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danielbres

massive-mcp

by danielbres

get_ipos

Search IPO listings by ticker, listing date, or pagination cursor. Filter and retrieve initial public offering data.

Instructions

IPO listings.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'get_ipos' tool. It calls the Massive API client to fetch IPO listings with optional ticker, listing_date lower bound, limit, and pagination cursor parameters.
    @mcp.tool()
    async def get_ipos(
        ticker: str | None = None,
        listing_date_gte: str | None = None,
        limit: int = 20,
        cursor: str | None = None,
    ) -> dict[str, Any]:
        """IPO listings.
    
        Args:
            ticker: Filter by symbol.
            listing_date_gte: Inclusive lower bound on listing date.
            limit: Max rows. Default 20.
            cursor: Pagination cursor.
        """
        return await client.get(
            "/vX/reference/ipos",
            {
                "ticker": ticker,
                "listing_date.gte": listing_date_gte,
                "limit": limit,
                "cursor": cursor,
                "order": "desc",
            },
        )
  • The tool is registered as an MCP tool via the @mcp.tool() decorator on the get_ipos handler function.
    @mcp.tool()
    async def get_ipos(
Behavior2/5

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

No annotations provided, so description carries full burden. It only says 'IPO listings' with no mention of whether it is read-only, requires authentication, or has rate limits. Does not disclose behavior beyond the noun phrase.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise (2 words), but at the cost of clarity. Under-specification is not conciseness; the description should provide enough information without being verbose.

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

Completeness1/5

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

Given 4 parameters and an output schema (not detailed), the description is grossly insufficient. It does not explain what an IPO listing object contains, pagination behavior, or what the output looks like. Without annotations, this is incomplete.

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% with parameter descriptions (e.g., 'Filter by symbol.'). The description adds no additional meaning beyond what the schema already provides, so baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description is 'IPO listings' which names the resource but lacks a verb. It's clear it relates to IPOs but does not specify the action (e.g., retrieve, list). This is borderline tautological with the tool name 'get_ipos'.

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

Usage Guidelines1/5

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. Sibling tools include many data retrieval functions, but the description provides no context for differentiation or prerequisites.

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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