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danielbres

massive-mcp

by danielbres

get_daily_market_summary

Retrieve open, high, low, close (OHLC) data for the entire US stock market on a specified trading date, with optional adjustment for stock splits.

Instructions

OHLC for the entire US stock market on a given trading date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes"YYYY-MM-DD".
adjustedNoAdjust for splits. Default true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler: an MCP tool decorated with @mcp.tool(). Calls the Massive API endpoint /v2/aggs/grouped/locale/us/market/stocks/{date} to return OHLC data for the entire US stock market on a given date.
    @mcp.tool()
    async def get_daily_market_summary(date: str, adjusted: bool = True) -> dict[str, Any]:
        """OHLC for the entire US stock market on a given trading date.
    
        Args:
            date: "YYYY-MM-DD".
            adjusted: Adjust for splits. Default true.
        """
        return await client.get(
            f"/v2/aggs/grouped/locale/us/market/stocks/{date}",
            {"adjusted": str(adjusted).lower()},
        )
  • Registration mechanism: the module exposes a register() function which is called from server.py line 48 (module.register(mcp, client)). The @mcp.tool() decorator registers the handler with the FastMCP server.
    def register(mcp: FastMCP, client: MassiveClient) -> None:
        @mcp.tool()
  • Server registration: loops through tool modules and calls their register() function to register all tools, including get_daily_market_summary from aggregates.py.
    for module in (
        aggregates,
        quotes,
        snapshots,
        tickers,
        news,
        reference,
        indicators,
        corporate,
        financials,
    ):
        module.register(mcp, client)
  • The HTTP client helper that performs the actual GET request with retry logic, error handling, and response trimming. Used by the get_daily_market_summary handler to call the Massive API.
    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?

Description only mentions OHLC, missing details like volume, adjusted handling, or output structure. No annotation support.

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?

Single sentence is concise but omits important context; efficient but could be more informative.

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?

Minimal description for a financial tool; lacks details on output, date handling, or limitations despite having an output schema.

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 covers 100% of parameters with descriptions; description adds no extra meaning beyond what schema provides.

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?

Description clearly states it provides OHLC for the entire US stock market, distinguishing it from sibling tools like get_daily_ticker_summary.

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 explicit guidance on when to use this tool versus alternatives like get_daily_ticker_summary for individual tickers.

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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