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jamesbrink

MCP Server for Coroot

update_dashboard

Modify dashboard configurations in Coroot to adjust panels, layout, or settings for monitoring applications and infrastructure.

Instructions

Update an existing dashboard configuration.

Updates dashboard panels, layout, or other settings.

Args: project_id: Project ID dashboard_id: Dashboard ID dashboard: Updated dashboard configuration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
dashboard_idYes
dashboardYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Primary MCP tool handler for 'update_dashboard'. Registered via @mcp.tool() decorator. Delegates to internal impl.
    @mcp.tool()
    async def update_dashboard(
        project_id: str, dashboard_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Update an existing dashboard configuration.
    
        Updates dashboard panels, layout, or other settings.
    
        Args:
            project_id: Project ID
            dashboard_id: Dashboard ID
            dashboard: Updated dashboard configuration
        """
        return await update_dashboard_impl(  # type: ignore[no-any-return]
            project_id, dashboard_id, dashboard
        )
  • Internal error-handling wrapper that calls CorootClient.update_dashboard and formats the response.
    @handle_errors
    async def update_dashboard_impl(
        project_id: str, dashboard_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Update a dashboard."""
        result = await get_client().update_dashboard(project_id, dashboard_id, dashboard)
        return {
            "success": True,
            "message": "Dashboard updated successfully",
            "dashboard": result,
        }
  • CorootClient method that executes the HTTP POST request to the Coroot API to update the dashboard configuration.
    async def update_dashboard(
        self, project_id: str, dashboard_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Update an existing dashboard.
    
        Args:
            project_id: Project ID.
            dashboard_id: Dashboard ID.
            dashboard: Updated dashboard configuration.
    
        Returns:
            Updated dashboard.
        """
        response = await self._request(
            "POST",
            f"/api/project/{project_id}/dashboards/{dashboard_id}",
            json=dashboard,
        )
        data: dict[str, Any] = response.json()
        return data
  • FastMCP tool registration decorator for the update_dashboard 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 but only states it's an update operation. It doesn't disclose behavioral traits like required permissions, whether updates are partial or full replacements, validation rules, error conditions, or rate limits. The description adds minimal context beyond the basic 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 appropriately sized with three sentences and a parameter list. It's front-loaded with the core purpose, followed by elaboration and parameter details. No wasted sentences, though the second sentence slightly repeats the first.

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 3 parameters with 0% schema coverage, nested objects, and an output schema present, the description provides basic parameter semantics but lacks behavioral context. The output schema existence reduces need to explain returns, but for a mutation tool with no annotations, more disclosure on permissions, validation, or side effects would improve completeness.

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 0%, but the description's 'Args' section documents all three parameters with brief explanations. It adds meaning by clarifying 'project_id' and 'dashboard_id' identify the target, and 'dashboard' contains the updated configuration. However, it doesn't explain the structure of the 'dashboard' object or provide format 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?

The description clearly states the tool's purpose as 'Update an existing dashboard configuration' with specific resources (dashboard panels, layout, settings). It distinguishes from sibling 'create_dashboard' by specifying 'existing' but doesn't differentiate from other update tools like 'update_project_settings' or 'update_ai_config'.

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 explicit guidance on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an existing dashboard), when-not-to-use scenarios, or comparisons with related tools like 'get_dashboard' for viewing or 'delete_dashboard' for removal.

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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