Rutherford MCP Server
Rutherford MCP Server lets you orchestrate existing ACP-compatible coding agents from an MCP client—delegating, comparing, debating, reviewing, planning, and managing agent-driven work—without calling model APIs directly or reimplementing each CLI.
Delegate work: send one task to one agent with optional model, role, files, effort, fallback targets, session resume, persistence, and sync/async execution.
Run panels: ask several agents the same prompt in parallel with consensus strategies, saved panels, per-seat steering, judging, synthesis, quorum, and budgets.
Run debates: have multiple agents argue over rounds using persistent sessions, with convergence tracking, optional transcript carry-forward, and closing synthesis.
Review and plan: get multi-agent code review of a diff or paths, or ask one agent for a read-only implementation plan.
Continue durable work: resume completed delegate/consensus/debate jobs with a new prompt while preserving parent lineage.
Manage background jobs: list jobs, view in-flight activity, check status, fetch results, cancel running work, and run tools asynchronously.
Configure and diagnose agents: list capabilities, run doctor probes, sign agents in, discover installed ACP agents, scaffold config, reload panels, and list role personas.
Analyze kept runs: run offline reports over persisted consensus history, such as cross-lineage historical agreement.
Control safety: default read-only operation, with propose/write/yolo modes gated by trusted workspaces and explicit permissions.
Allows delegation of coding tasks, code reviews, and consensus-building to Codex CLI, using OpenAI's models for code generation and analysis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Rutherford MCP Server Ask Claude Code and Codex to implement the login feature and compare solutions."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
uv tool install rutherford-mcp-serverUsing Claude Code? The Rutherford Claude Plugin wraps this server with one-step setup: it auto-registers the server (no manual
mcp add) and adds skills, an orchestrator agent, and slash commands for setup, panels, consensus, debate, and review. Install it with/plugin marketplace add chapmanjw/rutherford-claude-pluginthen/plugin install rutherford@rutherford-claude. Either path is fully supported — reach for the plugin for a batteries-included Claude Code experience, or wire up this server directly for any MCP client.
What Rutherford is
Rutherford is a Model Context Protocol server that speaks the
Agent Client Protocol on the other side. Your MCP client (a coding
CLI or a desktop app) calls Rutherford's tools; Rutherford spawns each target coding agent as an ACP
server over stdio and drives it through a real initialize / new_session / prompt exchange.
It is the ACP client, and each coding agent is an ACP agent. That distinction matters: under ACP the protocol negotiates the answer, token usage, tool activity, and permissions as structured events, so Rutherford never scrapes an agent's stdout and never reimplements a CLI's features. It also never calls a model provider's API directly — every answer comes from an agent you already log into, in the agent's own account.
your MCP client (Claude Code, Cursor, Codex, Claude Desktop, ...)
| MCP over stdio
v
rutherford-mcp-server (the ACP client)
| ACP over stdio, one session per voice
+--> goose acp
+--> codex-acp (the Zed adapter fronting Codex)
+--> claude-agent-acp (the Zed adapter fronting Claude Code)
+--> ... 17 more built-in agents, all config-drivenA voice that fails to spawn, handshake, or answer comes back as one failed result in a structured envelope, never an aborted panel.
Related MCP server: all-agents-mcp
See it work
The mode that is not just parallel answers is debate. Round one is each voice's independent take; in
every later round, each voice sees the others' latest positions and is asked to rebut and revise. Each
voice keeps one persistent ACP session across the rounds, so it remembers its own prior reasoning and
only the delta is sent each round — the capability the old subprocess-per-call model could not offer.
prompt "Is UUIDv7 or ULID the better primary key for a high-write event table?"
panel claude_code, codex, kiro rounds: 3
round 1 claude_code UUIDv7 — the timestamp prefix gives B-tree index locality
codex UUIDv7 — standardized and DB-native; monotonic within a process
kiro UUIDv7 — but argues ULID is BOTH lexicographically sortable AND
collision-resistant across concurrent writers
round 2 claude_code flags that Kiro conflates two properties: ULID's sortability and
its per-process monotonicity are not the same guarantee
codex agrees — the monotonic guarantee is per-process, not cross-node
kiro revises: cross-node, UUIDv7's timestamp prefix gives the locality
without relying on a per-process assumption
result converged on UUIDv7, with a closing synthesis of where the panel agreed and whyThe call returns the full per-round transcript plus the closing synthesis, so you can retrace who said what and where someone revised. Debates do not always converge or change a mind, but when they do the transcript shows precisely where.
