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jamesbrink

MCP Server for Coroot

get_custom_cloud_pricing

Retrieve custom cloud pricing configurations for accurate cost calculations in your project. This tool fetches pricing overrides to ensure precise infrastructure cost analysis.

Instructions

Get custom cloud pricing configuration.

Retrieves any custom cloud pricing overrides for cost calculations.

Args: project_id: Project ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler function that wraps the implementation and provides the tool interface with documentation.
    @mcp.tool()
    async def get_custom_cloud_pricing(project_id: str) -> dict[str, Any]:
        """Get custom cloud pricing configuration.
    
        Retrieves any custom cloud pricing overrides for cost calculations.
    
        Args:
            project_id: Project ID
        """
        return await get_custom_cloud_pricing_impl(project_id)  # type: ignore[no-any-return]
  • Helper implementation in server that calls the client method and formats the response.
    @handle_errors
    async def get_custom_cloud_pricing_impl(project_id: str) -> dict[str, Any]:
        """Get custom cloud pricing."""
        client = get_client()
        pricing = await client.get_custom_cloud_pricing(project_id)
        return {
            "success": True,
            "pricing": pricing,
        }
  • Core client method that performs the HTTP request to the Coroot API to fetch custom cloud pricing data.
    async def get_custom_cloud_pricing(self, project_id: str) -> dict[str, Any]:
        """Get custom cloud pricing configuration.
    
        Args:
            project_id: Project ID.
    
        Returns:
            Custom pricing configuration.
        """
        response = await self._request(
            "GET", f"/api/project/{project_id}/custom_cloud_pricing"
        )
        data: dict[str, Any] = response.json()
        return data
  • MCP tool registration decorator applied to the handler function.
    @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 ('Get', 'Retrieves'), implying it's a read-only operation, but doesn't specify authentication requirements, rate limits, error conditions, or the format/scope of returned data (e.g., whether it returns all overrides or a subset). 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 with three sentences: a title-like statement, a clarifying sentence, and a parameter section. It's front-loaded with the core purpose and avoids unnecessary details. However, the 'Args' section could be integrated more smoothly, and there's minor redundancy between the first two sentences.

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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally complete. It covers the purpose and parameter but lacks behavioral details (e.g., permissions, errors) and usage guidelines. With no annotations, it should do more to compensate, but the output schema reduces the burden slightly.

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', adding meaning beyond the input schema, which has 0% description coverage. This compensates well for the schema's lack of descriptions, though it doesn't provide additional context like format examples or constraints. With only one parameter, the baseline is high, and the description adequately covers it.

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 'Get custom cloud pricing configuration' and 'Retrieves any custom cloud pricing overrides for cost calculations', specifying the verb ('get', 'retrieves') and resource ('custom cloud pricing configuration', 'overrides'). It distinguishes from siblings like 'update_custom_cloud_pricing' and 'delete_custom_cloud_pricing' by focusing on retrieval, but doesn't explicitly differentiate from other 'get_' tools in the list.

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 a valid project_id), compare with similar tools like 'get_project' or 'get_application', or specify scenarios where custom pricing overrides are relevant versus default pricing. Usage is implied only by the tool's name and description.

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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