USCardForum MCP Server

get_topic_posts

Fetch posts from a USCardForum topic in batches for paginated reading, starting at a specified position with optional markdown source.

Instructions

Fetch a batch of posts from a topic starting at a specific position.

Args:
    topic_id: The numeric topic ID
    post_number: Which post number to start from (default: 1 = first post)
    include_raw: Include raw markdown source (default: False, returns HTML)

This fetches ~20 posts per call starting from post_number.
Use for paginated reading of topics.

Returns a list of Post objects with:
- post_number: Position in topic (1, 2, 3...)
- username: Author's username
- cooked: HTML content of the post
- raw: Markdown source (if include_raw=True)
- created_at: When posted
- updated_at: Last edit time
- like_count: Number of likes
- reply_count: Number of direct replies
- reply_to_post_number: Which post this replies to (if any)

Pagination example:
1. Call with post_number=1, get posts 1-20
2. Call with post_number=21, get posts 21-40
3. Continue until no posts returned

Input Schema

TableJSON Schema

Name	Required	Description
`topic_id`	Yes	The numeric topic ID
`post_number`	No	Which post number to start from (default: 1 = first post)
`include_raw`	No	Include raw markdown source (default: False, returns HTML)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedDec 30, 2025

TDQS

A4.4/5.0

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 key behavioral traits: it specifies the batch size (~20 posts per call), pagination logic (starting from post_number), and return format (list of Post objects with detailed fields). It also clarifies default behaviors and the effect of include_raw. No contradictions exist, and it covers most essential operational aspects.

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, parameters, returns, pagination example) and front-loads the core functionality. Every sentence earns its place by providing essential information, though the parameter section duplicates schema content unnecessarily. It could be more concise by omitting the redundant parameter descriptions.

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 (paginated fetching), no annotations, and the presence of an output schema (implied by the detailed return description), the description is complete. It covers purpose, usage, parameters, return format, and pagination behavior thoroughly. The output schema equivalent in the description eliminates any need for additional return value explanation.

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 three parameters thoroughly. The description repeats the parameter explanations verbatim in the 'Args' section, adding no additional semantic value beyond what's in the schema. However, it does provide context about how parameters interact (e.g., post_number as starting point for pagination), which slightly elevates it above baseline.

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 specific action ('fetch a batch of posts from a topic') and resource ('posts'), distinguishing it from siblings like 'get_all_topic_posts' (which presumably fetches all posts at once) and 'get_topic_info' (which likely fetches metadata). The verb 'fetch' combined with the pagination context makes the purpose unambiguous.

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 explicitly states when to use this tool ('use for paginated reading of topics') and provides a detailed pagination example with steps. It implicitly distinguishes from 'get_all_topic_posts' by emphasizing batch fetching with a starting position, though it doesn't name alternatives directly. The guidance on pagination behavior is comprehensive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.