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raidenrock

USCardForum MCP Server

by raidenrock

get_hot_topics

Fetch trending topics from USCardForum to discover current community discussions, breaking news, and popular conversations ranked by engagement metrics.

Instructions

Fetch currently trending/hot topics from USCardForum.

This returns the most actively discussed topics right now, ranked by
engagement metrics like recent replies, views, and likes.

Use this to:
- See what the community is currently discussing
- Find breaking news or time-sensitive opportunities
- Discover popular ongoing discussions

Args:
    page: Page number for pagination (0-indexed). Use page=1 to get more topics.

Returns a list of TopicSummary objects with fields:
- id: Topic ID (use with get_topic_posts)
- title: Topic title
- posts_count: Total replies
- views: View count
- like_count: Total likes
- created_at: Creation timestamp
- last_posted_at: Last activity timestamp

Example response interpretation:
A topic with high views but low posts may be informational.
A topic with many recent posts is actively being discussed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination (0-indexed, default: 0)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for get_hot_topics: decorated with @mcp.tool(), calls the client API with optional page parameter, returns list of TopicSummary.
    @mcp.tool()
    def get_hot_topics(
        page: Annotated[
            int | None,
            Field(default=None, description="Page number for pagination (0-indexed, default: 0)"),
        ] = None,
    ) -> list[TopicSummary]:
        """
        Fetch currently trending/hot topics from USCardForum.
    
        This returns the most actively discussed topics right now, ranked by
        engagement metrics like recent replies, views, and likes.
    
        Use this to:
        - See what the community is currently discussing
        - Find breaking news or time-sensitive opportunities
        - Discover popular ongoing discussions
    
        Args:
            page: Page number for pagination (0-indexed). Use page=1 to get more topics.
    
        Returns a list of TopicSummary objects with fields:
        - id: Topic ID (use with get_topic_posts)
        - title: Topic title
        - posts_count: Total replies
        - views: View count
        - like_count: Total likes
        - created_at: Creation timestamp
        - last_posted_at: Last activity timestamp
    
        Example response interpretation:
        A topic with high views but low posts may be informational.
        A topic with many recent posts is actively being discussed.
        """
        return get_client().get_hot_topics(page=page)
  • Pydantic model TopicSummary used as return type for get_hot_topics, defining the structure of each topic summary.
    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"
  • Client-side API implementation in TopicsAPI.get_hot_topics: makes HTTP GET to /hot.json, parses JSON, returns list of TopicSummary.
    def get_hot_topics(self, *, page: int | None = None) -> list[TopicSummary]:
        """Fetch currently trending topics.
    
        Args:
            page: Page number for pagination (0-indexed, default: 0)
    
        Returns:
            List of hot topic summaries
        """
        params: dict[str, Any] = {}
        if page is not None:
            params["page"] = int(page)
    
        payload = self._get(
            "/hot.json",
            params=params or None,
            headers={"Accept": "application/json, text/plain, */*"},
        )
        topics = payload.get("topic_list", {}).get("topics", [])
        return [TopicSummary(**t) for t in topics]
  • get_client() helper: creates and returns the shared DiscourseClient instance, handles auto-login if credentials provided.
    def get_client() -> DiscourseClient:
        """Get or create the Discourse client instance."""
        global _client, _login_attempted
    
        if _client is None:
            base_url = os.environ.get("USCARDFORUM_URL", "https://www.uscardforum.com")
            timeout = float(os.environ.get("USCARDFORUM_TIMEOUT", "15.0"))
    
            username = os.environ.get("NITAN_USERNAME")
            password = os.environ.get("NITAN_PASSWORD")
            user_api_key = os.environ.get("NITAN_API_KEY")
            user_api_client_id = os.environ.get("NITAN_API_CLIENT_ID")
    
            _client = DiscourseClient(
                base_url=base_url,
                timeout_seconds=timeout,
                user_api_key=user_api_key if not (username and password) else None,
                user_api_client_id=(
                    user_api_client_id if not (username and password) else None
                ),
            )
    
            if _client.is_authenticated:
                print("[uscardforum] Using User API Key authentication")
            elif not _login_attempted:
                _login_attempted = True
    
                if username and password:
                    try:
                        result = _client.login(username, password)
                        if result.success:
                            print(f"[uscardforum] Auto-login successful as '{result.username}'")
                        elif result.requires_2fa:
                            print(
                                "[uscardforum] Auto-login failed: 2FA required. Use login() tool with second_factor_token."
                            )
                        else:
                            print(
                                f"[uscardforum] Auto-login failed: {result.error or 'Unknown error'}"
                            )
                    except Exception as e:  # pragma: no cover - logging side effect
                        print(f"[uscardforum] Auto-login error: {e}")
    
        return _client
  • Import of get_hot_topics from server_tools/topics.py in server_tools __init__.py, which triggers registration via @mcp.tool() decorator when imported by server.py.
    from .topics import (
        get_all_topic_posts,
        get_hot_topics,
        get_new_topics,
        get_top_topics,
        get_topic_info,
        get_topic_posts,
    )
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by explaining the ranking logic ('engagement metrics like recent replies, views, and likes'), pagination behavior, and response interpretation. It doesn't mention rate limits or authentication needs, but covers core behavioral aspects adequately.

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, usage guidelines, parameters, returns, examples), front-loaded with the core purpose, and every sentence adds value without redundancy. It's appropriately sized for the tool's complexity.

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, no annotations, but with output schema (implied by 'Returns a list of TopicSummary objects'), the description provides complete context: clear purpose, usage guidelines, parameter explanation, return format details, and practical interpretation examples, covering all necessary aspects.

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 baseline is 3. The description adds minimal value beyond the schema by clarifying 'Use page=1 to get more topics', but doesn't provide additional semantic context about parameter behavior or constraints.

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 verbs ('fetch trending/hot topics') and resource ('from USCardForum'), distinguishing it from siblings like get_top_topics, get_new_topics, or get_categories by specifying it's based on current engagement 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 ('See what the community is currently discussing', 'Find breaking news', 'Discover popular ongoing discussions') and distinguishes it from alternatives by noting the returned data can be used with get_topic_posts, offering clear guidance on when to use this tool.

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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