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get_featured

Retrieve editor-picked feature stories for curated highlights, bypassing the full news feed. Choose language and limit results.

Instructions

Editor-picked feature stories (is_featured=TRUE). No time window. Use when the user wants curated highlights rather than the firehose.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen-US
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'get_featured'. Registered via @mcp.tool decorator, calls client.get_featured() and returns results.
    async def get_featured(
        lang: str = _DEFAULT_LANG,
        limit: int = _DEFAULT_LIMIT,
    ) -> dict[str, Any]:
        """Get editor-curated featured stories.
    
        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_featured(lang=lang, limit=limit)
  • Registration of the 'get_featured' tool with FastMCP, including description about editor-curated featured stories.
    @mcp.tool(
        name="get_featured",
        description=(
            "Editor-picked feature stories (is_featured=TRUE). No time "
            "window. Use when the user wants curated highlights rather "
            "than the firehose."
        ),
    )
  • ZippClient.get_featured() — the underlying API client method that calls GET /api/v1/news/featured with lang and limit params.
    async def get_featured(
        self,
        *,
        lang: str = "en-US",
        limit: int = 10,
    ) -> dict[str, Any]:
        return await self._get("/featured", params={"lang": lang, "limit": limit})
  • Documentation in __init__.py listing 'get_featured' as an available tool.
    * ``get_featured`` — editor-curated highlights
    * ``get_post`` — full detail by slug or numeric id
  • Parametrized test verifying 'get_featured' is registered in the MCP server's tool list.
    @pytest.mark.parametrize(
        "tool_name",
        [
            "search",
            "get_latest",
            "get_breaking",
            "get_featured",
            "get_post",
            "list_categories",
        ],
    )
    async def test_each_tool_registered(tool_name: str) -> None:
        from zipp_mcp.server import mcp
    
        tools = await mcp.list_tools()
        names = {t.name for t in tools}
        assert tool_name in names, f"tool {tool_name!r} missing from registry; got {names}"
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions 'no time window' and 'editor-picked', but does not explicitly state that it is read-only or disclose other behavioral traits like rate limits. However, the name and context imply a safe read operation.

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?

The description is only two sentences, front-loading the key purpose and usage condition. Every sentence adds value with no unnecessary words.

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

Completeness3/5

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

Given no annotations and 0% schema coverage, the description is short but covers purpose and usage. However, it misses parameter explanations and additional behavioral context, making it somewhat incomplete for an agent to fully understand invocation.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain the parameters (lang, limit). Basic parameters like lang and limit are not described, leaving the agent to infer their meaning from the names and defaults.

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?

The description clearly states the tool returns 'Editor-picked feature stories' with a condition 'is_featured=TRUE', and distinguishes it from other tools like get_breaking and get_latest by mentioning 'curated highlights rather than the firehose'.

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

Usage Guidelines5/5

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

Explicitly says 'Use when the user wants curated highlights rather than the firehose', providing clear context for when to choose this tool over siblings.

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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