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USCardForum MCP Server

by raidenrock

get_top_topics

Fetch top-performing topics from USCardForum for specific time periods to identify valuable discussions, research important threads, and discover popular content.

Instructions

Fetch top-performing topics for a specific time period.

Args:
    period: Time window for ranking. Must be one of:
        - "daily": Top topics from today
        - "weekly": Top topics this week
        - "monthly": Top topics this month (default)
        - "quarterly": Top topics this quarter
        - "yearly": Top topics this year
    page: Page number for pagination (0-indexed). Use page=1 to get more topics.

Use this to:
- Find the most valuable discussions in a time range
- Research historically important threads
- Identify evergreen popular content

Returns TopicSummary objects sorted by engagement score.

Example: Use "yearly" to find the most impactful discussions,
or "daily" to see what's trending today.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoTime window for ranking: 'daily', 'weekly', 'monthly' (default), 'quarterly', or 'yearly'monthly
pageNoPage number for pagination (0-indexed, default: 0)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for 'get_top_topics', decorated with @mcp.tool(). Defines input parameters with Pydantic Field descriptions and calls the underlying client to fetch top topics.
    @mcp.tool()
    def get_top_topics(
        period: Annotated[
            str,
            Field(
                default="monthly",
                description="Time window for ranking: 'daily', 'weekly', 'monthly' (default), 'quarterly', or 'yearly'",
            ),
        ] = "monthly",
        page: Annotated[
            int | None,
            Field(default=None, description="Page number for pagination (0-indexed, default: 0)"),
        ] = None,
    ) -> list[TopicSummary]:
        """
        Fetch top-performing topics for a specific time period.
    
        Args:
            period: Time window for ranking. Must be one of:
                - "daily": Top topics from today
                - "weekly": Top topics this week
                - "monthly": Top topics this month (default)
                - "quarterly": Top topics this quarter
                - "yearly": Top topics this year
            page: Page number for pagination (0-indexed). Use page=1 to get more topics.
    
        Use this to:
        - Find the most valuable discussions in a time range
        - Research historically important threads
        - Identify evergreen popular content
    
        Returns TopicSummary objects sorted by engagement score.
    
        Example: Use "yearly" to find the most impactful discussions,
        or "daily" to see what's trending today.
        """
        return get_client().get_top_topics(period=period, page=page)
  • Pydantic model defining the structure of each TopicSummary object returned by the get_top_topics tool (output schema).
    class TopicSummary(BaseModel):
        """Summary of a topic for list views (hot, new, top topics)."""
    
        id: int = Field(..., description="Unique topic identifier")
        title: str = Field(..., description="Topic title")
        posts_count: int = Field(0, description="Total number of posts")
        views: int = Field(0, description="Total view count")
        like_count: int = Field(0, description="Total likes on the topic")
        category_id: int | None = Field(None, description="Category identifier")
        category_name: str | None = Field(None, description="Category name")
        created_at: datetime | None = Field(None, description="When topic was created")
        last_posted_at: datetime | None = Field(None, description="Last activity time")
    
        class Config:
            extra = "ignore"
  • Import statement that brings get_top_topics into the server_tools namespace, triggering the @mcp.tool() decorator registration when the package is imported.
    from .topics import (
        get_all_topic_posts,
        get_hot_topics,
        get_new_topics,
        get_top_topics,
        get_topic_info,
        get_topic_posts,
    )
  • Underlying API implementation in TopicsAPI that makes the HTTP request to /top.json and parses the response into TopicSummary objects.
    def get_top_topics(
        self, period: str = "monthly", *, page: int | None = None
    ) -> list[TopicSummary]:
        """Fetch top topics for a time period.
    
        Args:
            period: One of 'daily', 'weekly', 'monthly', 'quarterly', 'yearly'
            page: Page number for pagination (0-indexed, default: 0)
    
        Returns:
            List of top topic summaries
        """
        allowed = {"daily", "weekly", "monthly", "quarterly", "yearly"}
        if period not in allowed:
            raise ValueError(f"period must be one of {sorted(allowed)}")
    
        params: dict[str, Any] = {"period": period}
        if page is not None:
            params["page"] = int(page)
    
        payload = self._get(
            "/top.json",
            params=params,
            headers={"Accept": "application/json, text/plain, */*"},
        )
        topics = payload.get("topic_list", {}).get("topics", [])
        return [TopicSummary(**t) for t in topics]
  • Client wrapper method that calls the API and enriches topics with category names.
    def get_top_topics(
        self, period: str = "monthly", *, page: int | None = None
    ) -> list[TopicSummary]:
        """Fetch top topics for a time period.
    
        Args:
            period: One of 'daily', 'weekly', 'monthly', 'quarterly', 'yearly'
            page: Page number for pagination (0-indexed, default: 0)
    
        Returns:
            List of top topic summaries
        """
        topics = self._topics.get_top_topics(period=period, page=page)
        return self._enrich_with_categories(topics)
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that results are sorted by engagement score and paginated, which are key behavioral traits. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps for a tool with no annotation coverage.

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 well-structured with clear sections (purpose, args, usage, returns, example), front-loaded key information, and every sentence adds value without redundancy. It's appropriately sized for a tool with two parameters and clear functionality.

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

Completeness5/5

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

Given the tool's moderate complexity, 100% schema coverage, and presence of an output schema (implied by 'Returns TopicSummary objects'), the description is complete enough. It covers purpose, parameters, usage contexts, return format, and provides examples, addressing all necessary aspects without needing to explain return values in detail.

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 the schema already fully documents parameters. The description adds minimal value by listing enum values for 'period' and explaining pagination, but doesn't provide additional semantics beyond what's in the schema, meeting the baseline for high coverage.

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's purpose with specific verb ('fetch') and resource ('top-performing topics'), and distinguishes it from siblings like 'get_hot_topics' or 'get_new_topics' by focusing on performance ranking over time periods rather than recency or popularity alone.

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?

The 'Use this to' section provides clear contexts for when to use the tool (e.g., finding valuable discussions, researching historical threads, identifying evergreen content), but it doesn't explicitly state when not to use it or name alternatives among siblings like 'get_hot_topics' for current trends.

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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