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list_archived_cycles

Retrieve all archived cycles for a specific project to review past planning periods and historical data.

Instructions

List archived cycles in a project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesUUID of the project
paramsNoOptional query parameters as a dictionary

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the list_archived_cycles tool. It uses the @mcp.tool() decorator (via FastMCP) and calls client.cycles.list_archived() to retrieve archived cycles from the Plane API, returning the results list.
    @mcp.tool()
    def list_archived_cycles(
        project_id: str,
        params: dict[str, Any] | None = None,
    ) -> list[Cycle]:
        """
        List archived cycles in a project.
    
        Args:
            workspace_slug: The workspace slug identifier
            project_id: UUID of the project
            params: Optional query parameters as a dictionary
    
        Returns:
            List of archived Cycle objects
        """
        client, workspace_slug = get_plane_client_context()
        response: PaginatedArchivedCycleResponse = client.cycles.list_archived(
            workspace_slug=workspace_slug, project_id=project_id, params=params
        )
        return response.results
  • Import of the PaginatedArchivedCycleResponse schema model used by the list_archived_cycles handler as the return type from the API call.
    from plane.models.cycles import (
        CreateCycle,
        Cycle,
        PaginatedArchivedCycleResponse,
        PaginatedCycleResponse,
        PaginatedCycleWorkItemResponse,
        TransferCycleWorkItemsRequest,
        UpdateCycle,
    )
  • The register_tools function calls register_cycle_tools(mcp) which in turn registers list_archived_cycles (along with all other cycle tools) with the MCP server.
    def register_tools(mcp: FastMCP) -> None:
        """Register all tools with the MCP server."""
        register_project_tools(mcp)
        register_work_item_tools(mcp)
        register_work_item_activity_tools(mcp)
        register_work_item_comment_tools(mcp)
        register_work_item_link_tools(mcp)
        register_work_item_relation_tools(mcp)
        register_work_log_tools(mcp)
        register_cycle_tools(mcp)
  • The register_cycle_tools function is where the @mcp.tool() decorator registers list_archived_cycles as an MCP tool.
    def register_cycle_tools(mcp: FastMCP) -> None:
        """Register all cycle-related tools with the MCP server."""
  • The get_plane_client_context helper function that provides the PlaneClient and workspace_slug used by list_archived_cycles.
    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,
        )
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, pagination, ordering, or any side effects. This is a significant gap for a list operation.

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?

The description is a single sentence and front-loaded with the action and resource. It is efficient but could be more helpful by adding a bit more context without becoming verbose.

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 an output schema exists, the return format is covered elsewhere. However, the description is too minimal; it does not explain what 'archived' means or provide usage context, making it barely adequate for a list operation.

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?

Schema description coverage is 100%, so the schema already documents the parameters. The description does not add meaning beyond the schema; for example, it does not explain what query parameters can be passed in the 'params' dictionary.

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 ('List'), the resource ('archived cycles'), and the scope ('in a project'). It distinguishes this tool from siblings like 'list_cycles' (all cycles) and 'archive_cycle' (archiving action).

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 on when to use this tool versus alternatives. It does not mention that it only returns archived cycles, nor does it contrast with 'list_cycles' or provide context for unarchiving cycles.

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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