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delimit_loop_config

Configure loop safeguards and policies—max iterations, cost cap, error threshold, approval policy—before starting a build session.

Instructions

Configure autonomous build loop safeguards (Pro).

When to use: BEFORE starting a loop session — to set max iterations, cost cap, error threshold, approval policy. When NOT to use: to read loop metrics (use delimit_loop_status) or drive the loop (delimit_build_loop).

Sibling contrast: delimit_loop_status reads metrics; delimit_build_loop runs; this configures the policy.

Side effects: writes the loop session config via ai.loop_engine.loop_config. Only non-zero/non-empty values are applied — pass just the fields you want to change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idNoSession to configure. Empty = create new.
max_iterationsNoMax tasks before stopping. Default 50.
cost_capNoMax session cost in dollars. Default 5.0.
auto_consensusNoIf True, suggest consensus when ledger empty.
error_thresholdNoConsecutive errors before circuit-breaker trips. Default 3.
statusNoSet loop status — "running", "paused", "stopped".
require_approval_forNoComma-separated action types requiring human approval.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses side effects (writes to ai.loop_engine.loop_config) and the behavior that only non-zero/non-empty values are applied. Lacks details on error handling or persistence, but still good.

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?

Well-structured with title, usage guidelines, sibling contrast, and side effects. Front-loaded and concise without redundant information.

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 (7 params with full schema descriptions) and the presence of an output schema, the description covers all needed behavioral and usage aspects, making it complete for an AI agent.

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?

Schema description coverage is 100%, so baseline is 3. The description adds critical behavioral insight that only non-zero/non-empty values are applied, which is not in the schema. This extra context justifies a 4.

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 configures autonomous build loop safeguards, with specific verb 'Configure' and resource. It distinguishes from siblings by naming alternatives for reading and running the loop.

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 provides 'When to use' and 'When NOT to use' sections, along with specific sibling tool names (delimit_loop_status, delimit_build_loop), making it easy for the agent to decide when to invoke.

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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