cli-bridge-mcp 0.1.1

Consult a council of AI CLIs (Gemini, GPT, Claude, Mistral, Qwen, Copilot, OpenCode…) from inside any MCP client — free and ban-safe.

English · Français · 简体中文 · Español · Português (BR) · 日本語 · Deutsch

cli-bridge

A Model Context Protocol server that lets your AI assistant call the other AI CLIs you already have installed.

No API keys · no token extraction · no Node · no daemon · stdlib + mcp only.

In one sentence

You're talking to one AI assistant. You've also installed and logged into others — Claude Code, Codex, Gemini, opencode, Ollama. cli-bridge connects them: when your assistant needs something it can't do alone, it asks one of the other CLIs and hands you the result.

The problem it solves

Whatever assistant you're using has hard limits. It can't read a 2M-token repo in one pass, can't see a screenshot, can't hand you a generated image, and can't check its own work without bias — but some other CLI on your machine can do each of those. cli-bridge is the bridge between them: it spawns the official CLI as a subprocess (exactly as you'd run it by hand — no keys, no token extraction) and returns the answer to your assistant.

The result: one assistant whose ceiling on every axis is the best tool in your toolbox, not the one you happened to open.

---

The 10-second demo

You're in Claude. Claude can't hand you an image. Codex can — natively with gpt-image-2, or by writing a script that renders one. For a precise layout like a social card, have it script and run it:

ask_build(lane="gpt", task="generate a 1200×630 social card to assets/card.png — write a script that renders it, then run it", zone="assets")
→ Codex writes assets/card.png · you get the path back, never a binary blob (artifact-return)

Your assistant just gained an ability it doesn't have. That's the whole idea — now scale it to giant-context reads, vision, parallel grunt-work, and independent cross-vendor verification.

(Codex generates the image with gpt-image-2, a real text-to-image model built into the CLI — counted against your ChatGPT plan's usage, no separate API key (image generation needs a paid plan; it's not on the Free tier). It comes back as a path, not a blob, because binaries travel by artifact-return, not the text channel. A build lane can also render charts, diagrams or SVGs by writing code, when that's the better fit.)

…and it delegates real work, safely

cli-bridge build <lane> "<task>" hands the job to another model running in a throwaway git worktree, then gives you back a diff — your repo is never touched until you apply it yourself.

---

What you get — the four levers

cli-bridge isn't one feature, it's four abilities your assistant gains. Get these and every tool below slots into place:

1. Borrow — reach a capability your assistant lacks (vision, a 1M-token context window, a file a coding agent generates, a model that's simply better at this).
2. Spread — when one subscription hits its limit, keep going on another lane you already pay for.
3. Offload — fan laborious, parallel grunt-work across cheap/free lanes while you build elsewhere.
4. Verify — have a different vendor family check the work, because a model can't catch its own blind spots. This is the one thing a single-vendor tool structurally cannot do.

---

What this unlocks

Each block: one sentence of when you reach for it, the exact call, and what you get back.

Borrow abilities your assistant doesn't have

Every CLI has a different superpower, and each runs non-interactively — so cli-bridge can spawn it. Borrow the one your host lacks (it must be installed + logged in):

Superpower	Which CLI has it	Borrow it when
Images	Codex (gpt-image-2, no API key — paid ChatGPT plan, not Free)	your host can't draw
Huge context	Gemini (1M-token window)	a file/repo won't fit your host's context
Fresh knowledge	Gemini (Google-Search grounding) · Grok (live web/X) ⚗️	beat a stale cutoff: "what's the current API of <lib>?"
Vision	Gemini (images=[…]) ⚗️	analyse a screenshot or diagram
A free second opinion	Gemini (free daily tier) · opencode · Ollama (local, $0)	a $0 cross-check
Generated files	any build lane → artifact-return	get a chart / PDF / diagram back by path
Video ⚗️	Grok (Imagine) — if your installed CLI exposes it (Veo isn't exposed by any official Gemini CLI extension)	you need a generated clip

ask_build(lane="gpt", task="generate a 1200×630 social card to assets/card.png", zone="assets")   # Codex image → file by path, no API key (paid ChatGPT plan)
ask_gemini(task="find the bug across ./src — read the files you need", cwd="path/to/repo")         # 1M-token context
ask_gemini(task="what's the current recommended API for <lib>? check the latest docs")            # fresh knowledge (Search grounding)
ask_gemini(task="what's wrong in this UI?", images=["screenshot.png"])                             # vision (experimental)

⚗️ = experimental / depends on the installed CLI's current build (e.g. Grok Build is beta) — verify with doctor deep.