Quickstart
You bring the crew. Rutherford does not install or authenticate any coding agent — it drives the ones you already have. You need Python 3.11+ and at least two ACP-capable agents installed and signed in (two is enough for a consensus or a debate). If you already use Claude Code or Codex, you have most of what you need.
1. Install Rutherford.
uv tool install rutherford-mcp-server
# or: pipx install rutherford-mcp-server / pip install rutherford-mcp-serverThis puts the console entry points rutherford-mcp-server and rutherford on your PATH (same
process; the short name is for terminal subcommands). The same command starts the stdio server on
Windows, macOS, and Linux; python -m rutherford is equivalent.
2. Register it with your MCP client.
claude mcp add rutherford -- rutherford-mcp-server # Claude Code
codex mcp add rutherford -- rutherford-mcp-server # CodexFor Claude Desktop, Cursor, and other JSON-config clients:
{ "mcpServers": { "rutherford": { "command": "rutherford-mcp-server" } } }If rutherford-mcp-server is not on the client's PATH, use an absolute path, or python -m rutherford
with the interpreter from the environment where you installed it. More clients and WSL:
docs/mcp-client-integration.md.
3. Scaffold a config (optional). Rutherford works with zero config. To write a starter file, either run the one-shot CLI from your terminal:
rutherford-mcp-server init # or: python -m rutherford init [--global] [--yes]or, once it is registered with a client, ask for the setup tool:
Run Rutherford's setup and write a project config.
Both resolve the config path, write a commented starter config.toml at the effective defaults, and never
clobber an existing file. init targets <cwd>/.rutherford/config.toml (or the global path with
--global); setup returns the path and content to the client and writes with write=true.
To allow write / yolo delegations into the current repo without a per-call trust_workspace=true,
register cwd in the global allowlist:
rutherford-mcp-server trust # adds cwd to global trusted_workspaces
rutherford-mcp-server trust --list # or: python -m rutherford trust --list
rutherford-mcp-server untrust # removes cwd from the global allowlistConfig is read once at server start, so restart or reconnect the server for a new entry to take effect.
4. Run doctor first. Multi-agent auth and PATH is the most common thing that goes wrong, so
confirm the crew actually drives before your first real task:
Run Rutherford's doctor and tell me which agents spawn, handshake, and answer.
doctor probes each agent with a real read-only ACP round trip — the only trustworthy health signal,
since there is no cheap non-interactive auth check. Each report is ok, no_answer,
model_unavailable, handshake_failed, auth_required, not_installed, or error. An auth_required agent is installed but
not yet signed in; sign it in once with login. Two or more ok agents means you are ready.
No paid agent subscription? Run your first consensus for free against a local model. With
Ollama or LM Studio running, Rutherford auto-detects
each tool-capable model and registers it as a goose-based agent — no key, no account. See
docs/local-models.md.
The tools
You rarely call these by name; your agent picks them from your request. Everything defaults to read-only.
Tool | What it does |
| Hand one task to one ACP agent; get one normalized result back. |
| Ask the same prompt of several agents in parallel; return every voice. |
| Have several agents argue across rounds (persistent sessions) and return the full transcript. |
| Review a diff or a working dir's changes across one or more agents — a code-review-shaped consensus. |
| Produce an implementation plan for a task without making changes (read-only by construction). |
| Resume or build on a completed durable job (delegate / consensus / debate) with a new prompt. |
| Run an offline report over the kept run corpus (e.g. |
| List the registered agents (id, display name, launch command, provider) — the cheap snapshot. |
| Probe each agent with a real read-only ACP round trip and report conformance. |
| Sign an agent in once through ACP |
| Detect installed ACP agents from the community registry and propose reviewable config blocks. |
| List the role personas you can pass as |
| Show where config lives, scaffold a starter |
| Reload the named multi-agent panel definitions from config without restarting the server. |
| List the background jobs being tracked (every status), newest first. |
| Show only the jobs in flight right now, each with a live elapsed time. |
| Report one background job's status and timings. |
| Return a finished job's result envelope (identical to the sync envelope). |
| Cancel a running background job and tear down its work. |
Shared arguments on delegate / consensus / debate: working_dir, files (paths to put in
scope), safety_mode, timeout_s, role, and mode (sync or async). delegate also takes
trust_workspace for the mutating modes; debate takes rounds, judge, and synthesize.
