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find_missed_spawns

Identify assistant responses with multiple actionable items that were answered linearly instead of being delegated as independent tasks, enabling calibration of spawn hint thresholds.

Instructions

Find assistant responses that decomposed into independent blocks but were answered linearly (no spawn() call nearby).

Algorithm:

  1. Pull recent assistant messages (last window_days days, length ≥ min_response_len, excluding subagent jsonls).

  2. For each, count top-level numbered items and H2/H3 headers.

  3. Mark as decomposable if numbered ≥ min_numbered OR headers ≥ min_headers.

  4. For each decomposable response, check whether any tasks row with parent_cid = response's session_id has started_at within ±10 min of the response. If none → missed_spawn.

  5. Return top top_n by score (numbered + headers).

Use this to calibrate the spawn_hint: a high missed-spawn count means the hint isn't strong enough, or thresholds need tuning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
window_daysNo
min_response_lenNo
min_numberedNo
min_headersNo
top_nNo
max_messagesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses the algorithm steps (1-5) and the output format ('Return top `top_n` by score'). There is no contradiction, and the behavior is transparent.

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 well-structured with a clear purpose followed by a numbered algorithm. However, it is somewhat lengthy and could be slightly more concise without losing clarity.

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 complexity (6 parameters, an algorithm, and an output schema), the description provides sufficient context for an AI agent to understand the tool's function and invocation. It covers purpose, usage, and behavior, though it could mention the output schema briefly.

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 0%, so the description must compensate. It explains the purpose of 'window_days', 'min_response_len', 'min_numbered', 'min_headers', and 'top_n' through the algorithm, but 'max_messages' is not mentioned, leaving one parameter partially unexplained.

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 with a specific verb ('Find') and resource ('assistant responses that decomposed...'). It distinguishes itself from sibling tools like 'spawn' and 'spawn_status' by focusing on diagnostic analysis of missed spawns.

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 provides explicit guidance on when to use the tool: 'Use this to calibrate the spawn_hint: a high missed-spawn count means the hint isn't strong enough, or thresholds need tuning.' It implies context but does not explicitly mention when not to use or alternative tools.

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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