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GregBaugues

Token Bowl MCP Server

by GregBaugues

token_bowl_chat_get_online_users

List users currently connected to Token Bowl Chat to see who is online and available for real-time chat. Returns usernames, logos, emojis, bot and viewer status.

Instructions

Get a list of users currently connected to Token Bowl Chat.

Use this to see who is actively online and available for real-time chat.

Returns: List of currently connected user objects with: - username: User's username - logo: Profile logo filename (if set) - emoji: Profile emoji (if set) - bot: Whether the account is a bot - viewer: Whether the account is view-only

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It implies a read-only operation ('Get') and clarifies what 'online' means ('currently connected', 'actively online and available for real-time chat'), which adds some context. Yet it omits any mention of authentication requirements, rate limits, caching, or ordering of results. For a no-annotation read tool, this is minimal but accurate.

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 front-loaded with purpose and usage in the first two sentences, followed by a structured return field list. It is appropriately sized for a simple tool. The returns list partially duplicates the output schema, but it is clearly organized and not excessively verbose.

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 the tool's simplicity (no parameters, output schema present), the description covers purpose, usage, and return shape. It does not explicitly distinguish the tool from its closest sibling (get_users), but that is a minor gap. Overall, the definition is complete enough for an agent to call it correctly.

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?

The tool takes zero parameters, and the schema description coverage is 100%. Per the scoring rules, zero parameters yield a baseline of 4. The description does not need to explain any parameter meaning, and it correctly does not.

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?

States a specific verb (Get), resource (list of users), and scope (currently connected to Token Bowl Chat). The scope 'currently connected' distinguishes it from sibling token_bowl_chat_get_users, which likely lists all users. An agent can identify the exact resource without opening the schema.

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

Usage Guidelines4/5

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

Provides clear context: 'Use this to see who is actively online and available for real-time chat.' This tells the agent when the tool is appropriate. However, it does not explicitly name alternatives or exclusions (e.g., how it differs from get_users or get_user_profile), so it falls short of full when/when-not guidance.

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