The agent roster
Rutherford ships 21 built-in agents with curated launch commands and quirks (the Windows npm-shim
resolution, per-agent handshake budgets, a fixed provider) that a bare acp.json cannot express, so
they work with zero config:
id | agent | how it launches | login |
| Goose |
| provider key / |
| OpenCode |
| a configured provider |
| Mistral Vibe |
| Mistral / |
| Cline |
| Cline's own service auth |
| Junie |
| JetBrains login |
| Kimi Code |
| Moonshot login |
| OpenHands |
| a configured provider |
| Codex |
| the existing Codex (ChatGPT) login — no API key |
| Claude Code |
| the existing Claude Code login — no API key |
| GitHub Copilot |
| GitHub Copilot plan |
| Qwen Code |
| Qwen OAuth / OpenAI-compatible key |
| Factory Droid |
| Factory login |
| Cursor |
| Cursor subscription |
| Kiro |
| Kiro login / |
| Pi |
| Pi login |
| Hermes |
| Nous endpoint |
| Gemini CLI |
| Google / Gemini CLI login |
| Qoder |
| Qoder login |
| Grok |
| xAI login + SuperGrok subscription |
| fast-agent |
| provider API key (env or |
| Google Antigravity |
| Google sign-in, once, with the |
codex and claude_code launch through the official Zed adapters (codex-acp and
claude-agent-acp, npm @agentclientprotocol/*), which front the Codex and Claude Code CLIs as ACP
servers and reuse the existing CLI login — no API key. cline drives over ACP only with Cline's own
service auth (a ChatGPT-subscription or OpenRouter provider set in the desktop app does not reach the
headless --acp path). hermes depends on the configured Nous model and its latency can be high.
gemini is Google's official Gemini CLI (the --acp mode works as of CLI 0.46.0). qoder's --acp
flag is real but hidden from --help, and Qoder's installer drops qodercli at ~/.qoder/bin/ rather
than on PATH — add that directory to PATH, point [agents.qoder] command at the full path, or let
discover find it. grok (xAI) is ACP-native and connects cleanly, but a completed turn needs a SuperGrok
subscription — without it the model call returns 403; run doctor connect_only=true to confirm Rutherford
can reach and configure it (it reports reachable and the advertised models) independent of the
entitlement. antigravity is Google Antigravity's ACP server, which Google now points individual Gemini
accounts to (Gemini CLI refuses them). It is a standalone binary from the
ACP registry, separate from the agy CLI, and is
not installed onto PATH, so add its directory to PATH or set [agents.antigravity] command to the full path.
Until it has been signed in once, doctor reports it as auth_required: run login agent=antigravity method=oauth-personal, open the returned sign-in link (Antigravity prints one; if signin_urls is empty
the agent may have opened a browser itself), and poll job_status / job_result with the returned
job_id. The server caches the credential under ~/.gemini/antigravity-acp/ ($GEMINI_HOME relocates
it), so later sessions open without it. It has been verified on Windows; the macOS and Linux launch commands (including the
--uid= argument Linux gets) come from the registry entry and are not yet verified. If it fails to launch
on Linux, try [agents.antigravity] command = ["agy_acp_server.par"] to drop that argument.
Not every agent drives cleanly on every machine — run doctor to see which actually answer here.
Config-driven agents. Under ACP an agent is just how to launch it plus a few quirks, so the roster
is config-driven. An [agents.<id>] section overrides a built-in's command / env / provider / model,
disables one with enabled = false, or defines a brand-new agent (any unknown id, which must supply a
launch command). enabled_agents restricts the registry to an allowlist. The launch fields mirror
the Zed/Cline acp.json shape, and the loader auto-imports an acp.json beside the global config or
in the project's .rutherford/. See docs/adding-an-agent.md.
