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Delimit Agent Poll

delimit_agent_poll

Explicitly poll a dispatched worker's transcript to check completion and retrieve final output, surfacing uncertain outcomes instead of silent failures.

Instructions

Read a tracked worker's output without inference (explicit poll).

When to use: after delimit_agent_dispatch with launch starts a muse worker, to check whether it finished. Call it explicitly — there is no polling loop in the server. A finished worker exposes its final text here; a worker that died mid-run (e.g. model stream idle timeout) surfaces as "uncertain", never as a silent completion. When the final text carries a QUESTION: block from the worker, this call binds it to the task with a handoff receipt — answer it with delimit_agent_answer. When NOT to use: to read audit-only dispatches (use delimit_agent_status), to close a task (use delimit_agent_complete), or in a tight loop — one explicit call per check; the worker transcript is re-read each time.

Sibling contrast: delimit_agent_status reads the stored task record without touching worker output; this reads the worker's JSONL transcript and advances running → completed/uncertain. delimit_agent_complete accepts and closes; delimit_agent_cancel aborts.

Side effects: reads the worker's JSONL transcript plus its rc sidecar (no model inference) and writes the observed outcome to the task record via ai.agent_dispatch.poll_worker, with an audit entry per call. Recording a worker QUESTION: block additionally creates a handoff receipt addressed to the lease owner.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesTask id of a launched worker (AGT-xxx). Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv4.19.1

TDQS

A4.6/5.0
Behavior5/5

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

The description goes far beyond the annotations: it discloses that the call reads the JSONL transcript and rc sidecar, writes the outcome to the task record via ai.agent_dispatch.poll_worker, creates an audit entry, and may create a handoff receipt. It also clarifies failure semantics ('uncertain', never silent completion) and explicitly notes there is no polling loop. This does not contradict the annotations, which set readOnlyHint=false and destructiveHint=false.

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 clear sections and front-loads the core purpose in the first sentence. It is long but every paragraph earns its place by covering usage, exclusions, sibling contrast, and side effects. Slight redundancy exists between 'Side effects' and the earlier mention of handoff receipts, but this does not substantially hurt 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, the description is complete: it names prerequisites, expected transitions, failure modes, side effects, alternatives, and even the special QUESTION: block handoff behavior. An output schema exists, so return-value description is not required. An agent has enough to invoke it correctly and interpret its effects.

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 100%, so the schema already documents task_id as an AGT-xxx string and required. The description adds context that the task is a launched worker from delimit_agent_dispatch, but it does not add parameter syntax or format details beyond the schema. The baseline of 3 applies because the schema carries the full load.

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 opening sentence states a specific verb and resource: 'Read a tracked worker's output without inference (explicit poll).' The sibling contrast section explicitly distinguishes it from delimit_agent_status, delimit_agent_complete, and delimit_agent_cancel, so an agent can tell it apart without inspecting schemas.

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 'When to use' section gives the exact trigger: after delimit_agent_dispatch launches a muse worker, to check completion. The 'When NOT to use' section names alternatives for audit-only dispatches, closing tasks, and tight loops. This is explicit, actionable guidance with no ambiguity.

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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