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offload_task

Delegate a focused coding subtask (function, tests, regex, refactor) to a fast external worker, freeing host agent context and execution limits.

Instructions

Offloads a focused implementation subtask, utility function, unit test suite, regex, or refactoring step to a fast external worker (DeepSeek-V4 Flash or OpenAI gpt-5-mini) to save host agent context and execution limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe exact subtask, function to write, unit test table, or transformation to perform.
modelNoOptional model override (defaults to deepseek-v4-flash or gpt-5.6-sol).
effortNoReasoning effort if using a reasoning model (default: 'low' for GPT SOL to achieve sub-2s latency).
contextNoSurrounding code, types, constraints, or interfaces needed to execute the task accurately.
providerNoWorker provider (default: 'deepseek' for sub-2s latency and minimal cost).
Behavior2/5

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

Annotations are absent, so the description bears full responsibility. It discloses that an external worker is used and mentions default models and latency, but it doesn't clarify whether the call is synchronous, what happens on failure, network dependencies, or cost implications. It also doesn't describe the return value or any side effects.

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 a single, efficient sentence that front-loads the core action and resource, then adds the purpose. It's not overly verbose and avoids redundancy, though it could be slightly more structured by separating the purpose from the resource list.

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

Completeness2/5

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

With 5 parameters, no output schema, and no annotations, the description is too thin. It doesn't explain expected output format, error conditions, when to choose specific providers/models, or how the result is returned. An agent would need to inspect the schema or rely on defaults without clear guidance.

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 all parameters. The description adds no additional meaning beyond what the schema provides; it doesn't explain relationships between parameters or edge-case behaviors, so the baseline 3 applies.

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 uses a specific verb ('offloads') and lists concrete resources ('focused implementation subtask, utility function, unit test suite, regex, or refactoring step'), clearly distinguishing this tool from siblings like consult_architect or deliberate_council, which are about thinking rather than execution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states the motivating purpose ('to save host agent context and execution limits') but doesn't explicitly say when to use this vs alternatives, nor when not to use it. It implies usage for small, well-defined tasks but no explicit exclusions or comparison with doing the work inline.

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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