item_id: str,
    ) -> None:
        """
        Remove a work item from a module.
    
        Args:
            workspace_slug: The workspace slug identifier
            project_id: UUID of the project
            module_id: UUID of the module
            work_item_id: UUID of the work item to remove
        """
        client, workspace_slug = get_plane_client_context()
        client.modules.remove_work_item(
            workspace_slug=workspace_slug,
            project_id=project_id,
            module_id=module_id,
            work_item_id=work_item_id,
        )
  • The get_plane_client_context() helper function used by the handler to obtain the PlaneClient instance and workspace slug.
    def get_plane_client_context() -> PlaneClientContext:
        """
        Initialize and return a PlaneClient instance with workspace context.
    
        Authentication is handled by the PlaneOAuthProvider, which supports:
        1. Environment variables (PLANE_API_KEY + PLANE_WORKSPACE_SLUG)
        2. HTTP headers (x-api-key + x-workspace-slug)
        3. OAuth access token
    
        Environment variables:
        - PLANE_INTERNAL_BASE_URL: Internal URL for Plane API (preferred for server-to-server calls)
        - PLANE_BASE_URL: Base URL for Plane API (fallback, default: https://api.plane.so)
    
        Returns:
            PlaneClientContext containing configured PlaneClient instance and workspace slug
    
        Raises:
            ConfigurationError: If access token is not available or workspace slug is missing
        """
        base_url = os.getenv("PLANE_INTERNAL_BASE_URL") or os.getenv("PLANE_BASE_URL", "https://api.plane.so")
        workspace_slug = os.getenv("PLANE_WORKSPACE_SLUG", "")
    
        api_key = os.getenv("PLANE_API_KEY", "")
        access_token = None
    
        # Get access token from the OAuth provider (which handles all auth methods)
        stored_access_token: AccessToken | None = get_access_token()
        if stored_access_token:
            # Determine authentication method to use appropriate PlaneClient constructor
            auth_method = stored_access_token.claims.get("auth_method", "oauth")
            token = stored_access_token.token
            workspace_slug = stored_access_token.claims.get("workspace_slug", "")
    
            # For API key auth methods, use api_key parameter; for OAuth, use access_token
            if auth_method in ("api_key_env", "api_key_header"):
                api_key = token
            else:
                access_token = token
    
        if access_token:
            client = PlaneClient(
                base_url=base_url,
                access_token=access_token,
            )
        else:
            client = PlaneClient(
                base_url=base_url,
                api_key=api_key,
            )
    
        return PlaneClientContext(
            client=client,
            workspace_slug=workspace_slug,
        )
  • The function signature defines the input schema: project_id (str), module_id (str), and work_item_id (str) parameters.
    @mcp.tool()
    def remove_work_item_from_module(
        project_id: str,
        module_id: str,
        work_item_id: str,
    ) -> None:
        """
        Remove a work item from a module.
    
        Args:
            workspace_slug: The workspace slug identifier
            project_id: UUID of the project
            module_id: UUID of the module
            work_item_id: UUID of the work item to remove
        """
        client, workspace_slug = get_plane_client_context()
        client.modules.remove_work_item(
            workspace_slug=workspace_slug,
            project_id=project_id,
            module_id=module_id,
            work_item_id=work_item_id,
        )
Behavior2/5

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

With no annotations, the description must disclose behavior. It states a destructive action ('remove') but does not explain consequences (e.g., whether the work item is disassociated or deleted, if the operation is reversible, or any side effects on the module or work item).

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 a single, efficient sentence with no unnecessary words. It is front-loaded and immediately clear.

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

Completeness2/5

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

Despite a simple tool with three parameters, the description lacks essential context. It does not clarify the meaning of 'remove' (e.g., unassigning vs. deleting), what happens to associated data, or the return value (no output schema). This is insufficient for an agent to fully understand the tool's behavior.

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

Parameters3/5

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

The input schema has 100% coverage, with each parameter described as 'UUID of...'. The description adds no further meaning, so a baseline of 3 is appropriate.

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

Purpose5/5

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

The description clearly states the action ('Remove') and the resource ('a work item from a module'), directly matching the tool name. It distinguishes from siblings like 'remove_work_item_from_cycle' or 'add_work_items_to_module'.

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?

No guidance is provided on when to use this tool or when to avoid it. There are sibling tools for removing work items from cycles or milestones, but the description does not differentiate them or mention any prerequisites.

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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