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record_thought

Record chain-of-thought reasoning from networked AI agents into match replays, preserving LLM thought processes that would otherwise be lost from client-side TUI panels.

Instructions

Record an agent reasoning entry to this match's replay.

Side-channel for networked clients to push their LLM's chain-of-thought to the server so the post-match replay file captures it (the TUI replayer renders agent_thought events alongside actions). Without this, networked replays only show actions; the reasoning lived in the client's TUI panel and was lost.

NOT exposed in the LLM-facing GAME_TOOLS list — the model shouldn't call this itself; the NetworkedAgent's on_thought callback fires it as a side-effect of every assistant response. The connection's pinned (slot → team) mapping determines which side the thought is attributed to.

── Locking ── Resolve (state + room + session + viewer) atomically under state_lock. session.add_thought takes care of its own write synchronisation via the writer lock; the thoughts buffer + hook fire happen inside add_thought and don't need session.lock (action_hooks is a leaf append).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connection_idYes
textYes
Behavior5/5

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

With no annotations, the description carries full burden and excels: it discloses the side-channel nature, attribution via connection mapping, locking mechanisms (state_lock, writer lock), and that it's triggered as a side-effect. Completely transparent about behavior beyond schema fields.

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?

Well-structured with sections and front-loaded purpose. However, the locking details are somewhat verbose for an AI agent; some sentences could be tightened without losing clarity.

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 complexity (side-channel, locking, non-LLM-visible), the description covers purpose, invocation rules, behavioral traits, and synchronization. No output schema exists, but the description doesn't need to explain return values; it's complete for agent understanding.

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?

Despite 0% schema coverage, the description adds meaning to the parameters: connection_id is linked to attribution via pinned mapping, and text is the reasoning content. It does not detail format constraints, but the context is sufficient for understanding parameter roles.

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 states a specific action: 'Record an agent reasoning entry to this match's replay.' It clearly identifies the resource (replay) and distinguishes from siblings by explaining its role as a side-channel for replay capture, not a game action.

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?

Explicitly states the tool is NOT exposed in GAME_TOOLS and should not be called by the model; it's fired as a side-effect by a callback. This provides clear when-not-to-use guidance and explains the intended invocation path.

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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