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delegate_start

Delegate multi-step research, file analysis, or long-running Python tasks to a local agent and receive a job ID instantly, then poll for status and collect results.

Instructions

Hand a multi-step task to Yevgeny and get a job id back immediately. Yevgeny can search and read files, fetch web pages, write structured output, and run Python (including multi-hour background simulations). Returns at once - poll with delegate_status and collect with delegate_result. Give a thorough brief: state the goal, the sources or directories to use, and the exact output format you want.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootNoAbsolute path the agent may read from via glob/grep/read_file. Omit for web-only work. The agent can never write here.
taskYesFull task brief, including desired output format
modelNoOllama model, default gemma4:12b
Install Server

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses asynchronous behavior (returns at once, poll later), support for multi-hour background simulations, and the read-only restriction on root. It stops short of failure modes or concurrency details, but core behavioral traits are well covered.

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?

Two efficient sentences. The primary behavior is front-loaded, then capabilities, then invocation protocol, then brief guidance. Every sentence contributes without waste.

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 no output schema, the description explaines the return value (job id) and the rest of the workflow (poll/collect). It covers capabilities, the required brief format, and the root condition. It omits error/health edge cases, but those are plausibly covered by sibling tools lik e yevgeny_health.

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 coverage is 100%, so the baseline is 3. The description adds meaningful usage meaning beyond the schema: it instructs the caller to provide a thorough brief with goal, sources/directories, and output format, and tells when to omit root. These enrich the task and root parameters beyond their raw schema descriptions.

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 ('Hand a multi-step task') and a resource (Yevgeny), and clearly states the immediate outcome (job id back). It distinguishes itself from siblings by explicitly pointing to delegate_status and delegate_result as follow-up tools.

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?

It explains when to use the tool (multi-step tasks) and gives clear post-invocation guidance (poll with delegate_status, collect with delegate_result). It also provides a conditional usage detail (omit root for web-only work), though it does not explicitly contrast with delegate_cancel or ask.

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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