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live_heartbeat

Send a heartbeat to mark the agent as online for a specified TTL window, enabling the bridge to track presence without model-layer polling.

Instructions

Agent 端 keep-alive. 在 ttl_seconds 内 bridge 算 online. ⚠️ ttl_seconds 是在线判定窗口, NOT 轮询间隔. 值更契约 v3: 等待不烧模型回合(红线: 禁止模型层轮询), 用看门脚本/live_wait/你家自己的监听机制均可, 见 recipe://live-teaching. 看门脚本会替你打心跳; 走 live_wait 的, 它自带 auto-heartbeat. 回应契约(单层 v3): 实质回答质量优先, 不设硬秒数, 在场感由在线灯负责不由报文. (旧 context_status 参数已退役——上下文余量指示灯已整体拆除, 心跳只管在线.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pair_idNo
ttl_secondsNodefault 60, clamp [15, 600]
Behavior4/5

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

With no annotations, the description carries full behavioral disclosure. It explains key nuances: ttl_seconds semantics, the 'waiting does not burn model turns' contract, and the retirement of context_status. However, it does not disclose return values, error handling, or whether the call is idempotent, leaving some gaps.

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 dense but each sentence carries relevant information, covering purpose, warnings, and contracts. It is front-loaded with the core purpose and then elaborates on nuances. Slightly longer than strictly necessary, but not wasteful.

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

Completeness3/5

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

The description covers many contextual aspects: purpose, ttl semantics, usage alternatives, and legacy deprecation. However, it lacks details on pair_id, return values, and mechanics of how the heartbeat is recorded or used. Given the absence of an output schema and annotations, these gaps affect completeness.

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 coverage is 50%: ttl_seconds has a schema description, pair_id does not. The description adds meaningful semantics for ttl_seconds by clarifying it is an online window, not a polling interval, but it never explains pair_id. Since pair_id is undocumented in both schema and description, the compensation is incomplete.

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 clearly states the tool's purpose as an 'Agent 端 keep-alive' and defines the online window via ttl_seconds. It distinguishes itself from related tools by mentioning live_wait's auto-heartbeat and the retired context_status parameter, though it does not name sibling tools directly.

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?

The description provides explicit usage guidance: it warns that ttl_seconds is an online determination window, NOT a polling interval, and explicitly forbids model-layer polling. It suggests alternatives (watchdog script, live_wait, custom listeners) and points to a recipe, making the when and when-not usage clear.

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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