Local models. With auto_detect_local_models on (the default), Rutherford probes a running Ollama
(:11434) and LM Studio (:1234) at startup and registers each tool-capable model as a goose-based
ACP agent automatically. You can also point an agent at a local runtime by hand. See
docs/local-models.md.
Safety modes
Every delegation runs in one of four modes, defaulting to the most restrictive. Rutherford is the permission authority at the moment of each ACP tool call: it answers the agent's filesystem-write, terminal-execution, and tool-permission requests according to the mode.
Mode | Meaning |
| Inspect only. Reads are served; writes, terminal execution, and tool-permission requests are denied. |
| Same denials as |
| The agent may modify the workspace, subject to the agent's own approvals. |
| The agent may act without approval prompts. |
A call that omits safety_mode adopts the configured default_safety_mode (read_only out of the
box); an explicit value always wins. write and yolo require a trusted workspace: the target
working_dir must be on the trusted_workspaces allowlist, or the call must pass
trust_workspace=true. Full detail: docs/security.md.
Jobs, roles, and config
Background jobs. Pass mode="async" to delegate / consensus / debate to run the work off
the request path: the call returns a small {job_id, status, tool} envelope immediately, and the work
runs as an in-memory task. Manage it with list_jobs, activity, job_status, job_result, and
cancel_job. A finished job's result envelope is byte-for-byte the same as the sync path's. Jobs are
in-memory and clear on restart.
Roles. A role is a reusable system prompt. Pass role="<id>" to delegate / consensus /
debate and the persona is prepended to your prompt. Five built-ins ship as package data:
principal-reviewer, architect, debugger, security-reviewer, and explainer. A role_dirs
directory adds new roles or overrides a built-in. list_roles enumerates the catalog.
Config. Rutherford works with zero config. When you do configure it, a TOML file at the global or
project scope sets the agent roster, defaults, and safety policy; RUTHERFORD_* environment variables
override specific fields. Full reference: docs/configuration.md.
Documentation
docs/architecture.md — the v3 ACP-native architecture and the key seams.
docs/configuration.md — the complete
RutherfordConfigreference and config discovery.docs/adding-an-agent.md — config-driven agents,
acp.jsonimport, local backends.docs/local-models.md — Ollama and LM Studio as first-class voices.
docs/recipes.md — task-oriented usage recipes.
docs/mcp-client-integration.md — wiring Rutherford into MCP clients.
docs/security.md — the safety model and the permission engine in depth.
docs/troubleshooting.md — common problems and fixes.
docs/integration-testing.md — running the real-agent integration suite.
The name
.---------.
| \/\/\/ |
| O [==]|
| < |
| \___/ |
'---------'
-- Ensign Sam Rutherford --
USS Cerritos . EngineeringNamed for the cheerful engineer aboard the USS Cerritos in Star Trek: Lower Decks, who has a gift for getting heterogeneous systems to cooperate. That is the job here: one agent hands work to a crew of others and brings the results back. Star Trek and Lower Decks are trademarks of their respective owners; this is an unaffiliated, fan-named open-source project.
Contributing
See CONTRIBUTING.md. The whole core is testable without a real agent; run
just check before pushing, then just test-integration for whatever agents your machine has
installed and authenticated.
License
MIT (c) John Chapman. See LICENSE.
Available Tools
19 toolsactivityA
Show the background jobs IN FLIGHT right now (running + pending), each with a live elapsed time.
The focused "what is happening now" snapshot, distinct from list_jobs: where list_jobs enumerates
every tracked job of every status (finished ones included), activity returns only the jobs still in
flight -- {active: [...], count} with each row {job_id, tool, status, summary, started_at, elapsed_s}, longest-running first. Empty ({active: [], count: 0}) when nothing is running.
