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GodisinHisHeaven

USCardForum MCP Server

get_user_topics

Fetch topics created by a specific user on USCardForum to analyze their discussion history, identify expertise areas, and research user interests.

Instructions

Fetch topics created by a specific user.

Args:
    username: The user's handle
    page: Page number for pagination (optional)

Returns a list of topic objects with:
- id: Topic ID
- title: Topic title
- posts_count: Number of replies
- views: View count
- created_at: When created
- category_id: Forum category

Use this to:
- See what discussions a user has initiated
- Find expert users in specific areas
- Research a user's areas of interest

Paginate by incrementing the page parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesThe user's handle
pageNoPage number for pagination

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'get_user_topics'. Defines input schema (username: str, page: int|None), output as list[dict], docstring with details. Delegates execution to DiscourseClient.get_user_topics.
    @mcp.tool()
    def get_user_topics(
        username: Annotated[
            str,
            Field(description="The user's handle"),
        ],
        page: Annotated[
            int | None,
            Field(default=None, description="Page number for pagination"),
        ] = None,
    ) -> list[dict[str, Any]]:
        """
        Fetch topics created by a specific user.
    
        Args:
            username: The user's handle
            page: Page number for pagination (optional)
    
        Returns a list of topic objects with:
        - id: Topic ID
        - title: Topic title
        - posts_count: Number of replies
        - views: View count
        - created_at: When created
        - category_id: Forum category
    
        Use this to:
        - See what discussions a user has initiated
        - Find expert users in specific areas
        - Research a user's areas of interest
    
        Paginate by incrementing the page parameter.
        """
        return get_client().get_user_topics(username, page=page)
  • Input/output schema defined via Pydantic Annotated Fields and return type annotation. Docstring specifies expected output fields: id, title, posts_count, views, created_at, category_id.
    @mcp.tool()
    def get_user_topics(
        username: Annotated[
            str,
            Field(description="The user's handle"),
        ],
        page: Annotated[
            int | None,
            Field(default=None, description="Page number for pagination"),
        ] = None,
    ) -> list[dict[str, Any]]:
        """
        Fetch topics created by a specific user.
    
        Args:
            username: The user's handle
            page: Page number for pagination (optional)
    
        Returns a list of topic objects with:
        - id: Topic ID
        - title: Topic title
        - posts_count: Number of replies
        - views: View count
        - created_at: When created
        - category_id: Forum category
    
        Use this to:
        - See what discussions a user has initiated
        - Find expert users in specific areas
        - Research a user's areas of interest
    
        Paginate by incrementing the page parameter.
        """
        return get_client().get_user_topics(username, page=page)
  • Core API implementation in UsersAPI: performs HTTP GET to /topics/created-by/{username}.json with optional page param, extracts topics from payload.
    def get_user_topics(
        self,
        username: str,
        page: int | None = None,
    ) -> list[dict[str, Any]]:
        """Fetch topics created by user.
    
        Args:
            username: User handle
            page: Optional page number
    
        Returns:
            List of topic objects (raw API format)
        """
        params_list: list[tuple[str, Any]] = []
        if page is not None:
            params_list.append(("page", int(page)))
    
        payload = self._get(
            f"/topics/created-by/{username}.json",
            params=params_list,
        )
        return payload.get("topic_list", {}).get("topics", [])
  • DiscourseClient wrapper: calls UsersAPI.get_user_topics and enriches results with category names using _enrich_with_categories.
    def get_user_topics(
        self,
        username: str,
        page: int | None = None,
    ) -> list[dict[str, Any]]:
        """Fetch topics created by user.
    
        Args:
            username: User handle
            page: Optional page number
    
        Returns:
            List of topic objects
        """
        topics = self._users.get_user_topics(username, page=page)
        return self._enrich_with_categories(topics)
  • Re-exports get_user_topics from users.py as part of server_tools public API, making it available for import in server.py.
    from .users import (
        get_user_summary,
        get_user_topics,
        get_user_replies,
        get_user_actions,
        get_user_badges,
        get_user_following,
        get_user_followers,
        get_user_reactions,
        list_users_with_badge,
    )
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 return format (list of topic objects with specific fields) and pagination behavior. It doesn't mention rate limits, authentication needs, or error conditions, 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?

Well-structured with clear sections: purpose statement, args explanation, return format, and use cases. Every sentence adds value with zero waste. The information is front-loaded with the core purpose stated first.

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 query tool with 100% schema coverage and an output schema (implied by 'Returns a list of topic objects'), the description provides complete context. It explains purpose, usage, parameters, return format, and practical applications without gaps.

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 documents both parameters fully. The description repeats the parameter explanations but doesn't add meaningful semantic context beyond what's in the schema. 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 verb 'fetch' and resource 'topics created by a specific user', distinguishing it from siblings like get_user_replies or get_user_summary. It specifies that it retrieves user-initiated discussions rather than replies or other user data.

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 use cases ('See what discussions a user has initiated', 'Find expert users', 'Research interests') and distinguishes from alternatives by focusing on user-created topics only. It also explains pagination usage clearly.

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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