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Quaestor-Technologies

Standard Metrics MCP Server

list_information_reports

Retrieve a paginated list of information reports for a firm, filtered by company or information request ID.

Instructions

List all information reports associated with the firm.

Args: company_id: Filter by company ID information_request_id: Filter by information request ID page: Page number for pagination (default: 1) per_page: Results per page (default: 100, max: 100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_idNo
information_request_idNo
pageNo
per_pageNo

Implementation Reference

  • MCP tool handler that is decorated with @mcp.tool. Accepts optional company_id, information_request_id, page, and per_page; delegates to StandardMetrics client.
    @mcp.tool
    async def list_information_reports(
        company_id: str | None = None,
        information_request_id: str | None = None,
        page: int = 1,
        per_page: int = 100,
    ) -> PaginatedInformationReports:
        """List all information reports associated with the firm.
    
        Args:
            company_id: Filter by company ID
            information_request_id: Filter by information request ID
            page: Page number for pagination (default: 1)
            per_page: Results per page (default: 100, max: 100)
        """
        async with StandardMetrics() as client:
            return await client.list_information_reports(
                company_id=company_id,
                information_request_id=information_request_id,
                page=page,
                page_size=per_page,
            )
  • Client-side helper that makes the actual HTTP GET request to 'v1/information-reports/' endpoint with query parameters and returns a PaginatedInformationReports model.
    async def list_information_reports(
        self,
        *,
        company_id: str | None = None,
        information_request_id: str | None = None,
        page: int = 1,
        page_size: int = 100,
    ) -> PaginatedInformationReports:
        """List all information reports associated with the firm."""
        params: dict[str, Any] = {"page": page, "page_size": page_size}
        if company_id:
            params["company_id"] = company_id
        if information_request_id:
            params["information_request_id"] = information_request_id
        response = await self._request("GET", "v1/information-reports/", params=params)
        return PaginatedInformationReports.model_validate(response)
  • Pydantic model defining the schema for an InformationReport (the response type).
    class InformationReport(pydantic.BaseModel):
        id: str
        information_request_id: str
        company_id: str
        status: str
        submitted_at: dt.datetime | None = None
        documents: list[dict[str, str]] = []
        metrics: list[dict[str, str]] = []
  • Type alias for the paginated response wrapping InformationReport objects.
    PaginatedInformationReports = PaginatedResponse[InformationReport]
  • src/server.py:74-77 (registration)
    Registration of all tools via import of src.tools module, which defines functions decorated with @mcp.tool (including list_information_reports).
    mcp = fastmcp.FastMCP[Any](
        "smx-mcp",
        instructions=_MCP_INSTRUCTIONS,
    )
Behavior2/5

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

With no annotations, the description should disclose traits like read-only nature, permissions, or data scope. It only describes parameters, missing behavioral context.

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 extremely concise with a single sentence followed by a bullet-style list. Every sentence provides essential information, no fluff.

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?

For a simple list tool, the description covers the action and parameters but omits output structure (no output schema). Missing details like result format or ordering, which are useful for an agent.

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 input schema has 0% description coverage, so the description carries the burden. It provides brief but clear meanings for each parameter, including defaults and max for 'per_page', adding value beyond 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 it lists all information reports for the firm, using a specific verb and resource. It distinguishes from siblings like list_documents or list_notes.

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 on when to use this tool versus alternatives or when the filters are appropriate. No mention of prerequisites or exclusions.

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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