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jamesbrink

MCP Server for Coroot

create_dashboard

Build custom monitoring dashboards with tailored panels and queries to track specific infrastructure metrics in your Coroot project.

Instructions

Create a new custom dashboard.

Creates a dashboard with custom panels and queries for monitoring specific aspects of your infrastructure.

Args: project_id: Project ID dashboard: Dashboard configuration with panels and layout

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
dashboardYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool registration for 'create_dashboard'. The @mcp.tool() decorator registers this function as the MCP tool with the given name, input schema from type hints and docstring, and delegates execution to the impl.
    @mcp.tool()
    async def create_dashboard(
        project_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Create a new custom dashboard.
    
        Creates a dashboard with custom panels and queries for monitoring
        specific aspects of your infrastructure.
    
        Args:
            project_id: Project ID
            dashboard: Dashboard configuration with panels and layout
        """
        return await create_dashboard_impl(project_id, dashboard)  # type: ignore[no-any-return]
  • Implementation wrapper with error handling that invokes the CorootClient.create_dashboard method and formats the success response.
    @handle_errors
    async def create_dashboard_impl(
        project_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Create a new dashboard."""
        result = await get_client().create_dashboard(project_id, dashboard)
        return {
            "success": True,
            "message": "Dashboard created successfully",
            "dashboard": result,
        }
  • Core handler implementation in CorootClient class. Makes authenticated POST request to Coroot API endpoint /api/project/{project_id}/dashboards to create the dashboard and parses the JSON response.
    async def create_dashboard(
        self, project_id: str, dashboard: dict[str, Any]
    ) -> dict[str, Any]:
        """Create a new custom dashboard.
    
        Args:
            project_id: Project ID.
            dashboard: Dashboard configuration.
    
        Returns:
            Created dashboard.
        """
        response = await self._request(
            "POST",
            f"/api/project/{project_id}/dashboards",
            json=dashboard,
        )
        return self._parse_json_response(response)
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. While it mentions creating a dashboard, it doesn't address permissions needed, whether this is a destructive operation, rate limits, error conditions, or what happens on success/failure. The description provides basic functionality but lacks critical behavioral context for a mutation tool.

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: a clear purpose statement, elaboration on dashboard features, and parameter explanations. It's front-loaded with the main purpose and avoids unnecessary verbosity while maintaining clarity.

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 this is a mutation tool with no annotations, 0% schema description coverage, but with an output schema present, the description provides basic functionality but lacks important context. The output schema reduces the need to explain return values, but the description should still address behavioral aspects like permissions, side effects, and error handling for a creation 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 0%, so the schema provides no parameter documentation. The description adds some value by explaining 'project_id' and 'dashboard' parameters, but doesn't detail the dashboard configuration structure, panel requirements, or valid values. It compensates partially but not fully for the schema coverage gap.

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 creates a new custom dashboard with custom panels and queries for monitoring infrastructure, which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'update_dashboard' or 'get_dashboard' beyond the basic create vs update/get distinction.

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 'update_dashboard' or 'get_dashboard', nor does it mention prerequisites or context for dashboard creation. It simply states what the tool does without usage context.

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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