Skip to main content
Glama
GodisinHisHeaven

USCardForum MCP Server

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 get_client().get_hot_topics(page=page) to fetch trending topics.
    @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 defining the output schema for get_hot_topics: TopicSummary with fields like id, title, posts_count, etc.
    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"
  • Re-export of get_hot_topics from server_tools/topics.py for use in the MCP server.
    from .topics import get_hot_topics, get_new_topics, get_top_topics
  • Shared helper function get_client() that provides the DiscourseClient instance used by the tool handler.
    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"))
            _client = DiscourseClient(base_url=base_url, timeout_seconds=timeout)
    
            # Auto-login if credentials are provided
            if not _login_attempted:
                _login_attempted = True
                username = os.environ.get("NITAN_USERNAME")
                password = os.environ.get("NITAN_PASSWORD")
    
                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
  • Client method get_hot_topics on DiscourseClient that delegates to TopicsAPI and enriches with categories.
    def get_hot_topics(self, *, page: int | None = None) -> list[TopicSummary]:
        """Fetch currently hot/trending topics.
    
        Args:
            page: Page number for pagination (0-indexed, default: 0)
    
        Returns:
            List of hot topic summaries
        """
        topics = self._topics.get_hot_topics(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 full burden and does well by explaining ranking methodology ('engagement metrics like recent replies, views, and likes'), pagination behavior, and response interpretation guidance. It doesn't mention rate limits or authentication requirements, but provides substantial behavioral context beyond basic functionality.

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 statement, usage guidelines, parameters, return format, and interpretation examples. Every sentence adds value, there's no redundancy, and information is front-loaded with the core purpose stated immediately.

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?

For a read-only tool with one parameter and an output schema, the description is complete. It explains what the tool does, when to use it, how results are ranked, includes parameter guidance, documents the return structure, and provides interpretation examples - covering all necessary context despite having no annotations.

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% with the single parameter 'page' fully documented in the schema. The description adds minimal value beyond the schema by mentioning 'Use page=1 to get more topics' which slightly clarifies usage but doesn't add significant semantic meaning. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('fetch trending/hot topics'), identifies the resource ('from USCardForum'), and distinguishes it from siblings by specifying it returns 'most actively discussed topics right now, ranked by engagement metrics' - differentiating it from tools like get_new_topics, get_top_topics, or get_categories.

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 provides explicit usage scenarios with three bullet points explaining when to use this tool ('See what the community is currently discussing', 'Find breaking news or time-sensitive opportunities', 'Discover popular ongoing discussions'), giving clear context for when this tool is appropriate versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/GodisinHisHeaven/uscardforum-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server