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danielbres

massive-mcp

by danielbres

get_splits

Retrieve historical and upcoming stock splits for any ticker. Filter by execution date and limit results with pagination.

Instructions

Historical and upcoming stock splits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerNoFilter by symbol.
execution_date_gteNoInclusive lower bound on execution date.
limitNoMax rows. Default 20.
cursorNoPagination cursor.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_splits tool handler: an async function registered as an MCP tool that calls the Massive API endpoint /v3/reference/splits with optional ticker, execution_date_gte, limit, cursor, and order=desc parameters.
    async def get_splits(
        ticker: str | None = None,
        execution_date_gte: str | None = None,
        limit: int = 20,
        cursor: str | None = None,
    ) -> dict[str, Any]:
        """Historical and upcoming stock splits.
    
        Args:
            ticker: Filter by symbol.
            execution_date_gte: Inclusive lower bound on execution date.
            limit: Max rows. Default 20.
            cursor: Pagination cursor.
        """
        return await client.get(
            "/v3/reference/splits",
            {
                "ticker": ticker,
                "execution_date.gte": execution_date_gte,
                "limit": limit,
                "cursor": cursor,
                "order": "desc",
            },
        )
  • The register function in corporate.py that registers get_splits (along with get_dividends, get_ipos, get_ticker_events) as an MCP tool via the @mcp.tool() decorator.
    def register(mcp: FastMCP, client: MassiveClient) -> None:
        @mcp.tool()
        async def get_dividends(
            ticker: str | None = None,
            ex_dividend_date_gte: str | None = None,
            limit: int = 20,
            cursor: str | None = None,
        ) -> dict[str, Any]:
            """Historical and upcoming dividends.
    
            Args:
                ticker: Filter by symbol. If None, returns across all tickers.
                ex_dividend_date_gte: Inclusive lower bound on ex-date ("YYYY-MM-DD").
                limit: Max rows. Default 20.
                cursor: Pagination cursor.
            """
            return await client.get(
                "/v3/reference/dividends",
                {
                    "ticker": ticker,
                    "ex_dividend_date.gte": ex_dividend_date_gte,
                    "limit": limit,
                    "cursor": cursor,
                    "order": "desc",
                },
            )
    
        @mcp.tool()
        async def get_splits(
            ticker: str | None = None,
            execution_date_gte: str | None = None,
            limit: int = 20,
            cursor: str | None = None,
        ) -> dict[str, Any]:
            """Historical and upcoming stock splits.
    
            Args:
                ticker: Filter by symbol.
                execution_date_gte: Inclusive lower bound on execution date.
  • The build_server function in server.py that calls corporate.register(mcp, client), which registers get_splits as an MCP tool.
    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?

With no annotations, the description must disclose behavioral traits. It states 'historical and upcoming' but omits details like ordering (chronological), how upcoming splits are defined, or that the tool is read-only. The output schema exists but the description adds no behavioral context such as rate limits or authentication.

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, concise sentence with no wasted words. It is front-loaded with the core purpose. However, given the minimal content, it sacrifices completeness for brevity, which is acceptable but not excellent.

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?

Although an output schema exists, the description lacks context about what constitutes a split (e.g., ratio, date). It does not explain typical use cases (e.g., adjusting prices). For a tool with 4 optional parameters and 22 siblings, more context would help the agent decide when to use it.

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% (each parameter has a description), so the baseline is 3. The description 'Historical and upcoming stock splits' adds no additional parameter info beyond the schema. It does not explain how parameters interact (e.g., date filter with ticker).

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 identifies the resource as 'stock splits' and specifies both historical and upcoming events. It distinguishes this tool from siblings like 'get_dividends' (dividends) and 'get_aggregates' (bars) by mentioning the specific subject. However, it could be more precise about the data returned (e.g., execution date, ratio).

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 over siblings. For example, it doesn't mention that this complements 'get_dividends' for corporate actions or that limit and cursor handle pagination. The agent must infer usage from the name alone.

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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