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jamesbrink

MCP Server for Coroot

get_dashboard

Retrieve dashboard configuration and panel details from the Coroot observability platform to monitor application performance metrics.

Instructions

Get a specific dashboard configuration.

Retrieves the full configuration of a dashboard including all panels.

Args: project_id: Project ID dashboard_id: Dashboard ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
dashboard_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler function registered with @mcp.tool(). This is the entry point for the 'get_dashboard' tool, which delegates to the implementation helper.
    @mcp.tool()
    async def get_dashboard(project_id: str, dashboard_id: str) -> dict[str, Any]:
        """Get a specific dashboard configuration.
    
        Retrieves the full configuration of a dashboard including all panels.
    
        Args:
            project_id: Project ID
            dashboard_id: Dashboard ID
        """
        return await get_dashboard_impl(project_id, dashboard_id)  # type: ignore[no-any-return]
  • Helper implementation function wrapped with error handling that calls the CorootClient to retrieve dashboard details and formats the response.
    async def get_dashboard_impl(project_id: str, dashboard_id: str) -> dict[str, Any]:
        """Get dashboard details."""
        dashboard = await get_client().get_dashboard(project_id, dashboard_id)
        return {
            "success": True,
            "dashboard": dashboard,
        }
  • CorootClient method that executes the actual HTTP GET request to the Coroot API endpoint to fetch the specific dashboard configuration.
    async def get_dashboard(self, project_id: str, dashboard_id: str) -> dict[str, Any]:
        """Get a specific dashboard.
    
        Args:
            project_id: Project ID.
            dashboard_id: Dashboard ID.
    
        Returns:
            Dashboard configuration.
        """
        response = await self._request(
            "GET", f"/api/project/{project_id}/dashboards/{dashboard_id}"
        )
        data: dict[str, Any] = response.json()
        return data
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 configuration, implying a read-only operation, but doesn't specify permissions required, rate limits, error conditions, or what 'full configuration' entails (e.g., JSON structure, size limits). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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, with the core purpose in the first sentence and parameter details in a structured 'Args' section. There's no wasted text, though the parameter section could be more integrated. It efficiently conveys key information without redundancy.

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 (2 required parameters), no annotations, and the presence of an output schema (which handles return values), the description is somewhat complete but has gaps. It covers the basic purpose and parameters but lacks behavioral details and usage guidelines. The output schema mitigates the need to describe return values, but other aspects like error handling or performance are missing.

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 descriptions. The description adds minimal semantics by listing the parameters ('project_id' and 'dashboard_id') and stating they are required, but doesn't explain their format, sourcing (e.g., from 'list_dashboards'), or constraints. This partially compensates but falls short of fully clarifying parameter meaning, aligning with the baseline when schema coverage is low.

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 with the verb 'Get' and resource 'dashboard configuration', specifying it retrieves 'full configuration including all panels'. It distinguishes from siblings like 'list_dashboards' (which lists dashboards) and 'get_panel_data' (which gets panel data, not configuration). However, it doesn't explicitly contrast with 'update_dashboard' or 'delete_dashboard', keeping it from a perfect score.

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 when to choose 'get_dashboard' over 'list_dashboards' for listing dashboards, 'get_panel_data' for panel data, or 'update_dashboard' for modifications. There's also no mention of prerequisites or contextual cues for usage.

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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