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Quaestor-Technologies

Standard Metrics MCP Server

get_custom_column_options

Retrieve custom columns and their options from Standard Metrics. Specify page and per-page parameters to paginate results.

Instructions

Get all custom columns and their available options.

Args: page: Page number for pagination (default: 1) per_page: Results per page (default: 100, max: 100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
per_pageNo

Implementation Reference

  • The MCP tool handler function decorated with @mcp.tool that exposes 'get_custom_column_options'. Calls client.get_custom_column_options(page, page_size).
    @mcp.tool
    async def get_custom_column_options(
        page: int = 1,
        per_page: int = 100,
    ) -> PaginatedCustomColumnOptions:
        """Get all custom columns and their available options.
    
        Args:
            page: Page number for pagination (default: 1)
            per_page: Results per page (default: 100, max: 100)
        """
        async with StandardMetrics() as client:
            return await client.get_custom_column_options(page=page, page_size=per_page)
  • Client-side implementation that makes the actual HTTP GET request to 'v1/custom-columns/options/' and validates the response into PaginatedCustomColumnOptions.
    async def get_custom_column_options(
        self,
        *,
        page: int = 1,
        page_size: int = 100,
    ) -> PaginatedCustomColumnOptions:
        """Get all custom columns and their available options."""
        params: dict[str, Any] = {"page": page, "page_size": page_size}
        response = await self._request("GET", "v1/custom-columns/options/", params=params)
        return PaginatedCustomColumnOptions.model_validate(response)
  • Pydantic model representing a single custom column option with id, name, type, and optional list of options.
    class CustomColumnOption(pydantic.BaseModel):
        id: str
        name: str
        type: str
        options: list[Option] | None = None
  • Type alias for a paginated response containing CustomColumnOption items.
    PaginatedCustomColumnOptions = PaginatedResponse[CustomColumnOption]
  • src/server.py:35-35 (registration)
    Documentation/listing of the tool in the server description; the actual registration is via the @mcp.tool decorator in src/tools.py.
    - get_custom_column_options: Get available custom column definitions
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only mentions pagination parameters but fails to indicate the read-only nature, authentication needs, or any side effects. The description is insufficient for an unannotated tool.

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 short and front-loaded with the main purpose. The Args section provides a clear parameter listing. However, the total description could be condensed further by integrating the parameters into a single sentence.

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

Completeness2/5

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

Given the complexity (no output schema) and many sibling tools, the description is incomplete. It does not describe the return format, the structure of options, or any pagination behavior. It also lacks context on when to use this over other data retrieval tools.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description adds minimal value: it restates default values and adds a max limit for 'per_page' (100). However, it does not explain the meaning or typical use of 'page' or 'per_page' beyond what is in the schema.

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 'Get all custom columns and their available options,' specifying the verb 'Get' and the resource 'custom columns and options.' This distinguishes it from sibling tools like 'get_custom_columns' which likely focuses only on column definitions.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention preferred scenarios, exclusions, or relate to any sibling tools like 'get_custom_columns'.

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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