Never stop working when you hit a limit

When your main subscription caps out mid-task. ask_cascade falls through to another lane you already pay for, skipping any lane that's cooled down after a quota/auth/timeout error.

ask_cascade(task="finish wiring this endpoint")   # cheapest→strongest; a cooled-down lane is skipped
ask_best(task="…", mode="deep")                   # let the router pick the most suitable available lane

Offload the grunt work — in parallel, and cheap

When the work is laborious but not hard (refactors, migrations, test coverage). Fan it out, journaled so a server restart resumes instead of restarting; delegate a build and keep working.

batch_run(tasks=[...], dry_run=true)                       # cost envelope first — nothing is spawned
batch_run(tasks=[...], max_calls=20, max_credits=2.0)      # then run under a hard budget (resumable)
ask_build(lane="opencode", task="add the landing page", zone="frontend", mode="direct", async=true)   # delegate, keep building
job_tail(job_id="…")  ·  build_steer(job_id="…", instruction="use Tailwind, not inline CSS")

Break self-confirmation — the 2026 problem one vendor can't solve

When you need to trust a result. A model reviewing its own work (or a sibling's) just confirms its own blind spots. cli-bridge puts a different model family in the reviewer's seat.

workflow(preset="jury", task="is this migration safe?", author_lane="gpt")            # cross-family vote, fail-closed
workflow(preset="verify_repair", task="add retry with backoff",
         builder_lane="gpt", verifier_lane="gemini")                                   # A builds, B reviews, loop to green
security_review(base="origin/main")   ·   review_diff(base="origin/main")              # OWASP, severity-ranked

Get a real second opinion

When you've reached a conclusion and want it pressure-tested, or several models side by side.

challenge(task="I'm dropping the cache layer — here's why: …")                         # one skeptic attacks it
consensus(task="which migration strategy is safest here?")                             # N answer, peer-rank the best
workflow(preset="fanout_compare", task="fix this failing test", lanes=["gpt","gemini","opencode"])

---

The toolbox

~30 tools, grouped by intent — the headline ones:

• Consult (read-only): ask_<lane> (one model), ask_all (every free lane in parallel + a disagreement score), ask_cascade (resilient fall-through), ask_best (router), consensus, challenge.
• Build (opt-in write): ask_build — mode=isolated→diff · mode=direct→zone-guarded · async→steerable, behind an executable Definition-of-Done gate.
• Review & verify: review_diff, security_review, debate, and workflow(preset=…) — jury (cross-family k-of-N vote, fail-closed), verify_repair, fanout_compare, …
• Orchestrate & operate: batch_run (durable, budget-capped fan-out), plus usage_report, rate_lane, lane_stats, set_lane_cost, doctor.

Full reference — every tool, every flag: docs/TOOLS.md (or cli-bridge --help). Run CLI_BRIDGE_LEAN=1 for a curated ~12-tool surface.

There's also a human CLI — cli-bridge doctor|ask|build|review-diff|eval|… — the same engine from your terminal or CI (--json everywhere); cli-bridge build <lane> "<task>" returns a reviewable diff without touching your repo.

---

What you actually get when you combine them

One assistant whose ceiling on every axis is the ecosystem's best — not the tool you opened this morning: code with the strongest model, read ~1M tokens when yours is too short, answer with fresh knowledge past a stale cutoff, generate images/video, see screenshots, and fall back to a free/local lane when you're capped — spread across the subscriptions you already pay for.

The emergent property no single CLI has: true cross-vendor control — a different vendor in the reviewer's seat. Same-family subagents (Claude Code's, Grok's) can only self-confirm.

The honest seam: this unites capabilities, not mind — stateless spawns (no shared memory), spawn latency/cost, uneven quality, and the host always drives. It's orchestration, not fusion: you conduct specialists, you don't get one brain with every power.

→ Per-CLI strengths & limits (dated, churns fast): docs/COMPARISON.md.

Why cli-bridge (and not another "call other models" MCP)

• 🛡️ Ban-safe by design. It spawns each model's official CLI, exactly as you'd run it by hand — no OAuth-token extraction, no API-key reuse. Each CLI handles its own auth and billing.
• 💸 Cost-safe defaults you tune to your plan. Out of the box ask_all / ask_cascade build a free council and never touch paid quota unless you ask. Each lane ships a tier sourced from the vendor's published plans (dated in docs/COSTS.md, never detected from your account); override per lane with CLI_BRIDGE_<LANE>_COST=free|limited|paid.
• 🔌 Works from any host. Claude Code, Codex, opencode, Cursor, VS Code (Cline/Continue), Zed — anything that speaks MCP over stdio. The host's own lane is kept out of fan-out; hide it with CLI_BRIDGE_HIDE_HOST=1. Even a local model can be the host — see examples/local-first-host.md.
• 🧭 The cross-vendor edge is the moat. Independent verification means a different vendor in the reviewer's seat — the scarce thing as AI writes a larger share of code, and exactly what a single-vendor tool can't offer.

---

How it works

host (Claude/Codex/…) ──MCP──> cli-bridge ──spawn──> official CLI ──> model
                                    │
       keeps the host's own lane out of fan-out · only shows installed, enabled CLIs
       kills the whole process tree on timeout/cancellation · redacts secrets
       classifies errors (auth/limit/failed) · spills huge output to a file

No network calls of its own. No keys stored. It runs the same binaries you already trust, in your working directory, and hands the answer back.

