get_forum_threadOne request from the public forum by id: title, body, status, the developer's written answer if there is one, and the heart counts (hearts count COMPANIES — a twelve-person crew and a solo shop each get one effective vote).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The thread id from a forum URL or search_forum result |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, and the description adds useful detail beyond that: it specifies what fields are returned, notes the answer is included only 'if there is one,' and clarifies the important nuance that heart counts are per company, not per person. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states what the tool does, then lists the return fields, and ends with a compactly worded but valuable clarification about heart counts. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one parameter and no output schema, the description adequately covers what will be returned: title, body, status, developer answer, and heart counts with their interpretation. No critical information is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the only parameter 'id' with its description, and the tool description adds no additional parameter-specific semantics. Baseline 3 is appropriate because the schema carries the parameter burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The title 'Read one forum request' and the description 'One request from the public forum by id' clearly identify the action and resource. The singular 'by id' distinguishes it from search_forum and other forum tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear this tool is for fetching a single forum request when you have its id. It does not explicitly name alternatives or say when not to use it, but the by-id framing plus the sibling search_forum makes the intended context obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct actions, but get_product_overview, search_knowledge_base, and server_info all overlap on product-capability questions, and search_forum overlaps with get_roadmap for status discovery. The descriptions help direct usage, but ambiguity remains in a few pairs.
Nearly all tools follow a clean snake_case verb_noun pattern: get_pricing, heart_request, post_feature_request, search_forum. Minor deviations like server_info and my_requests are noun-phrase names, but they do not disrupt the overall readability.
Thirteen tools is well within the ideal range, and each tool earns its place. The set covers product info, pricing, forum engagement, knowledge base, waitlist, and contact without feeling padded.
The toolset covers the full customer-facing lifecycle: discover the product, check pricing, search and read forum threads, post requests, comment, heart, track personal requests, join the waitlist, and contact a human. There are no obvious dead ends for the stated purpose.