Inputs: no named parameters. Returns: result
analyzeA
Analyze the kept run corpus (read-only). report="historical_agreement" is the default and only report.
historical_agreement scans the consensus panels you kept (persist=true / default_persistence=job) and
reports how often two DISTINCT model lineages reached the same verdict when they co-voted -- an
OBSERVATIONAL signal for your roster choice (e.g. a lineage that never adds a dissent), NOT a vote discount:
agreement is not correctness, so down-weighting agreeing lineages would punish them for being right
together. An empty corpus returns an empty report whose notes explain how to build one.
Inputs: report. Returns: result
cancel_jobA
Cancel a running background job (killing its work) and return {job_id, status}; JOB_NOT_FOUND if unknown.
Cancelling an already-finished job is a no-op that returns its current status. The job's process tree is torn down on the next cancellation point.
Inputs: job_id (required). Returns: result
capabilitiesA
List the ACP agents Rutherford can drive (static roster; no spawn).
Each agent includes id, display name, launch command, provider, configured default_model /
fallback_model, model_selection (launch_argv for Cursor-style launch flags, else
in_session), and effort_capable. Model resolution is: explicit model -> agent
default_model -> agent-native default. For live advertised model ids, use
doctor(agent=<id>, connect_only=true) -- capabilities never probes an agent.
Inputs: no named parameters. Returns: result
consensusA
Ask the same prompt of several ACP agents in parallel and reduce the voices.
targets is a list of {cli, model} objects or cli / cli:model strings; each runs as its own ACP
session concurrently. Omit targets, pass an empty list, pass the sentinel "all", or set
expand_all=true to fan out to every registered agent (each at its default model, capped at
max_targets); the result's skipped field explains any agent left out. Or name a saved panel (with
optional panel_overrides) to reuse a stored roster + strategy instead of targets; they are mutually
exclusive (see reload_panels). A {cli, model} target may
also carry per-seat role / label / weight / parity / stance. With a strategy other than
all-voices (unanimous | majority | plurality | weighted | parity-pair | rank, optionally
with a verdict_schema), each voice is asked for a verdict and the panel collapses to one outcome
(StrategyResult) instead of every voice. rank is a two-round protocol (F4b): every voice answers, then
ranks the OTHER answers anonymized and self-excluded, aggregated by Borda mean-rank into a rank
leaderboard with a pairwise agreement matrix and concordance; require_dissent surfaces each non-winning
position on its dissent. discount_correlated=true (F3 vote-math, opt-in) down-weights correlated votes
by model-family lineage (vendor fallback) so a panel of "one model in N CLI costumes" counts as one
effective vote under majority / plurality / weighted (each voice's lineage_weight shows it). Optional
stances (parallel to targets) steer each voice and cannot combine with the auto-expanded panel.
synthesize (defaults to synthesize_default, off
out of the box) adds a server-side combined answer (all-voices only); judge names the seat that
writes it. timeout_s applies to every voice; one failing voice is a failed result, never an aborted
panel. Consensus is read-only deliberation: a safety_mode beyond read_only (propose / write /
yolo) is refused -- there is no coherent merge of edits from several agents into one tree -- so route
write / propose work through delegate (a single agent isolated in a worktree sandbox). role names a
persona (see list_roles) prepended to the prompt every voice
receives. effort (low | medium | high | xhigh | max) asks every voice to spend more reasoning where it
has a knob; max is accepted and clamped to each agent's ceiling. time_budget_s is a wall-clock
deadline for the WHOLE panel (distinct from each voice's
timeout_s): at the deadline answered voices are kept, in-flight ones cut, and the panel aggregates over
the harvest if min_quorum usable remain (stop_reason="budget", with a rollup); below min_quorum
is BUDGET_EXHAUSTED. on_budget is harvest | continue | resume (default default_on_budget). persist
keeps the panel as a durable job (F2): a parent state.json linking a child record per voice, plus
voices/voice-N.md artifacts; None follows default_persistence, true / false force it.
mode="async" runs the panel as a background job and returns a job_id (poll with job_status /
job_result); mode="sync" awaits it.
