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get_breaking

Retrieve breaking news with high importance scores from the last 24 hours, filtered for market-moving stories.

Instructions

Breaking news only — last 24 hours, importance score >= 75. Lower volume than get_latest but every item is market-moving by Zipp's editorial threshold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen-US
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'get_breaking'. Registered via @mcp.tool decorator with name='get_breaking' and a description of breaking news (importance >= 75, last 24h). The async function accepts 'lang' (default en-US) and 'limit' (default 10), then delegates to client.get_breaking().
    @mcp.tool(
        name="get_breaking",
        description=(
            "Breaking news only — last 24 hours, importance score >= 75. "
            "Lower volume than get_latest but every item is market-moving "
            "by Zipp's editorial threshold."
        ),
    )
    async def get_breaking(
        lang: str = _DEFAULT_LANG,
        limit: int = _DEFAULT_LIMIT,
    ) -> dict[str, Any]:
        """Get breaking news (importance >= 75) from the last 24h.
    
        Args:
            lang: BCP-47 language tag (default en-US).
            limit: Max results (1-30, default 10).
        """
        async with ZippClient() as client:
            return await client.get_breaking(lang=lang, limit=limit)
  • ZippClient.get_breaking() — the underlying HTTP helper that calls GET /api/v1/news/breaking with lang and limit query parameters, returning the raw JSON dict.
    async def get_breaking(
        self,
        *,
        lang: str = "en-US",
        limit: int = 10,
    ) -> dict[str, Any]:
        return await self._get("/breaking", params={"lang": lang, "limit": limit})
  • Registration infrastructure: _build_mcp() creates the FastMCP instance, assigned to module-level 'mcp'. The @mcp.tool decorator on line 118 registers 'get_breaking' as an MCP tool.
    def _build_mcp() -> FastMCP:
        settings = get_settings()
        return FastMCP(
            name="Zipp",
            instructions=_INSTRUCTIONS,
            host=settings.mcp_host,
            port=settings.mcp_port,
            transport_security=TransportSecuritySettings(
                enable_dns_rebinding_protection=False,
            ),
        )
    
    
    mcp = _build_mcp()
  • Test verifying the get_breaking client method constructs the correct path /api/v1/news/breaking with query params lang=tr-TR and limit=3.
    async def test_get_breaking_path(settings: Settings) -> None:
        seen: list[httpx.Request] = []
    
        def handler(req: httpx.Request) -> httpx.Response:
            seen.append(req)
            return httpx.Response(200, json={"items": []})
    
        async with ZippClient(settings=settings, transport=httpx.MockTransport(handler)) as c:
            await c.get_breaking(lang="tr-TR", limit=3)
    
        assert seen[0].url.path == "/api/v1/news/breaking"
        params = parse_qs(seen[0].url.query.decode())
        assert params["lang"] == ["tr-TR"]
        assert params["limit"] == ["3"]
  • Test verifying that 'get_breaking' is among the registered tool names on the MCP server.
    @pytest.mark.parametrize(
        "tool_name",
        [
            "search",
            "get_latest",
            "get_breaking",
            "get_featured",
            "get_post",
            "list_categories",
        ],
    )
Behavior4/5

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

With no annotations, the description discloses selection criteria (24h, importance threshold) and editorial threshold (Zipp's). It does not mention rate limits or authentication, but as a read operation, this is acceptable.

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?

Two sentences, front-loaded with the key purpose and differentiation, no wasted words.

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?

Output schema exists, so return values are covered. Description adequately explains what is returned, but missing parameter details slightly reduces completeness for parameter usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description does not mention the two parameters (lang, limit), leaving the agent to infer their meaning from names alone.

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?

Clearly states it returns breaking news from last 24 hours with importance score >= 75, and distinguishes itself from get_latest.

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

Usage Guidelines4/5

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

Provides clear context for when to use this tool vs get_latest (lower volume, market-moving items), but does not explicitly exclude other siblings like get_featured or search.

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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