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danielbres

massive-mcp

by danielbres

get_news

Fetch news articles about stocks. Optionally filter by ticker symbol, date range, and set pagination.

Instructions

Recent news articles, optionally filtered by ticker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerNoOptional symbol filter (e.g. "AAPL").
published_utc_gteNoInclusive lower bound on publish time ("YYYY-MM-DD" or RFC3339).
published_utc_lteNoInclusive upper bound.
limitNoMax articles. Default 10.
cursorNoPagination cursor.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `get_news` async function that executes the tool logic. It calls the Massive API endpoint `/v2/reference/news` with optional ticker filter, date range, limit, cursor, and sorting parameters.
    async def get_news(
        ticker: str | None = None,
        published_utc_gte: str | None = None,
        published_utc_lte: str | None = None,
        limit: int = 10,
        cursor: str | None = None,
    ) -> dict[str, Any]:
        """Recent news articles, optionally filtered by ticker.
    
        Args:
            ticker: Optional symbol filter (e.g. "AAPL").
            published_utc_gte: Inclusive lower bound on publish time ("YYYY-MM-DD" or RFC3339).
            published_utc_lte: Inclusive upper bound.
            limit: Max articles. Default 10.
            cursor: Pagination cursor.
        """
        return await client.get(
            "/v2/reference/news",
            {
                "ticker": ticker,
                "published_utc.gte": published_utc_gte,
                "published_utc.lte": published_utc_lte,
                "limit": limit,
                "cursor": cursor,
                "order": "desc",
                "sort": "published_utc",
            },
        )
  • The `register` function decorates `get_news` with `@mcp.tool()` to register it as an MCP tool.
    def register(mcp: FastMCP, client: MassiveClient) -> None:
        @mcp.tool()
  • Input schema/type hints for `get_news`: ticker (optional str), published_utc_gte/lte (optional str), limit (int, default 10), cursor (optional str). Returns dict[str, Any].
        ticker: str | None = None,
        published_utc_gte: str | None = None,
        published_utc_lte: str | None = None,
        limit: int = 10,
        cursor: str | None = None,
    ) -> dict[str, Any]:
  • The server loop that calls `module.register(mcp, client)` for each module including `news`, which triggers the registration of the `get_news` tool.
    for module in (
        aggregates,
        quotes,
        snapshots,
        tickers,
        news,
        reference,
        indicators,
        corporate,
        financials,
    ):
        module.register(mcp, client)
  • The news module is imported and exported in the tools package `__init__.py`, making `register` accessible for tool registration.
        news,
        quotes,
        reference,
        snapshots,
        tickers,
    )
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It mentions 'recent' without defining the time range, omits return structure (despite an output schema existing), and lacks details on pagination, rate limits, or data sources.

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, front-loading the core action. While very short, it avoids unnecessary words, but could include more context without becoming bloated.

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 five parameters and an output schema, the description is too sparse. It does not explain what 'recent' means, how results are ordered, or what fields are returned, leaving agents with insufficient context.

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 description coverage is 100%, so baseline is 3. The description adds minimal value beyond the schema, only reinforcing the ticker filter. It does not mention other parameters like date bounds or pagination.

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 states the tool returns recent news articles with optional ticker filtering, which is a specific verb+resource. However, it does not differentiate from siblings like 'get_ticker_events' or 'get_snapshot', though the tool's function is distinct enough.

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 on when to use this tool versus alternatives. The description provides no context about when to choose this over other news or data tools, nor any prerequisites or excluded scenarios.

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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