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hmumixaM

USCardForum MCP Server

by hmumixaM

get_all_topic_posts

Fetch all posts from a USCardForum topic with automatic pagination, allowing control over post ranges and limits for efficient data retrieval.

Instructions

Fetch all posts from a topic with automatic pagination.

Args:
    topic_id: The numeric topic ID
    include_raw: Include markdown source (default: False)
    start_post_number: First post to fetch (default: 1)
    end_post_number: Last post to fetch (optional, fetches to end if not set)
    max_posts: Maximum number of posts to return (optional safety limit)

This automatically handles pagination to fetch multiple batches.

IMPORTANT: For topics with many posts (>100), use max_posts to limit
the response size. You can always fetch more with start_post_number.

Use cases:
- Fetch entire small topic: get_all_topic_posts(topic_id=123)
- Fetch first 50 posts: get_all_topic_posts(topic_id=123, max_posts=50)
- Fetch posts 51-100: get_all_topic_posts(topic_id=123, start_post_number=51, max_posts=50)
- Fetch specific range: get_all_topic_posts(topic_id=123, start=10, end=30)

Returns the same Post structure as get_topic_posts but for all matching posts.

Pro tip: Use get_topic_info first to check post_count before deciding
whether to fetch all or paginate manually.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topic_idYesThe numeric topic ID
include_rawNoInclude markdown source (default: False)
start_post_numberNoFirst post to fetch (default: 1)
end_post_numberNoLast post to fetch (optional, fetches to end if not set)
max_postsNoMaximum number of posts to return (optional safety limit)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'get_all_topic_posts'. Defines input schema with Annotated Fields and delegates execution to the DiscourseClient via get_client().
    @mcp.tool()
    def get_all_topic_posts(
        topic_id: Annotated[
            int,
            Field(description="The numeric topic ID"),
        ],
        include_raw: Annotated[
            bool,
            Field(default=False, description="Include markdown source (default: False)"),
        ] = False,
        start_post_number: Annotated[
            int,
            Field(default=1, description="First post to fetch (default: 1)"),
        ] = 1,
        end_post_number: Annotated[
            int | None,
            Field(default=None, description="Last post to fetch (optional, fetches to end if not set)"),
        ] = None,
        max_posts: Annotated[
            int | None,
            Field(default=None, description="Maximum number of posts to return (optional safety limit)"),
        ] = None,
    ) -> list[Post]:
        """
        Fetch all posts from a topic with automatic pagination.
    
        Args:
            topic_id: The numeric topic ID
            include_raw: Include markdown source (default: False)
            start_post_number: First post to fetch (default: 1)
            end_post_number: Last post to fetch (optional, fetches to end if not set)
            max_posts: Maximum number of posts to return (optional safety limit)
    
        This automatically handles pagination to fetch multiple batches.
    
        IMPORTANT: For topics with many posts (>100), use max_posts to limit
        the response size. You can always fetch more with start_post_number.
    
        Use cases:
        - Fetch entire small topic: get_all_topic_posts(topic_id=123)
        - Fetch first 50 posts: get_all_topic_posts(topic_id=123, max_posts=50)
        - Fetch posts 51-100: get_all_topic_posts(topic_id=123, start_post_number=51, max_posts=50)
        - Fetch specific range: get_all_topic_posts(topic_id=123, start=10, end=30)
    
        Returns the same Post structure as get_topic_posts but for all matching posts.
    
        Pro tip: Use get_topic_info first to check post_count before deciding
        whether to fetch all or paginate manually.
        """
        return get_client().get_all_topic_posts(
            topic_id,
            include_raw=include_raw,
            start_post_number=start_post_number,
            end_post_number=end_post_number,
            max_posts=max_posts,
        )
  • Imports the get_all_topic_posts tool (line 20) among other MCP tools from server_tools. Importing registers the tool via its @mcp.tool() decorator before running the MCP server.
    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,
    )
    
    __all__ = [
        "MCP_HOST",
        "MCP_PORT",
        "MCP_TRANSPORT",
        "NITAN_TOKEN",
        "SERVER_INSTRUCTIONS",
        "get_client",
        "main",
        "mcp",
        "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",
        "resource_categories",
        "resource_hot_topics",
        "resource_new_topics",
        "search_forum",
        "subscribe_topic",
        "research_topic",
    ]
  • Core pagination logic and API implementation called by the client method. Defines the parameter schema matching the tool and returns list[Post], providing the effective input/output structure.
    def get_all_topic_posts(
        self,
        topic_id: int,
        *,
        include_raw: bool = False,
        start_post_number: int = 1,
        end_post_number: int | None = None,
        max_posts: int | None = None,
    ) -> list[Post]:
        """Fetch all posts in a topic with automatic pagination.
    
        Args:
            topic_id: Topic ID
            include_raw: Include raw markdown (default: False)
            start_post_number: Starting post number (default: 1)
            end_post_number: Optional ending post number
            max_posts: Optional maximum posts to fetch
    
        Returns:
            List of all matching posts
        """
        current = max(1, int(start_post_number))
        collected: list[Post] = []
        seen_numbers: set[int] = set()
    
        while True:
            if max_posts is not None and len(collected) >= int(max_posts):
                break
    
            batch = self.get_topic_posts(
                topic_id, post_number=current, include_raw=include_raw
            )
            if not batch:
                break
    
            last_in_batch: int | None = None
            for post in batch:
                pn = post.post_number
                if pn not in seen_numbers:
                    if end_post_number is not None and pn > int(end_post_number):
                        last_in_batch = last_in_batch or pn
                        continue
                    seen_numbers.add(pn)
                    collected.append(post)
                    last_in_batch = pn
                    if max_posts is not None and len(collected) >= int(max_posts):
                        break
    
            if last_in_batch is None:
                break
            current = last_in_batch + 1
            if end_post_number is not None and current > int(end_post_number):
                break
    
        collected.sort(key=lambda p: p.post_number)
        return collected
  • Global helper function get_client() used by the tool handler to obtain the DiscourseClient instance for API calls.
    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
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 an excellent job disclosing behavioral traits. It explains the automatic pagination mechanism, provides safety guidance about response size limits for large topics, describes how parameters interact (start_post_number, end_post_number, max_posts), and references the return structure. The only minor gap is not explicitly mentioning whether this is a read-only operation, though 'fetch' implies it.

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 statement, parameter explanations, important notes, use cases, return information, and pro tip. While comprehensive, some redundancy exists (parameter descriptions partially repeat schema info). Every sentence adds value, but it could be slightly more concise by eliminating schema repetition.

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 (automatic pagination, multiple interacting parameters) and the presence of an output schema (mentioned in 'Returns the same Post structure as get_topic_posts'), the description is complete. It covers purpose, usage guidelines, parameter semantics, behavioral traits, and references sibling tools appropriately. No significant gaps remain for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema by explaining parameter interactions and practical usage: it shows how start_post_number, end_post_number, and max_posts work together, provides default behavior explanations, and gives concrete examples of parameter combinations in the use cases section. This goes well beyond what the schema descriptions provide.

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: 'Fetch all posts from a topic with automatic pagination.' It specifies the verb ('fetch'), resource ('posts from a topic'), and key behavioral trait ('automatic pagination'). It distinguishes from sibling 'get_topic_posts' by emphasizing comprehensive fetching with pagination handling.

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 versus alternatives. It includes an 'IMPORTANT' section advising to use max_posts for large topics, mentions using 'get_topic_info first to check post_count before deciding whether to fetch all or paginate manually,' and gives four concrete use cases with parameter examples. It clearly differentiates from 'get_topic_posts' by handling pagination automatically.

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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