Inputs: prompt (required), mode, role, files, +20 more. Returns: result
continue_jobA
Continue a completed durable job with a new direction, picking up where the kept run left off.
job_id is the id of a kept run under <jobs_dir>/ (the run_dir name a persisted result carries). A
delegate job resumes its one session (else re-injects the prior prompt + answer); a consensus panel
resumes each voice's session and re-aggregates under the recorded strategy; a debate resumes each seat's
session and argues rounds MORE rounds (rounds is ignored for the other kinds). The parent's record
supplies the roster, model, working dir, role, files, and -- for a panel -- the strategy / stances /
per-seat steering, all inherited unless overridden here. A seat whose agent cannot reload its ACP session
is recorded as a failed voice, never silently dropped. The continuation is a fresh run linked to the
parent (continued_from) -- the parent is never mutated. The trust gate is re-applied fresh and defaults
to read_only (panels are read-only deliberation regardless). persist (default true) keeps the
continuation as its own durable child job. mode="async" runs it as a background job and returns a
job_id.
Inputs: job_id (required), prompt (required), mode, role, +9 more. Returns: result
debateA
Have several ACP agents argue a question across rounds and return the full transcript.
targets is a list of {cli, model} objects or cli / cli:model strings; a debate needs at least
two. Or name a saved panel (with optional panel_overrides) for a stored roster instead of targets;
they are mutually exclusive (rounds / judge stay call args). Each voice keeps ONE persistent ACP
session across all rounds: round one is each voice's
independent answer, and each later round shows a voice the others' latest positions and asks it to
revise -- the agent remembers its own prior reasoning in-session, so only the delta is sent.
carry_forward=true instead re-sends the FULL prior transcript verbatim each round (for a weaker session
memory; bounded by time_budget_s). track_convergence=true asks each voice for a one-word verdict each
round and stops early when the panel CONVERGES (a unanimous verdict) or STALLS (the decision holds for the
configured tolerance); the outcome field reports the termination reason (converged / stalled /
unresolved / budget / quorum_lost) and the final decision.
synthesize=true (default) adds a closing summary; judge names a target to write it. A debate is
read-only deliberation: a safety_mode beyond read_only (propose / write / yolo) is refused --
the voices run on persistent sessions in the working directory with no per-turn sandbox -- so route write /
propose work through delegate (a single agent isolated in a worktree sandbox). role names a
persona (see list_roles) prepended to the opening prompt every voice argues from. effort (low |
medium | high | xhigh | max) asks every voice to spend more reasoning where it has a knob; max is
accepted and clamped to each agent's ceiling. time_budget_s is a wall-clock deadline for the WHOLE
debate enforced at round boundaries: a round still in flight at the deadline is cut and the transcript
so far is finalized (stop_reason="budget", with a rollup); on_budget is harvest | continue | resume
(default default_on_budget; continue runs every round to completion). persist keeps the debate as a
durable job (F2): a parent state.json plus the full transcript.md; None follows
default_persistence, true / false force it. mode="async" runs the debate as a background job and
returns a job_id (poll with job_status / job_result); mode="sync" awaits it.
Inputs: prompt (required), mode, role, judge, +16 more. Returns: result
delegateA
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). direct_workspace_mutation=true asks for a write/yolo agent to edit
working_dir itself, with live terminal access there, instead of an isolated worktree/temp copy — no
diff is captured and nothing is applied back, so the run leaves no record of what it changed. Asking is
not enough: the operator must have set allow_direct_workspace_mutation in config, working_dir must
be explicit and on the configured trusted_workspaces allowlist (trust_workspace=true does NOT
qualify), and it is refused inside a delegation chain. propose cannot use it at all (INVALID_INPUT).
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 | max)
asks the agent to spend more reasoning where it has a knob (codex via an advertised model[tier] id or a
confirmed reasoning_effort config option; cursor via the model id; cline via --thinking; junie via env).
max is accepted and clamped to the agent's ceiling where it has one. 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.
Inputs: cli (required), prompt (required), mode, role, +13 more. Returns: result
discoverA
Find installed ACP agents via the community registry and propose [agents.<id>] config for them.
