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deploy-agents

Deploy multiple AI agents to collaborate or work independently on tasks. Choose parallel, sequential, or fan-out strategies; agents share context to avoid conflicts and redundancy.

Instructions

Deploy multiple AI agents to work on tasks collaboratively or independently. Strategies: 'parallel' (N agents on 1 task), 'sequential' (chained, each sees prior output), 'fan-out' (1 agent per task). Agents communicate via shared context to avoid conflicts and redundancy. Current agent mode: read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tasksYesArray of task descriptions. Single task = collaborative (parallel/sequential). Multiple tasks = fan-out (one agent per task).
agentCountNoNumber of agents to deploy for parallel/sequential strategies (2-10). Ignored in fan-out mode where agent count = number of tasks.
strategyNoExecution strategy: 'parallel' = all agents work on 1 task concurrently, 'sequential' = agents chain, each sees prior outputs, 'fan-out' = each agent gets a separate task.parallel
providerNoCLI provider to use ('claude', 'codex', 'gemini'). Defaults to 'gemini'.gemini
modelNoOptional model override. Defaults to server config.
maxConcurrencyNoMax parallel agent processes (1-10, default 3). Controls resource usage.
contextNoShared context injected into every agent's prompt. Use for project background, constraints, or coordination instructions.
useAgentTeamsNoEnable Claude Code Agent Teams (Claude provider only). Delegates team coordination to Claude Code's native Agent Teams feature. Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS enabled in Claude Code settings. See: https://code.claude.com/docs/en/agent-teams
Behavior3/5

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

With no annotations, the description bears full burden. It mentions 'read-only' mode and agent communication, but does not disclose side effects, resource implications, or post-deployment behavior. Increased transparency would improve agent decision-making.

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?

Three sentences efficiently convey purpose, strategies, and a behavioral qualifier, with no 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?

The description covers core functionality and strategies but lacks information on return values and post-deployment actions, leaving some gaps for a complex tool. Considering the absence of output schema, more details on outcomes would be helpful.

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?

The schema provides full descriptions, but the description adds context on strategies and shared context, enhancing understanding beyond the schema. It justifies the 'context' parameter and clarifies strategy semantics.

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 verb 'Deploy' and the resource 'multiple AI agents', and outlines three specific strategies (parallel, sequential, fan-out), making it distinct from sibling tools like agent-status or ask-ai.

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 implies usage for multi-agent deployment but does not explicitly differentiate from sibling tools or state when not to use this tool. The strategy descriptions guide parameter selection but not tool selection.

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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