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danielbres

massive-mcp

by danielbres

get_snapshot

Retrieves a real-time stock snapshot for a ticker, including last trade, last quote, and daily open/high/low/close data.

Instructions

Real-time snapshot for a single stock: last trade, last quote, day OHLC, prev day OHLC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock symbol (e.g. "TSLA").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual tool handler function 'get_snapshot'—an async function that takes a ticker string and calls the Massive API endpoint /v2/snapshot/locale/us/markets/stocks/tickers/{ticker} to return real-time snapshot data.
    async def get_snapshot(ticker: str) -> dict[str, Any]:
        """Real-time snapshot for a single stock: last trade, last quote, day OHLC, prev day OHLC.
    
        Args:
            ticker: Stock symbol (e.g. "TSLA").
        """
        return await client.get(f"/v2/snapshot/locale/us/markets/stocks/tickers/{ticker}")
  • The 'register' function that decorates 'get_snapshot' with @mcp.tool() to register it with the FastMCP server.
    def register(mcp: FastMCP, client: MassiveClient) -> None:
        @mcp.tool()
        async def get_snapshot(ticker: str) -> dict[str, Any]:
            """Real-time snapshot for a single stock: last trade, last quote, day OHLC, prev day OHLC.
    
            Args:
                ticker: Stock symbol (e.g. "TSLA").
            """
            return await client.get(f"/v2/snapshot/locale/us/markets/stocks/tickers/{ticker}")
  • The snapshots module is imported in the tools __init__.py, enabling registration.
    snapshots,
  • The 'build_server' function calls module.register(mcp, client) for snapshots (and others), which registers all tools including get_snapshot with the MCP server.
    for module in (
        aggregates,
        quotes,
        snapshots,
        tickers,
        news,
        reference,
        indicators,
        corporate,
        financials,
    ):
        module.register(mcp, client)
Behavior2/5

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

No annotations exist; description carries full burden. It mentions 'real-time' but doesn't disclose caching, data latency, rate limits, or side effects. Minimal behavioral insight beyond data included.

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?

Single sentence, no wasted words. Front-loads key information: purpose and data fields. Highly efficient.

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

Completeness4/5

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

Simple tool with one parameter and output schema present. Description covers core data points. Could mention that data is real-time (not delayed) but overall adequate given low complexity and presence of 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?

Parameter 'ticker' has schema description 'Stock symbol (e.g. "TSLA").' Description adds no additional meaning beyond what schema provides. Schema coverage is 100%, so baseline 3 applies.

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 verb and resource: 'Real-time snapshot for a single stock' with specific data points (last trade, last quote, OHLC). Distinct from siblings like get_all_snapshots (multiple stocks) and get_last_trade/get_last_quote (single fields).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use vs alternatives. Implied usage is for a comprehensive real-time view of one stock, but no exclusions (e.g., 'for historical data use get_aggregates') are provided.

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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