The registry-driven companion to setup/doctor. It fetches the ACP agent registry (cached under
~/.rutherford/acp-registry.json for offline use), detects which registry agents are ALREADY installed
here -- scanning PATH plus curated install dirs (~/.local/bin, ~/.cargo/bin, ~/.<vendor>/bin),
never downloading or running npx -- and (with probe=true, the default) drives each found agent with a
real read-only ACP round trip so the proposal only includes ones that actually answer. Returns the
discovered agents and a proposed [agents.<id>] config block for the new drivers. write=true appends
that block to the config for scope (project -> <cwd>/.rutherford/config.toml, global -> the
platform path), creating the file if needed and never overwriting an existing section. refresh
re-fetches the registry. Use this to adopt an ACP agent (or bridge) Rutherford does not ship as a built-in.
Inputs: probe, scope, write, refresh. Returns: result
doctorA
Probe each agent (or one named agent) with a real read-only ACP round trip and report conformance.
The trustworthy health check for ACP agents: whether each spawns, handshakes, and answers. Each report
is ok / no_answer / model_unavailable / handshake_failed / auth_required / not_installed / error.
auth_required means the agent is installed but holds no credential yet; sign it in once with login.
model_unavailable means spawn + handshake succeeded (the agent is reachable) but the harness/provider
rejected the model on the turn (a model/provider config issue, e.g. a Claude Code on AWS Bedrock / Vertex),
so it is NOT reported as a broken agent. Slower than capabilities (it makes a real call per agent); run it
to see which of the roster actually drive on this machine. connect_only runs the lighter handshake-only
check (spawn + handshake, no prompt) and reports reachable / handshake_failed / auth_required /
not_installed plus each agent's advertised models -- it shows whether Rutherford can
talk to and configure an agent even when a model call would fail for a reason outside ACP (an auth /
entitlement / quota issue, e.g. Grok without a SuperGrok subscription).
When an agent (codex / claude_code / pi) launches a separate npm ACP adapter shim and that shim is not
installed but its underlying CLI is (you have codex/claude/pi), the report adds an install_hint
with the exact npm i -g <package> command instead of a flat not_installed -- run that, or
setup install_adapters=true, to set the adapter up.
Inputs: agent, timeout_s, connect_only. Returns: result
job_resultA
Return a finished background job's result envelope -- identical to the sync tool's envelope.
A succeeded job returns its stored result verbatim; a failed job returns its error; a cancelled
or still-running job returns a structured error (poll job_status and retry); an unknown id is
JOB_NOT_FOUND.
Inputs: job_id (required). Returns: result
job_statusA
Report one background job's status and timings (no heavy result); JOB_NOT_FOUND if the id is unknown.
status is pending | running | succeeded | failed | cancelled. Poll this, then call job_result once
the job is succeeded (or to read the failure of a failed / cancelled job).
Inputs: job_id (required). Returns: result
list_jobsA
List the background jobs Rutherford is tracking (id, tool, status, summary, timestamps), newest first.
The light listing -- no heavy result. Fetch a finished job's result with job_result. Jobs are
in-memory: a finished one is evicted after job_ttl_s, and a restart clears them all.
Inputs: no named parameters. Returns: result
list_rolesA
List the available role personas (id, name, description) for the role param.
A role is a reusable system prompt; pass its id as role="<id>" to delegate / consensus /
debate and the persona is prepended to your prompt. Built-in roles ship with Rutherford; a
role_dirs directory can add or override one.
Inputs: no named parameters. Returns: result
loginA
Sign an ACP agent in once, for an agent that doctor reports as auth_required.
Some agents open no session until a client has called ACP authenticate, then cache the credential
themselves (Google Antigravity is one). No other Rutherford call signs in; this is the explicit place.
Without method it is read-only: status=authenticated if a session already opens, else
status=method_required with the agent's advertised methods (id, name, kind). With method (an
agent-kind id) it starts the sign-in as a background job, waits up to wait_s for the agent to print its
sign-in link, and returns status=pending with job_id and signin_urls -- give the first link to the
user, who completes the sign-in in a browser, then poll job_status / job_result. The job finishes
authenticated once a session opens with the new credential (lifting any cooldown bench), fails with
AUTH_FAILED (declined, the agent still refused a session afterwards, or no sign-in within timeout_s,
which bounds the authenticate step), or with the agent's own handshake error if the confirming session
fails for another reason. status=authenticated without method means a session opens and no ACP
sign-in is needed; a turn may still need a provider key, which doctor checks. A repeated login while
one is running returns that job (joined=true) instead of starting another (and status=cancelled if
cancel_job ends it while the call waits); a different method then is
refused with INVALID_INPUT until it finishes or is cancelled. The links are agent-authored, taken only
from sign-in prompt lines, and never opened by Rutherford. An env_var
method returns the variables to set (status=configure_env) and a terminal method returns the command to
run (status=run_in_terminal); Rutherford never asks for or stores a credential value.
