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chapmanjw

Rutherford MCP Server

by chapmanjw

delegate

Delegate a task to an AI coding agent and return its normalized result. Supports fallback, model selection, and safety modes.

Instructions

Delegate a task to one ACP agent and return its normalized result.

cli is an agent id (see capabilities); model is optional (the agent's default otherwise). safety_mode is read_only | propose | write | yolo; when omitted, the configured default_safety_mode applies (read_only out of the box). write and yolo also need a trusted workspace (trust_workspace=true or a configured allowlist). files lists paths to put in scope. role names a persona (see list_roles) whose system prompt is prepended to prompt. effort (low | medium | high | xhigh) asks the agent to spend more reasoning where it has a knob (codex/cursor via the model id, cline via --thinking, junie via env); a reported no-op for an agent with none. Omitted, the configured default_effort (per-agent or global) applies. fallback is an ordered list of alternate targets (cli / cli:model strings or {cli, model} objects) tried when the primary fails on a re-execution-safe failure (a spawn/handshake failure that never ran the prompt); a benched alternate is skipped and fallback_chain records the path. A write/yolo delegation never falls back. allow_model_fallback (default true) first retries the same agent on its configured fallback model on a model-unavailable failure, where it has one. persist keeps this run as a durable job under <jobs_dir>/<run_id>/ (state.json + answer / diff artifacts); None follows default_persistence (ephemeral out of the box), true / false force it. session_id resumes a prior agent session: pass the session_id from an earlier delegate result and the agent reloads that conversation (ACP session/load) instead of starting fresh, so a follow-up turn continues it; agents that do not persist their own sessions fail RESUME_FAILED. mode="async" runs the turn as a background job and returns a job_id (poll with job_status / job_result); mode="sync" awaits it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cliYes
modeNosync
roleNo
filesNo
modelNo
effortNo
promptYes
persistNo
fallbackNo
timeout_sNo
session_idNo
safety_modeNo
working_dirNo
trust_workspaceNo
external_trackingNo
allow_model_fallbackNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries the full burden. It thoroughly explains behavioral traits: safety_mode and trust_workspace requirements, fallback behavior (write/yolo never fall back, allow_model_fallback retries on model failure), persistence options, async vs sync mode, and session resumption. This level of detail fully discloses side effects and constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy and dense. While it is front-loaded with the purpose, the parameter details are presented as a continuous paragraph without clear separation. It could be more concise by using bullet points or summarizing common patterns. Every sentence is informative, but the structure hinders quick scanning.

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 complexity of the tool (16 parameters, required schema fields, output schema, advanced features like fallback, async, persistence), the description is highly complete. It covers all parameters, default behaviors, edge cases, and explains the return value for async mode. The presence of an output schema reduces the need to describe return values, but the description still adds context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides extensive semantic meaning for each parameter beyond the bare schema. Given a schema description coverage of 0%, the description compensates by explaining every parameter: cli, model, safety_mode, files, role, effort, fallback, allow_model_fallback, persist, session_id, mode, and more. It also explains defaults and interactions.

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 opening sentence clearly states the tool's function: 'Delegate a task to one ACP agent and return its normalized result.' This provides a specific verb and resource, and the tool's name 'delegate' aligns with this purpose. It is distinct from sibling tools like 'consensus', 'debate', 'analyze', and 'plan'.

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to use this tool versus its siblings. It does not mention alternatives or scenarios where delegation is appropriate versus other tools. The parameter explanations are detailed but do not provide usage context.

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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