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jamesbrink

MCP Server for Coroot

list_dashboards

Retrieve all user-created dashboards and their configurations for a specified project in the Coroot observability platform.

Instructions

List all custom dashboards for a project.

Returns all user-created dashboards with their configurations.

Args: project_id: Project ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'list_dashboards'. This is the entry point for the tool execution, decorated with @mcp.tool(). It calls the implementation helper.
    async def list_dashboards(project_id: str) -> dict[str, Any]:
        """List all custom dashboards for a project.
    
        Returns all user-created dashboards with their configurations.
    
        Args:
            project_id: Project ID
        """
        return await list_dashboards_impl(project_id)  # type: ignore[no-any-return]
  • Helper function that invokes the CorootClient's list_dashboards method and formats the response with success indicator.
    async def list_dashboards_impl(project_id: str) -> dict[str, Any]:
        """List all dashboards."""
        dashboards = await get_client().list_dashboards(project_id)
        return {
            "success": True,
            "dashboards": dashboards,
        }
  • CorootClient method that makes the actual HTTP GET request to the Coroot API endpoint /api/project/{project_id}/dashboards to fetch the list of dashboards.
    async def list_dashboards(self, project_id: str) -> dict[str, Any]:
        """List all custom dashboards for a project.
    
        Args:
            project_id: Project ID.
    
        Returns:
            List of dashboards.
        """
        response = await self._request("GET", f"/api/project/{project_id}/dashboards")
        return self._parse_json_response(response)
  • FastMCP tool registration decorator that registers the list_dashboards function as an MCP 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 the full burden of behavioral disclosure. It mentions the tool returns all user-created dashboards with configurations, which implies a read-only operation, but does not specify permissions, pagination, rate limits, or error handling. This leaves significant gaps for a tool with no annotation coverage.

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: one stating the purpose, one detailing the return, and one listing the parameter. It is front-loaded with the main action, but the 'Args' section could be integrated more smoothly for better flow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which handles return values), a single parameter well-documented in the description, and no annotations, the description is reasonably complete. It covers the purpose and parameter semantics adequately, though behavioral aspects like permissions or pagination are missing.

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 documents the single parameter 'project_id' as 'Project ID', adding meaning beyond the input schema which has 0% description coverage. This compensates well for the low schema coverage, though it could provide more context like format or 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 verb ('List') and resource ('all custom dashboards for a project'), specifying that it returns user-created dashboards with configurations. However, it does not explicitly differentiate from sibling tools like 'get_dashboard' (which likely retrieves a single dashboard) or 'create_dashboard', leaving some ambiguity in sibling 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. It does not mention sibling tools such as 'get_dashboard' for retrieving a single dashboard or 'list_projects' for listing projects, nor does it specify prerequisites or exclusions 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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