Inputs: agent (required), method, wait_s, timeout_s. Returns: result
planA
Ask one ACP agent for an implementation plan for goal under the architect persona (read-only).
A read-only delegate with the architect (planner) persona prepended: the agent designs an approach
rather than implementing it. cli is an agent id (see capabilities); model is optional. files
lists paths to put in scope. Always read-only -- planning never mutates the tree; implementing the plan
is delegate in write mode.
Inputs: cli (required), goal (required), role, files, +3 more. Returns: result
reload_panelsA
Re-read saved panels from disk (after editing a panels.toon) and list those now available.
Returns {reloaded, count, panels: [{name, description, target_count}]}. Panels are discovered under
~/.rutherford/panels.toon, the project .rutherford/panels.toon, and $RUTHERFORD_CONFIG_DIR, merged
by name (closest scope wins). A malformed panels file raises PANEL_INVALID naming the file and seat.
Inputs: no named parameters. Returns: result
reviewA
Review a diff or a set of files across one or more ACP agents (read-only). Provide diff or paths.
A read-only consensus under the principal-reviewer persona: each agent reviews the code and the
panel returns every voice plus a combined verdict. targets is a list of {cli, model} objects (or
cli / cli:model strings); or name a saved panel (with optional panel_overrides) instead -- the
two are mutually exclusive. Provide diff (a unified diff, inlined into the prompt) or paths (files put
in scope for the agents to read). synthesize defaults on (the combined verdict); pass false for the
raw per-voice reviews. Always read-only -- a review never mutates the tree.
Inputs: diff, role, panel, paths, +5 more. Returns: result
setupA
Show where config lives and scaffold a starter config.toml; the first-run helper.
scope is project (<cwd>/.rutherford/config.toml) or global (the platform config dir's
config.toml). It returns the proposed starter content (the most useful settings at their effective
defaults) and the resolved path, plus a snapshot of the agents you already have. Pass write=true to
create the file -- it never overwrites an existing one (already_exists=true, written=false).
trust_workspace=true adds the current directory to trusted_workspaces so write/yolo delegations are
permitted there.
The adapters block reports agents whose underlying CLI is installed but whose npm ACP adapter shim is
not (codex needs codex-acp, claude_code needs claude-agent-acp, pi needs pi-acp -- what doctor
flags as not_installed with an install hint). Pass install_adapters=true to run npm i -g <package>
for each of those automatically (an explicit, opt-in machine change; off by default).
Inputs: scope, write, trust_workspace, install_adapters. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 19 tools
Most tools target clearly distinct operations: delegation, multi-agent consensus, debate, jobs, setup, and diagnostics. The main overlap is deliberate: `plan` is a specialized `delegate`, and `review` is a specialized read-only `consensus`, while `list_jobs`, `activity`, `job_status`, and `job_result` are closely related but explicitly contrasted in their descriptions.
All names use consistent snake_case with no camelCase or casing chaos. They are mostly readable and predictable, though some are bare verbs/nouns (`delegate`, `consensus`, `doctor`) rather than uniformly verb_noun.
Nineteen tools is borderline heavy for a single server, driven by separate surfaces for delegation, deliberation, jobs, agent diagnostics, setup, discovery, and panels. Each tool has a role, but the set is broad enough that an agent must navigate many adjacent operations.
The surface covers the main lifecycle well: agent discovery/authentication/health, roles, delegation, planning, consensus, debate, review, job polling/cancellation/continuation, and setup. Minor gaps remain around explicit panel CRUD/session management, but core workflows are not dead-ended.
Maintenance
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