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hmumixaM

USCardForum MCP Server

by hmumixaM

get_top_topics

Retrieve top-performing forum discussions for specified time periods to identify trending or historically valuable 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

  • MCP tool handler for 'get_top_topics'. Defines input schema with Pydantic Annotated Fields and comprehensive docstring. Executes core logic by delegating to the DiscourseClient implementation.
    @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 output structure for each topic in the list returned by get_top_topics.
    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"
  • Explicit import of the get_top_topics tool from topics.py into the server_tools __init__, making it available for re-export and MCP server registration.
    from .topics import get_hot_topics, get_new_topics, get_top_topics
    from .search import search_forum
  • Low-level API implementation in TopicsAPI that makes HTTP request to /top.json and parses JSON 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(list(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 that calls the API layer and enriches results 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)
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool returns sorted results ('sorted by engagement score'), supports pagination ('Page number for pagination'), and returns structured data ('Returns TopicSummary objects'). However, it doesn't mention potential limitations like rate limits, authentication requirements, or data freshness.

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 (Args, Use this to, Returns, Example), uses bullet points efficiently, and every sentence adds value without redundancy. It's appropriately sized for a tool with 2 parameters and good behavioral context.

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 (2 parameters, 100% schema coverage, output schema exists), the description is complete. It covers purpose, usage guidelines, parameter semantics, return values, and includes examples. The existence of an output schema means the description doesn't need to detail return value structure, which it appropriately avoids.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the semantic meaning of period options (e.g., 'daily': Top topics from today, 'weekly': Top topics this week) and providing context for the page parameter ('Use page=1 to get more topics'), which goes beyond the schema's technical documentation.

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 specifying ranking based on performance over time periods rather than recency or popularity metrics.

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?

The description explicitly provides usage scenarios ('Find the most valuable discussions', 'Research historically important threads', 'Identify evergreen popular content') and includes an example that distinguishes when to use different period values ('Use "yearly" to find the most impactful discussions, or "daily" to see what's trending today'), offering clear guidance on when to use this tool versus alternatives.

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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