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Quaestor-Technologies

Standard Metrics MCP Server

list_companies

List all companies associated with your firm with pagination and optional filtering by company IDs.

Instructions

List all companies associated with your firm.

Args: page: Page number for pagination (default: 1) per_page: Results per page (default: 100, max: 100) ids: Filter by specific company IDs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
per_pageNo
idsNo

Implementation Reference

  • The MCP tool handler for 'list_companies' decorated with @mcp.tool. Uses StandardMetrics client to call get_companies with pagination params and optional IDs filter.
    @mcp.tool
    async def list_companies(
        page: int = 1,
        per_page: int = 100,
        ids: list[str] | None = None,
    ) -> PaginatedCompanies:
        """List all companies associated with your firm.
    
        Args:
            page: Page number for pagination (default: 1)
            per_page: Results per page (default: 100, max: 100)
            ids: Filter by specific company IDs
        """
        async with StandardMetrics() as client:
            return await client.get_companies(page=page, page_size=per_page, ids=ids)
  • The Company Pydantic model returned by list_companies, containing id, name, slug, description, city, sector, etc.
    class Company(pydantic.BaseModel):
        id: str
        name: str
        slug: str | None = None
        description: str | None = None
        city: str | None = None
        sector: CompanySector | None = None
        firm_sector: str | None = None
        fiscal_year_end: str | None = pydantic.Field(
            None,
            description="MM/DD format",
        )
        website: str | None = None
        logo_url: str | None = None
        status: str | None = None
        investment_lead_id: str | None = None
        invested_fund_ids: list[str] | None = None
        unique_ref: str | None = None
  • The PaginatedResponse generic model and PaginatedCompanies type alias used as the return type of list_companies.
    class PaginatedResponse[T](pydantic.BaseModel):
        results: list[T]
        count: int | None = None
        next: str | None = None
        previous: str | None = None
    
    
    # These can't be type-aliases as we need to make use of runtime behavior.
    PaginatedCompanies = PaginatedResponse[Company]
  • src/server.py:74-78 (registration)
    Where all tools (including list_companies) are registered: the FastMCP server instance and the wildcard import that pulls in all @mcp.tool decorated functions from src/tools.py.
    mcp = fastmcp.FastMCP[Any](
        "smx-mcp",
        instructions=_MCP_INSTRUCTIONS,
    )
    from src.tools import *  # noqa: F403 - need to register all of the tools
  • The underlying API client method get_companies that sends a GET request to 'v1/companies/' and parses the response into PaginatedCompanies.
    async def get_companies(
        self,
        *,
        page: int = 1,
        page_size: int = 100,
        ids: list[str] | None = None,
    ) -> PaginatedCompanies:
        """Get all companies associated with your firm."""
        params: dict[str, Any] = {"page": page, "page_size": page_size}
        if ids:
            for company_id in ids:
                params.setdefault("ids[]", []).append(company_id)
        response = await self._request("GET", "v1/companies/", params=params)
        return PaginatedCompanies.model_validate(response)
Behavior3/5

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 mentions pagination defaults and a max value for per_page, providing some behavioral insight. However, it does not disclose whether the operation is read-only, idempotent, or any other traits.

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 concise and well-structured: a leading purpose sentence followed by a bullet list of parameters with clear explanations. No redundant text.

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?

The description adequately covers the parameters but lacks any mention of the output format or return structure. Given no output schema, this is a gap. Additionally, there is no information about authentication or rate limits, though these may be less critical for a list tool.

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?

With 0% schema description coverage, the description compensates by explaining each parameter's purpose (e.g., 'Page number for pagination', 'Filter by specific company IDs') and constraints like max per_page. This adds meaning beyond the schema's type and default.

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 lists all companies associated with the firm, with a specific verb and resource. However, it does not explicitly distinguish this tool from siblings like search_companies, though the name and context imply a broad listing.

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 such as search_companies or get_company. The description only explains parameters, lacking usage context 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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