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claude-code-delegate-local

by fegone
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

delegate_to_local_agent

Execute a specific agent on an alternative backend (local, cloud, or API) while keeping the orchestrator session intact. Returns the agent's full result.

Instructions

Despacha un agente (cargado desde un .md con frontmatter) a un backend OpenAI/Anthropic- compatible con tool calling completo (read_file / write_file / run_bash). Devuelve resultado consolidado.

USAR cuando el usuario quiera ejecutar un agente específico en un backend alternativo (local, cloud, etc.) en vez del default del orquestador. El orquestador sigue intacto.

Para despachar VARIOS agentes en paralelo en una sola llamada, ver delegate_batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesTarea concreta para el agente. Sé específico, el agente leerá ese prompt.
modelNoModel alias as configured in your LiteLLM proxy (or direct provider). Default 'local-qwen-3-6-35b'. Override via DELEGATE_LOCAL_MODEL env var.local-qwen-3-6-35b
workdirNoDirectorio de trabajo del agente (default: '.' del MCP). Recomendado pasar ruta absoluta al proyecto donde trabajará..
max_turnsNoTope de iteraciones de tool-calling (hard cap 40). Default 0 = AUTO: 15 para backends locales (local-*, MoE-A3B con techo de ctx ~262K), 25 para backends cloud (MiniMax M3 512K, DeepSeek API, Sonnet/Opus). Pasar un valor explícito lo fuerza. Para tareas cortas conocidas: 5-10. Para review/análisis multi-archivo pesado en cloud: 25-30.
agent_nameYesNombre del agente sin .md. Ej: 'seo-content', 'security-engineer', 'database-optimizer'. Debe existir en ~/.claude/agents/
max_tokensNoTope de tokens por turno del modelo. Default = 65536, EXCEPTO si `model` termina en "-max" (p.ej. glm-coding-plan-max, deepseek-v4-pro-max) -> default sube a 150000 automático. Motivo: en deep-reasoning tiers el modelo puede gastar TODO el budget pensando y no dejar nada para la respuesta (verificado: deepseek-v4-pro-max con 32K devolvió 0 tool_calls, respuesta vacía). Pasar un valor explícito siempre gana sobre el auto-bump.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.7/5.0
Behavior5/5

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

No annotations were provided, so the description carries full burden. It discloses that the agent is loaded from a .md file with frontmatter, that tool calling includes read_file/write_file/run_bash, that the backend must be OpenAI/Anthropic-compatible, and that the orchestrator remains intact. This sufficiently informs the agent of behavioral traits.

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?

The description is concise and front-loaded with the core action. Each sentence serves a purpose: stating the action, clarifying when to use, and pointing to an alternative. No unnecessary words or repetition.

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 (6 parameters, output schema exists), the description covers the agent source, backend compatibility, alternative tool, and basic behavior. The presence of an output schema means return values do not need explanation. The description is complete for the agent to select and invoke correctly.

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 main description does not add additional meaning beyond what is already in the input schema parameter descriptions, which are already detailed. Therefore, no extra value is provided by the description for parameters.

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's purpose: 'Despacha un agente (cargado desde un .md con frontmatter) a un backend OpenAI/Anthropic-compatible con tool calling completo (read_file / write_file / run_bash).' It distinguishes from the sibling tool delegate_batch by specifying 'Para despachar VARIOS agentes en paralelo en una sola llamada, ver delegate_batch.'

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?

The description explicitly states when to use the tool: 'USAR cuando el usuario quiera ejecutar un agente específico en un backend alternativo (local, cloud, etc.) en vez del default del orquestador.' It also provides an exclusion by pointing to delegate_batch for multiple agents in parallel.

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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