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jamesbrink

MCP Server for Coroot

delete_project

Remove a project and all its associated data from the Coroot observability platform. This irreversible action permanently deletes project information, metrics, logs, and traces.

Instructions

Delete a project and all associated data.

WARNING: This action is irreversible and will delete all project data.

Args: project_id: Project ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool registration and handler for 'delete_project'. This is the entry point for the MCP tool, decorated with @mcp.tool() which registers it, and delegates to the implementation.
    @mcp.tool()
    async def delete_project(project_id: str) -> dict[str, Any]:
        """Delete a project and all associated data.
    
        WARNING: This action is irreversible and will delete all project data.
    
        Args:
            project_id: Project ID
        """
        return await delete_project_impl(project_id)  # type: ignore[no-any-return]
  • Core implementation of project deletion in the CorootClient. Performs the HTTP DELETE request to the Coroot API endpoint /api/project/{project_id} and handles various response formats.
    async def delete_project(self, project_id: str) -> dict[str, Any]:
        """Delete a project.
    
        Args:
            project_id: Project ID.
    
        Returns:
            Deletion status.
        """
        response = await self._request("DELETE", f"/api/project/{project_id}")
    
        # Handle empty response (204 or empty body)
        if response.status_code == 204:
            return {"status": "deleted"}
    
        # Try to parse JSON response
        try:
            content = response.text.strip()
            if not content:
                # Empty response body with 200 status
                return {"status": "deleted"}
            data: dict[str, Any] = response.json()
            return data
        except Exception:
            # If parsing fails, assume success if status code is 2xx
            if 200 <= response.status_code < 300:
                return {"status": "deleted"}
            raise
  • Helper implementation function called by the MCP tool handler. Wraps the client call and formats the success response.
    @handle_errors
    async def delete_project_impl(project_id: str) -> dict[str, Any]:
        """Delete a project."""
        result = await get_client().delete_project(project_id)
        return {
            "success": True,
            "message": f"Project {project_id} deleted successfully",
            "result": result,
        }
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It effectively discloses critical behavioral traits: the action is irreversible, deletes all associated data, and includes a clear warning. This goes beyond the basic 'delete' verb to highlight risks and consequences, which is essential for a destructive operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the purpose, followed by a critical warning, and then parameter details. Every sentence adds value without redundancy, making it efficient and easy to parse.

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's high complexity (destructive operation) and no annotations, the description does well by covering purpose, warning, and parameter semantics. However, it lacks details on prerequisites (e.g., permissions) or output behavior, though the presence of an output schema mitigates the latter. It is mostly complete but could be enhanced with more context.

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 schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'project_id' is the Project ID, which clarifies the parameter's purpose beyond the schema's title. However, it does not provide additional details like format or constraints, leaving some gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Delete a project and all associated data'), identifies the resource ('project'), and distinguishes it from sibling tools like 'create_project' or 'get_project' by focusing on deletion. It goes beyond just restating the name by specifying the scope of deletion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (to delete a project and its data) and includes a WARNING about irreversibility, which helps guide usage. However, it does not explicitly mention alternatives (e.g., archiving or deactivating) or when not to use it, such as compared to 'update_project_settings' for modifications.

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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