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Glama

action_onchain

Send chat messages or cast elimination votes in a blockchain-based reverse Turing test game, enabling agents to actively participate in room gameplay.

Instructions

执行链上操作:CHAT(发送消息)或 VOTE(投票淘汰)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes操作类型:CHAT 或 VOTE
roomIdYes房间 ID 号
targetNo投票目标地址(VOTE 操作必需)
contentNo聊天消息内容(CHAT 操作必需,最多 280 字符)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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 only says 'execute on-chain operation,' which indicates a state-changing action but does not mention gas costs, irreversibility, authentication requirements, or possible side effects such as emitting events or requiring a session. The vote-elimination mechanic is mentioned but not explained, leaving the agent unaware of the consequences.

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 a single compact sentence with no filler, front-loading the core operation and listing the two types. It is concise and clear at a glance, though it achieves brevity by omitting behavioral context that would be useful. The structure itself is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an on-chain state-changing tool with no annotations, no output schema, and 4 parameters, the description is incomplete. It does not cover return values, prerequisites, failure modes, or the practical impact of VOTE. The schema explains parameter syntax, but the description leaves the agent with little understanding of what happens when the tool is invoked or when it should be chosen.

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 parameter meanings and the conditional requirements for target and content. The description adds no extra parameter details beyond what the schema provides, such as the 280-character limit or that target is only needed for VOTE. Baseline 3 is appropriate because the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action - executing on-chain operations - and enumerates the two allowed operation types (CHAT to send a message, VOTE to vote someone out). This distinguishes it from sibling tools, most of which are about session/room/game management. It could be more explicit that this is a player action rather than an administrative one, but the core purpose is understandable.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives like create_room, start_game, or settle_round. The description implies it is for sending chat messages or casting votes during a game, but it never states preconditions (e.g., being in a room, having an active session) or situations where this tool is inappropriate. An agent must infer the usage context from the parameter names alone.

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