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get_workspace_features

Fetches feature flags for the current workspace, enabling you to check which features are active.

Instructions

Get features of the current workspace.

Returns: WorkspaceFeature object containing feature flags

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_groupingYes
initiativesYes
teamsYes
customersYes
wikiYes
piYes

Implementation Reference

  • The actual handler function for the 'get_workspace_features' tool. It retrieves workspace features by calling client.workspaces.get_features() using the Plane client and workspace slug obtained from the context.
    @mcp.tool()
    def get_workspace_features() -> WorkspaceFeature:
        """
        Get features of the current workspace.
    
        Returns:
            WorkspaceFeature object containing feature flags
        """
        client, workspace_slug = get_plane_client_context()
        return client.workspaces.get_features(workspace_slug=workspace_slug)
  • The 'register_workspace_tools' function registers all workspace tools (including 'get_workspace_features') with the MCP server via the @mcp.tool() decorator.
    def register_workspace_tools(mcp: FastMCP) -> None:
        """Register all workspace-related tools with the MCP server."""
    
        @mcp.tool()
        def get_workspace_members() -> list[UserLite]:
            """
            Get all members of the current workspace.
    
            Returns:
                List of UserLite objects representing workspace members
            """
            client, workspace_slug = get_plane_client_context()
            return client.workspaces.get_members(workspace_slug=workspace_slug)
    
        @mcp.tool()
        def get_workspace_features() -> WorkspaceFeature:
            """
            Get features of the current workspace.
    
            Returns:
                WorkspaceFeature object containing feature flags
            """
            client, workspace_slug = get_plane_client_context()
            return client.workspaces.get_features(workspace_slug=workspace_slug)
    
        @mcp.tool()
        def update_workspace_features(
            project_grouping: bool | None = None,
            initiatives: bool | None = None,
            teams: bool | None = None,
            customers: bool | None = None,
            wiki: bool | None = None,
            pi: bool | None = None,
        ) -> WorkspaceFeature:
            """
            Update features of the current workspace.
    
            Args:
                project_grouping: Enable/disable project grouping feature
                initiatives: Enable/disable initiatives feature
                teams: Enable/disable teams feature
                customers: Enable/disable customers feature
                wiki: Enable/disable wiki feature
                pi: Enable/disable PI (Program Increment) feature
    
            Returns:
                Updated WorkspaceFeature object
            """
            client, workspace_slug = get_plane_client_context()
    
            # Build data dict with only non-None values
            feature_data: dict[str, bool] = {}
            if project_grouping is not None:
                feature_data["project_grouping"] = project_grouping
            if initiatives is not None:
                feature_data["initiatives"] = initiatives
            if teams is not None:
                feature_data["teams"] = teams
            if customers is not None:
                feature_data["customers"] = customers
            if wiki is not None:
                feature_data["wiki"] = wiki
            if pi is not None:
                feature_data["pi"] = pi
    
            data = WorkspaceFeature(**feature_data)
    
            return client.workspaces.update_features(workspace_slug=workspace_slug, data=data)
  • Imports the WorkspaceFeature type from plane.models.workspaces, which serves as the return type schema for get_workspace_features.
    from plane.models.workspaces import WorkspaceFeature
  • Imports register_workspace_tools from plane_mcp.tools.workspaces.
    from plane_mcp.tools.workspaces import register_workspace_tools
  • Calls register_workspace_tools(mcp) to register all workspace tools including get_workspace_features.
    register_workspace_tools(mcp)
Behavior2/5

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

With no annotations, the description carries full burden. It states the return type but does not disclose behavioral traits like idempotency, authentication requirements, rate limits, or error conditions. This is insufficient for a tool with no safety hints.

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 very short and to the point, with no wasted words. It is front-loaded with the purpose. However, it could be slightly more structured (e.g., separate sections) but is adequate for a zero-parameter tool.

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 the presence of an output schema, the description's mention of 'WorkspaceFeature object containing feature flags' provides useful context. However, it lacks any behavioral or usage context that would help an agent fully understand the tool's role, especially since no annotations exist.

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 no parameters and 100% coverage, so the baseline is 3. The description adds nothing beyond the schema, which is acceptable here because no parameters exist to describe.

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 'Get features of the current workspace' and mentions the return type, which matches the tool name. However, it does not explicitly differentiate from sibling tools like 'get_project_features' beyond the resource scope, though the resource difference is implicit.

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 versus alternatives, such as when to check feature flags before operations or that it is a read-only call. There are no exclusions or context cues for appropriate invocation.

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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