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jamesbrink

MCP Server for Coroot

get_panel_data

Retrieve metrics and time series data for a specific dashboard panel in Coroot to analyze application performance and infrastructure monitoring.

Instructions

Get data for a specific dashboard panel.

Retrieves the data that powers a specific panel in a custom dashboard, including metrics, time series data, or aggregated values.

Args: project_id: The project ID dashboard_id: The dashboard ID panel_id: The panel ID within the dashboard from_time: Optional start time (ISO format or relative like '-1h') to_time: Optional end time (ISO format or 'now')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
dashboard_idYes
panel_idYes
from_timeNo
to_timeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler function for 'get_panel_data'. This is the entry point registered with FastMCP that handles tool calls and delegates to the implementation.
    @mcp.tool()
    async def get_panel_data(
        project_id: str,
        dashboard_id: str,
        panel_id: str,
        from_time: str | None = None,
        to_time: str | None = None,
    ) -> dict[str, Any]:
        """
        Get data for a specific dashboard panel.
    
        Retrieves the data that powers a specific panel in a custom dashboard,
        including metrics, time series data, or aggregated values.
    
        Args:
            project_id: The project ID
            dashboard_id: The dashboard ID
            panel_id: The panel ID within the dashboard
            from_time: Optional start time (ISO format or relative like '-1h')
            to_time: Optional end time (ISO format or 'now')
        """
        return await get_panel_data_impl(
            project_id, dashboard_id, panel_id, from_time, to_time
        )
  • Core handler in CorootClient that makes the actual HTTP request to the Coroot API endpoint /api/project/{project_id}/panel/data to fetch the panel data.
    async def get_panel_data(
        self,
        project_id: str,
        dashboard_id: str,
        panel_id: str,
        params: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        """Get data for a specific dashboard panel.
    
        Args:
            project_id: The project ID
            dashboard_id: The dashboard ID
            panel_id: The panel ID
            params: Optional query parameters (time range, etc.)
    
        Returns:
            Dict containing panel data
        """
        query_params = params or {}
        query_params.update({"dashboard": dashboard_id, "panel": panel_id})
        response = await self._request(
            "GET", f"/api/project/{project_id}/panel/data", params=query_params
        )
        data: dict[str, Any] = response.json()
        return data
  • Helper implementation function in the MCP server that wraps the client call with error handling specific to the tool.
    async def get_panel_data_impl(
        project_id: str,
        dashboard_id: str,
        panel_id: str,
        from_time: str | None = None,
        to_time: str | None = None,
    ) -> dict[str, Any]:
        """Implementation for get_panel_data tool."""
        try:
            client = get_client()
            params = {}
            if from_time:
                params["from"] = from_time
            if to_time:
                params["to"] = to_time
            data = await client.get_panel_data(project_id, dashboard_id, panel_id, params)
            return {"success": True, "data": data}
        except ValueError as e:
            return {"success": False, "error": str(e)}
        except Exception as e:
            return {"success": False, "error": f"Unexpected error: {str(e)}"}
  • FastMCP tool registration decorator that registers the get_panel_data handler with name 'get_panel_data'.
    @mcp.tool()
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data (implying a read-only operation) but doesn't cover other important aspects like authentication requirements, rate limits, error conditions, pagination, or data format specifics. The description is minimal 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 and front-loaded: the first sentence states the core purpose, followed by elaboration and a structured parameter list. There's minimal waste, though the separation between the descriptive text and 'Args' could be slightly clearer.

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 tool's moderate complexity (5 parameters, no annotations, but with an output schema), the description is adequate but has gaps. It covers the purpose and parameters well, but lacks behavioral details (e.g., permissions, errors) and doesn't leverage the output schema to explain return values. It's minimally viable for a read operation.

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

Parameters4/5

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

The description includes an 'Args' section that lists all 5 parameters with brief explanations, adding meaningful context beyond the schema (which has 0% description coverage). It clarifies that 'from_time' and 'to_time' are optional and provides format examples (ISO or relative like '-1h'), compensating well for the schema's lack of descriptions.

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: 'Get data for a specific dashboard panel' and elaborates with 'Retrieves the data that powers a specific panel in a custom dashboard, including metrics, time series data, or aggregated values.' This specifies the verb ('get'/'retrieve') and resource ('dashboard panel data'), though it doesn't explicitly differentiate from sibling tools like 'get_dashboard' or 'get_application_profiling'.

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. It doesn't mention prerequisites (e.g., needing an existing dashboard/panel), exclusions, or comparisons to sibling tools such as 'get_dashboard' or 'get_application_profiling', leaving the agent to infer 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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