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danielbres

massive-mcp

by danielbres

get_financials

Retrieve financial statements including income, balance sheet, cash flow, and ratios for a stock ticker. Supports annual, quarterly, or trailing twelve months data with customizable periods.

Instructions

Financial statements (income, balance sheet, cash flow, ratios) for a ticker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock symbol.
timeframeNo"annual", "quarterly", or "ttm". Default "quarterly".quarterly
limitNoMax periods returned. Default 4 (last year).
cursorNoPagination cursor.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'get_financials' async tool handler function. Accepts ticker, timeframe (annual/quarterly/ttm), limit, and cursor; calls the Massive API endpoint /vX/reference/financials with query params and returns the response dict.
    async def get_financials(
        ticker: str,
        timeframe: Timeframe = "quarterly",
        limit: int = 4,
        cursor: str | None = None,
    ) -> dict[str, Any]:
        """Financial statements (income, balance sheet, cash flow, ratios) for a ticker.
    
        Args:
            ticker: Stock symbol.
            timeframe: "annual", "quarterly", or "ttm". Default "quarterly".
            limit: Max periods returned. Default 4 (last year).
            cursor: Pagination cursor.
        """
        return await client.get(
            "/vX/reference/financials",
            {
                "ticker": ticker,
                "timeframe": timeframe,
                "limit": limit,
                "cursor": cursor,
                "order": "desc",
            },
        )
  • Timeframe type alias (Literal['annual', 'quarterly', 'ttm']) used for the timeframe parameter of get_financials.
    Timeframe = Literal["annual", "quarterly", "ttm"]
  • Registration loop iterates over tool modules (including financials) and calls module.register(mcp, client) to register all tools with the FastMCP server.
    for module in (
        aggregates,
        quotes,
        snapshots,
        tickers,
        news,
        reference,
        indicators,
        corporate,
        financials,
    ):
        module.register(mcp, client)
  • The register() function that uses the @mcp.tool() decorator to register get_financials (and other tools) with FastMCP.
    def register(mcp: FastMCP, client: MassiveClient) -> None:
        @mcp.tool()
        async def get_financials(
            ticker: str,
            timeframe: Timeframe = "quarterly",
            limit: int = 4,
            cursor: str | None = None,
        ) -> dict[str, Any]:
            """Financial statements (income, balance sheet, cash flow, ratios) for a ticker.
    
            Args:
                ticker: Stock symbol.
                timeframe: "annual", "quarterly", or "ttm". Default "quarterly".
                limit: Max periods returned. Default 4 (last year).
                cursor: Pagination cursor.
            """
            return await client.get(
                "/vX/reference/financials",
                {
                    "ticker": ticker,
                    "timeframe": timeframe,
                    "limit": limit,
                    "cursor": cursor,
                    "order": "desc",
                },
            )
  • MassiveClient.get() - the HTTP helper that executes the actual API call; get_financials delegates to this method.
    async def get(
        self, path: str, params: dict[str, Any] | None = None, *, trim: bool = True
    ) -> dict[str, Any]:
        merged: dict[str, Any] = {k: v for k, v in (params or {}).items() if v is not None}
        if self._settings.auth_mode == "query":
            merged["apiKey"] = self._settings.api_key
    
        last_exc: Exception | None = None
        for attempt in range(MAX_RETRIES):
            try:
                resp = await self._http.get(path, params=merged)
            except httpx.HTTPError as exc:
                last_exc = exc
                await asyncio.sleep(2**attempt)
                continue
    
            if resp.status_code == 429:
                retry_after = float(resp.headers.get("Retry-After", 2**attempt))
                await asyncio.sleep(min(retry_after, 30))
                continue
            if 500 <= resp.status_code < 600 and attempt < MAX_RETRIES - 1:
                await asyncio.sleep(2**attempt)
                continue
    
            if resp.status_code == 401:
                hint = (
                    "auth rejected — verify MASSIVE_API_KEY; "
                    "if you used MASSIVE_AUTH_MODE=bearer, try 'query' (or vice versa)"
                )
                raise MassiveAPIError(401, hint, _strip_secrets(str(resp.request.url)))
    
            try:
                data = resp.json()
            except ValueError:
                data = {"raw": resp.text}
    
            if not resp.is_success:
                msg = data.get("error") or data.get("message") or resp.reason_phrase or "request failed"
                raise MassiveAPIError(resp.status_code, str(msg), _strip_secrets(str(resp.request.url)))
    
            return _trim(data) if trim else data
    
        raise MassiveAPIError(0, f"network error after {MAX_RETRIES} retries: {last_exc}", path)
Behavior2/5

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

With no annotations, the description should disclose behaviors such as pagination, rate limits, and that it is read-only. The description only lists output types, omitting how the tool operates or handles cursors/timeframes.

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 a single sentence with no wasted words. It is front-loaded with the main output. However, it could include a bit more context without becoming verbose.

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 output schema and parameter richness, the description lacks context on pagination, when to use, and behavioral traits. It is minimally complete but leaves significant gaps.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema; it doesn't explain parameter usage or constraints like enum values or defaults.

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 specifies that the tool returns financial statements (income, balance sheet, cash flow, ratios) for a given ticker. This is specific and distinguishes it from sibling tools that return quotes, snapshots, or summaries.

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 like get_ticker_overview or get_snapshot. There is no mention of prerequisites, limitations, or best practices.

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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