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jamesbrink

MCP Server for Coroot

create_project

Create a new Coroot project with a specified name to organize and monitor application performance metrics, logs, and infrastructure data.

Instructions

Create a new project.

Creates a new Coroot project with the specified name. The name must contain only lowercase letters, numbers, and hyphens.

Args: name: Project name (must match ^a-z0-9?$)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for create_project: decorated with @mcp.tool(), calls the impl function, includes schema in docstring and type annotations.
    @mcp.tool()
    async def create_project(name: str) -> dict[str, Any]:
        """Create a new project.
    
        Creates a new Coroot project with the specified name.
        The name must contain only lowercase letters, numbers, and hyphens.
    
        Args:
            name: Project name (must match ^[a-z0-9]([-a-z0-9]*[a-z0-9])?$)
        """
        return await create_project_impl(name)  # type: ignore[no-any-return]
  • Implementation helper that wraps the client call and formats the response.
    @handle_errors
    async def create_project_impl(name: str) -> dict[str, Any]:
        """Create a new project."""
        project = await get_client().create_project(name)
        return {
            "success": True,
            "message": "Project created successfully",
            "project": project,
        }
  • CorootClient method that performs the actual HTTP POST to create the project.
    async def create_project(self, name: str) -> dict[str, Any]:
        """Create a new project.
    
        Args:
            name: Project name (must match ^[a-z0-9]([-a-z0-9]*[a-z0-9])?$).
    
        Returns:
            Created project information.
        """
        data = {"name": name}
        response = await self._request("POST", "/api/project/", json=data)
    
        # Handle different response types
        try:
            if response.headers.get("content-type", "").startswith("application/json"):
                result: dict[str, Any] = response.json()
                return result
            else:
                # If not JSON, return a success indicator with the name
                return {"id": name, "name": name}
        except Exception:
            # If parsing fails, return minimal success response
            return {"id": name, "name": name}
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool creates a new project but doesn't disclose behavioral traits like required permissions, whether the operation is idempotent, what happens on duplicate names, rate limits, or what the output contains. The description is minimal beyond the basic action.

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. The two-sentence structure is efficient, though the Args section could be integrated more smoothly. Every sentence adds value with no 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 has an output schema (which handles return values) and only one parameter (well-documented in the description), the description is moderately complete. However, as a mutation tool with no annotations, it lacks behavioral context like permissions or side effects, leaving gaps for an AI agent.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It fully documents the single parameter 'name', including its purpose ('Project name') and a detailed regex constraint ('must match ^[a-z0-9]([-a-z0-9]*[a-z0-9])?$'), adding significant value beyond the schema's basic type information.

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 ('Create a new project') and resource ('Coroot project'), distinguishing it from sibling tools like 'delete_project', 'get_project', 'list_projects', and 'update_project_settings'. It provides a complete verb+resource+scope statement.

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

Usage Guidelines3/5

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

The description implies usage context through the naming constraint but doesn't explicitly state when to use this tool versus alternatives like 'list_projects' or 'update_project_settings'. No guidance on prerequisites, permissions, or exclusions is provided.

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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