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veto_execute_parallel

Run multiple worker agents in parallel to collect input from diverse domain experts like coders, testers, and security scanners in a single round-trip.

Instructions

Runs multiple worker agents simultaneously via Promise.all. Use to get domain expert input from several agents in one round-trip — e.g. coder + tester + security-scanner all planning the same feature together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tasksYesList of agent tasks to run in parallel.
llm_backedNoIf true, uses the agentic loop to run these agents via the host AI. Required for Phase 2 LLM-backed reasoning.
max_tokensNoOptional: token budget for this parallel execution. Veto estimates combined output tokens and warns if the estimate exceeds this limit. Logged to usage_log.
project_dirNoOptional: project directory applied to all tasks (per-task project_dir overrides this). Auto-injects codebase context.
editor_modelNoOptional: override model used for the editing/execution phase (e.g. claude-3-5-haiku).
agent_outputsNoPhase 2 responses from the host AI (JSON). Pass this back when prompted by the server to complete the agentic loop.
architect_modelNoOptional: override model used for the architecture/planning phase (e.g. claude-3-7-sonnet).
Behavior2/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, so the description carries burden to disclose other behaviors. It does not mention failure modes (e.g., Promise.all rejection behavior), rate limits, or required permissions. The description adds only the name of the implementation technique without deeper behavioral context.

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 sentences, no waste. The first sentence states core functionality; the second provides usage context and example. Front-loaded and efficient.

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?

No output schema exists, so the description should explain return values but does not. It also omits details about error handling (Promise.all rejection), per-task result format, or behavior when tasks have conflicting project directories. For a tool with nested objects and 7 parameters, this is insufficient.

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 100%, so baseline is 3. The description adds no additional parameter-level information beyond what the schema already provides. No credit needed for compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it runs multiple worker agents in parallel via Promise.all, with a concrete example (coder + tester + security-scanner). However, it does not explicitly differentiate from sibling tools like veto_compose_agents or veto_council_debate that also involve multiple agents.

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?

The description says to use it for getting domain expert input from several agents in one round-trip, providing clear when-to-use guidance. However, it lacks when-not-to-use instructions or alternatives, so the guidance is incomplete.

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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