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USCardForum MCP Server

by raidenrock

get_topic_info

Retrieve topic metadata to plan pagination before reading posts, including title, post count, and timestamps from USCardForum discussions.

Instructions

Get metadata about a specific topic without fetching all posts.

Args:
    topic_id: The numeric topic ID (from URLs like /t/slug/12345)

Use this FIRST before reading a topic to:
- Check how many posts it contains (for pagination planning)
- Get the topic title and timestamps
- Decide whether to fetch all posts or paginate

Returns a TopicInfo object with:
- topic_id: The topic ID
- title: Full topic title
- post_count: Total number of posts
- highest_post_number: Last post number (may differ from count if posts deleted)
- last_posted_at: When the last reply was made

Strategy for large topics:
- <50 posts: Safe to fetch all at once
- 50-200 posts: Consider using max_posts parameter
- >200 posts: Fetch in batches or summarize key posts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topic_idYesThe numeric topic ID (from URLs like /t/slug/12345)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoTopic title
topic_idYesTopic identifier
post_countNoTotal number of posts
last_posted_atNoLast activity time
highest_post_numberNoHighest post number

Implementation Reference

  • The main MCP tool handler for 'get_topic_info'. Uses @mcp.tool() decorator to register the function, which delegates to the DiscourseClient via get_client().
    def get_topic_info(
        topic_id: Annotated[
            int,
            Field(description="The numeric topic ID (from URLs like /t/slug/12345)"),
        ],
    ) -> TopicInfo:
        """
        Get metadata about a specific topic without fetching all posts.
    
        Args:
            topic_id: The numeric topic ID (from URLs like /t/slug/12345)
    
        Use this FIRST before reading a topic to:
        - Check how many posts it contains (for pagination planning)
        - Get the topic title and timestamps
        - Decide whether to fetch all posts or paginate
    
        Returns a TopicInfo object with:
        - topic_id: The topic ID
        - title: Full topic title
        - post_count: Total number of posts
        - highest_post_number: Last post number (may differ from count if posts deleted)
        - last_posted_at: When the last reply was made
    
        Strategy for large topics:
        - <50 posts: Safe to fetch all at once
        - 50-200 posts: Consider using max_posts parameter
        - >200 posts: Fetch in batches or summarize key posts
        """
        return get_client().get_topic_info(topic_id)
  • Pydantic model defining the output schema for get_topic_info tool (TopicInfo). The input schema is defined inline via Annotated Field in the handler.
    class TopicInfo(BaseModel):
        """Detailed topic metadata."""
    
        topic_id: int = Field(..., description="Topic identifier")
        title: str | None = Field(None, description="Topic title")
        post_count: int = Field(0, description="Total number of posts")
        highest_post_number: int = Field(0, description="Highest post number")
        last_posted_at: datetime | None = Field(None, description="Last activity time")
    
        class Config:
            extra = "ignore"
  • Registration/export of the get_topic_info tool by importing from topics.py submodule. These are then re-exported in __all__ and imported into the main MCP server.py.
    from .topics import (
        get_all_topic_posts,
        get_hot_topics,
        get_new_topics,
        get_top_topics,
        get_topic_info,
        get_topic_posts,
    )
  • Core API implementation that fetches topic metadata from Discourse JSON endpoint /t/{id}.json. Called by client wrapper and ultimately by the tool handler.
    def get_topic_info(self, topic_id: int) -> TopicInfo:
        """Fetch topic metadata.
    
        Args:
            topic_id: Topic ID
    
        Returns:
            Topic info with post count, title, timestamps
        """
        payload = self._get(f"/t/{int(topic_id)}.json")
        return TopicInfo(
            topic_id=topic_id,
            title=payload.get("title"),
            post_count=payload.get("posts_count", 0),
            highest_post_number=payload.get("highest_post_number", 0),
            last_posted_at=payload.get("last_posted_at"),
        )
  • Client wrapper method that delegates to TopicsAPI.get_topic_info. Invoked by server_tools get_client().
    def get_topic_info(self, topic_id: int) -> TopicInfo:
        """Fetch topic metadata.
    
        Args:
            topic_id: Topic ID
    
        Returns:
            Topic info with post count, title, timestamps
        """
        return self._topics.get_topic_info(topic_id)
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 the tool's behavior: it's a read-only operation (implied by 'Get metadata'), returns a TopicInfo object with specific fields, and includes practical advice like handling large topics with batch fetching. However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, usage, returns, strategy) and is appropriately sized. However, the 'Args' section is redundant with the schema, and the strategy section, while helpful, could be more concise. Overall, it's efficient but has minor verbosity.

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 complexity (simple read operation), 100% schema coverage, and the presence of an output schema (implied by the 'Returns' section), the description is complete. It covers purpose, usage, parameters, return values, and strategic advice, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.

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?

The schema description coverage is 100%, so the input schema already documents the topic_id parameter. The description repeats the same information in the 'Args' section without adding new semantics beyond what's in the schema. This meets the baseline of 3, as the schema does the heavy lifting, but no extra value is provided.

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: 'Get metadata about a specific topic without fetching all posts.' It specifies the verb ('Get metadata') and resource ('a specific topic'), and distinguishes it from sibling tools like get_all_topic_posts and get_topic_posts by emphasizing it doesn't fetch posts, focusing only on metadata.

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 guidance on when to use this tool: 'Use this FIRST before reading a topic to...' and includes a 'Strategy for large topics' section with thresholds (e.g., <50 posts: safe to fetch all at once). It implicitly distinguishes from alternatives like get_all_topic_posts by advising on pagination planning.

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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