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GodisinHisHeaven

USCardForum MCP Server

get_user_reactions

Fetch a user's post reactions (likes, etc.) to see what content they engage with, indicating their interests and values on the USCardForum community.

Instructions

Fetch a user's post reactions (likes, etc.).

Args:
    username: The user's handle
    offset: Pagination offset (optional)

Returns a UserReactions object with reaction data.

Use to see what content a user has reacted to,
which can indicate their interests and values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesThe user's handle
offsetNoPagination offset

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reactionsNoReaction data

Implementation Reference

  • The primary MCP tool handler for 'get_user_reactions'. Decorated with @mcp.tool(), defines input schema (username: Annotated[str], offset: Annotated[int|None]) and output type UserReactions. Delegates execution to get_client().get_user_reactions().
    @mcp.tool()
    def get_user_reactions(
        username: Annotated[
            str,
            Field(description="The user's handle"),
        ],
        offset: Annotated[
            int | None,
            Field(default=None, description="Pagination offset"),
        ] = None,
    ) -> UserReactions:
        """
        Fetch a user's post reactions (likes, etc.).
    
        Args:
            username: The user's handle
            offset: Pagination offset (optional)
    
        Returns a UserReactions object with reaction data.
    
        Use to see what content a user has reacted to,
        which can indicate their interests and values.
        """
        return get_client().get_user_reactions(username, offset=offset)
  • Registration via import into MCP server entrypoint (server.py). The import from server_tools registers all @mcp.tool() functions, including get_user_reactions (line 33).
    from uscardforum.server_tools import (
        analyze_user,
        bookmark_post,
        compare_cards,
        find_data_points,
        get_all_topic_posts,
        get_categories,
        get_current_session,
        get_hot_topics,
        get_new_topics,
        get_notifications,
        get_top_topics,
        get_topic_info,
        get_topic_posts,
        get_user_actions,
        get_user_badges,
        get_user_followers,
        get_user_following,
        get_user_reactions,
        get_user_replies,
        get_user_summary,
        get_user_topics,
        list_users_with_badge,
        login,
        research_topic,
        resource_categories,
        resource_hot_topics,
        resource_new_topics,
        search_forum,
        subscribe_topic,
    )
  • Package-level re-export: imports get_user_reactions from .users (line 45) and includes in __all__ (line 86), facilitating registration.
    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,
    )
  • Underlying HTTP API implementation in UsersAPI.get_user_reactions(). Performs GET request to '/discourse-reactions/posts/reactions.json', parses response into UserReactions model.
    def get_user_reactions(
        self,
        username: str,
        offset: int | None = None,
    ) -> UserReactions:
        """Fetch user's post reactions.
    
        Args:
            username: User handle
            offset: Optional pagination offset
    
        Returns:
            User reactions data
        """
        params_list: list[tuple[str, Any]] = [("username", username)]
        if offset is not None:
            params_list.append(("offset", int(offset)))
    
        payload = self._get(
            "/discourse-reactions/posts/reactions.json",
            params=params_list,
        )
        return UserReactions(reactions=payload.get("reactions", []))
  • Client-side wrapper in DiscourseClient.get_user_reactions(), which delegates to the UsersAPI instance (_users.get_user_reactions()). Called by the MCP handler.
    def get_user_reactions(
        self,
        username: str,
        offset: int | None = None,
    ) -> UserReactions:
        """Fetch user's post reactions.
    
        Args:
            username: User handle
            offset: Optional pagination offset
    
        Returns:
            User reactions data
        """
        return self._users.get_user_reactions(username, offset=offset)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions pagination ('offset: Pagination offset') and return type ('Returns a UserReactions object'), but lacks critical behavioral details: authentication requirements, rate limits, whether it's read-only (implied but not stated), error conditions, or what happens with invalid usernames. For a tool with no annotation coverage, this is a significant gap.

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 and appropriately sized: purpose statement, parameter documentation, return value, and usage context in four concise sentences. It's front-loaded with the core functionality. Minor redundancy in parameter descriptions slightly reduces efficiency, but overall it's economical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (implied by 'Returns a UserReactions object'), the description doesn't need to detail return values. With 100% schema coverage and clear purpose, it's mostly complete for a read operation. However, the lack of behavioral transparency (auth, errors, limits) for a tool with no annotations prevents a perfect score.

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 thoroughly. The description adds minimal value beyond the schema: it repeats the parameter descriptions almost verbatim ('username: The user's handle', 'offset: Pagination offset') and doesn't provide additional context about username format, offset units, or pagination behavior. 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Fetch a user's post reactions (likes, etc.)' - a specific verb ('fetch') and resource ('user's post reactions'). It distinguishes from siblings like 'get_user_badges' or 'get_user_summary' by focusing specifically on reactions, though it doesn't explicitly contrast with similar tools like 'get_user_actions' which might overlap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context: 'Use to see what content a user has reacted to, which can indicate their interests and values.' This implies when to use it (for interest/value analysis), but doesn't explicitly state when NOT to use it or mention alternatives like 'get_user_actions' or 'get_user_summary' that might provide related information. The guidance is helpful but incomplete.

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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