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GodisinHisHeaven

USCardForum MCP Server

get_user_actions

Retrieve a user's activity feed from USCardForum with filtering options for likes, topics, replies, and mentions to analyze participation patterns.

Instructions

Fetch a user's activity feed with optional filtering.

Args:
    username: The user's handle
    filter: Action type filter (optional). Common values:
        - 1: Likes given
        - 2: Likes received
        - 4: Topics created
        - 5: Replies posted
        - 6: Posts (all)
        - 7: Mentions
    offset: Pagination offset (0, 30, 60, ...)

Returns a list of UserAction objects showing what the user has done.

Use this for detailed activity analysis beyond just replies.
For most cases, get_user_replies or get_user_topics are simpler.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesThe user's handle
filterNoAction type filter: 1=likes given, 2=likes received, 4=topics created, 5=replies posted, 6=all posts, 7=mentions
offsetNoPagination offset (0, 30, 60, ...)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The @mcp.tool()-decorated handler function implementing the core logic of the get_user_actions tool. Includes input schema definitions via Annotated and Pydantic Field, docstring, and delegation to the underlying client API.
    @mcp.tool()
    def get_user_actions(
        username: Annotated[
            str,
            Field(description="The user's handle"),
        ],
        filter: Annotated[
            int | None,
            Field(
                default=None,
                description="Action type filter: 1=likes given, 2=likes received, 4=topics created, 5=replies posted, 6=all posts, 7=mentions",
            ),
        ] = None,
        offset: Annotated[
            int | None,
            Field(default=None, description="Pagination offset (0, 30, 60, ...)"),
        ] = None,
    ) -> list[UserAction]:
        """
        Fetch a user's activity feed with optional filtering.
    
        Args:
            username: The user's handle
            filter: Action type filter (optional). Common values:
                - 1: Likes given
                - 2: Likes received
                - 4: Topics created
                - 5: Replies posted
                - 6: Posts (all)
                - 7: Mentions
            offset: Pagination offset (0, 30, 60, ...)
    
        Returns a list of UserAction objects showing what the user has done.
    
        Use this for detailed activity analysis beyond just replies.
        For most cases, get_user_replies or get_user_topics are simpler.
        """
        return get_client().get_user_actions(username, filter=filter, offset=offset)
  • Pydantic BaseModel defining the UserAction type, which is the return type (list[UserAction]) of the get_user_actions tool, providing output schema validation and structure.
    class UserAction(BaseModel):
        """A user activity entry (reply, like, etc.)."""
    
        action_type: int | None = Field(None, description="Type of action")
        topic_id: int | None = Field(None, description="Related topic ID")
        post_number: int | None = Field(None, description="Related post number")
        title: str | None = Field(None, description="Topic title")
        excerpt: str | None = Field(None, description="Content preview")
        created_at: datetime | None = Field(None, description="When action occurred")
        username: str | None = Field(None, description="Username who performed action")
        acting_username: str | None = Field(None, description="Acting user")
    
        class Config:
            extra = "ignore"
  • Import statement in server_tools/__init__.py that registers the get_user_actions tool by importing it into the package namespace, making it available for MCP auto-registration upon import.
    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,
    )
  • Import of all tools including get_user_actions in the main server.py entrypoint file, ensuring the tool is registered in the MCP server context.
        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,
    )
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 fetches an activity feed with optional filtering, returns a list of UserAction objects, and mentions pagination via offset. It doesn't cover potential rate limits, authentication needs, or error conditions, but provides solid operational context.

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 and appropriately sized. It starts with a clear purpose statement, provides parameter details in a readable format, explains the return value, and ends with usage guidance. Every sentence adds value with zero waste.

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, 100% schema coverage, and the presence of an output schema (implied by 'Returns a list of UserAction objects'), the description is complete enough. It covers purpose, parameters, return values, and usage guidelines without needing to duplicate schema information.

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 all parameters thoroughly. The description adds minimal value beyond the schema: it provides the same filter mapping as the schema and repeats the offset explanation. Baseline 3 is appropriate when the 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 tool's purpose with a specific verb ('fetch') and resource ('user's activity feed'), and distinguishes it from siblings by mentioning it's for 'detailed activity analysis beyond just replies' and contrasting with 'get_user_replies' and 'get_user_topics' as simpler alternatives.

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 vs. alternatives: 'Use this for detailed activity analysis beyond just replies. For most cases, get_user_replies or get_user_topics are simpler.' This clearly defines the context and names specific sibling tools as simpler alternatives.

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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