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send_to_agent

Send a natural-language coaching instruction to a designated team's AI agent (blue or red) at the start of its next turn. Agents see the context but may ignore it. Only human coaches can send.

Instructions

Mutating. Coach-only tool: queue a natural-language message that will be delivered to the specified team's AI agent at the start of its next turn. team must be 'blue' or 'red'. text is the coaching instruction (e.g. 'push cavalry on the right flank'). The agent sees the message as context but is free to ignore it. Only human coach connections can use this; AI agent connections receive an error. Messages are not visible to the opposing team.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connection_idYes
teamYes
textYes
Behavior5/5

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

With no annotations, the description fully discloses key behaviors: it mutates state, queues the message for the next turn, the agent may ignore it, and messages are not visible to the opposing team. This provides sufficient transparency for an AI agent to understand the tool's effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first covers purpose, constraints, and parameters; second adds behavioral details. No extraneous information. The description is front-loaded and efficient.

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 3 required parameters, no output schema, and no annotations, the description adequately covers the tool's function, constraints, and parameter semantics. It could mention error handling for invalid team values or connection types, but the core information is present.

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 input schema has no parameter descriptions (0% coverage), so the description adds crucial meaning: 'team' must be 'blue' or 'red', 'text' is a coaching instruction. 'connection_id' is implied to identify the human coach connection. While 'connection_id' is not explicitly explained, the context 'Coach-only tool' gives enough hint.

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 specifies the exact action ('queue a natural-language message') and the resource ('team's AI agent'). It also states the delivery timing ('at the start of its next turn'). This clearly distinguishes it from sibling tools like 'attack' or 'move', which are direct game actions.

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?

The description explicitly restricts usage to human coach connections and states that AI agent connections receive an error. It also specifies that team must be 'blue' or 'red'. However, it does not explicitly state when not to use this tool or suggest alternatives, though no siblings serve a similar purpose.

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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