Real run (2.2× speed): the Verify lever — security-review fans OWASP roles across free models in parallel (claude/gpt/opencode/ollama here); they flag a committed auth bypass blocker, and usage shows the receipts.

---

Writing code safely: two modes

Writes are contained, two ways — you pick review-gated or hands-off:

• isolated (default). Edits in a throwaway git worktree and hands back a diff. Your working tree is never touched.
• direct. Writes real files, but only inside a zone you declare, behind a per-zone lock with a post-turn zone-violation check. You in backend/, a delegate in frontend/, concurrently — neither can scribble across your whole repo; undo is zone-scoped, never a global reset.

Delegate re-entry is depth-capped (CLI_BRIDGE_MAX_DEPTH, default 1) so a misconfigured delegate can't fork-bomb the council.

---

Installation (≈5 min)

Prerequisites

• Python 3.10+ and uv (uvx ships with it): curl -LsSf https://astral.sh/uv/install.sh | sh (macOS/Linux) · winget install astral-sh.uv (Windows).
• At least one AI CLI installed and logged in — that's what cli-bridge borrows. Have any of: Claude Code, Codex, Gemini CLI, opencode, Ollama (local, $0). You consult only the lanes you already have.

1. Check what cli-bridge can see (no install — uvx fetches, runs, discards):

uvx --from cli-bridge-mcp cli-bridge doctor

doctor lists which CLIs are detected, their resolved paths, and cost tiers. doctor deep validates each lane against its own --help.

2. Add it to your MCP host. cli-bridge is an MCP server — it runs inside your assistant, not by hand. Point the host at the same command:

• Claude Code (writes the config for you):

  claude mcp add cli-bridge -- uvx cli-bridge-mcp
  

• Any other host (Codex, Cursor, VS Code, Zed, …) — add to its MCP config (~/.claude.json, .mcp.json, or the host's equivalent):

  {
    "mcpServers": {
      "cli-bridge": {
        "command": "uvx",
        "args": ["cli-bridge-mcp"]
      }
    }
  }
  

  Full example with env vars: examples/mcp.example.json.

Note: cli-bridge-mcp (the MCP server) is the host entry point; cli-bridge (no -mcp) is the human terminal CLI you ran for doctor in step 1.

3. Use it. Restart/reload your host and ask it to consult a lane — e.g. "use cli-bridge to get a second opinion from gpt" or "ask gemini to read ./src and find the bug".

Lanes

Built-in: Claude Code, Codex, Gemini (+ Antigravity agy), Mistral (Vibe), opencode, Ollama (local models, $0, offline), Qwen Code, Copilot, Grok.

Local runtimes beyond Ollama — LM Studio · MLX · llama.cpp — ship as zero-code recipes: point CLI_BRIDGE_LANES_FILE at examples/lmstudio.lane.json, mlx.lane.json, or llamacpp.lane.json. (Several local runtimes of the same open weights give correlated answers — real council diversity comes from distinct vendors, not a second local runtime.)

Community lanes (examples/community-lanes.json, experimental + limited until you declare their cost): Aider, Goose, Plandex, Amp, Crush, Amazon Q Developer CLI, Droid.

Anything else is ~3 lines of JSON. Add a custom lane, or wrap any OpenAI-compatible endpoint by spawning curl (key kept inside curl, never in argv). See examples/ for recipes.

---

The honest part

"More models = better" is fragile — big models share training data, so their errors correlate. We measured our own central claim (cli-bridge eval, no LLM judge): a diverse council did not catch more bugs than one strong model — it cut the false alarms ~2×. Same catch rate, far less noise — which is exactly what keeps a reviewer trustworthy instead of muted. Precision is the product, not recall. The harness ships, so you can confirm it on your CLIs — numbers either way in docs/BENCHMARKS.md.

---

Known limitations

• Ban-safe = no token/key extraction, not a blanket guarantee — non-interactive use of a provider's CLI isn't formally sanctioned everywhere and can change. Use your own accounts within their terms.
• Async jobs are in-process — a server restart marks running jobs interrupted. batch_run / workflow are the exception: they journal each task and resume via resume_id.
• The injection guard is heuristic — it catches high-signal patterns, not everything; treat delegate output as data, not instructions.
• Token/credit figures are estimates (chars/4 + your CREDITS_PER_1K), never exact.
• Cost tiers are sourced defaults, not detection — vendor-plan facts are dated; doctor warns when the snapshot is stale.
• Experimental (qwen, copilot, grok, community lanes, Gemini images=): flags aren't verified live — doctor deep checks them against each CLI's --help on your machine.

---

Roadmap

See CHANGELOG.md for shipped history. Currently exploring (not shipped): an independent-oracle verify mode (a cross-family lane writes tests from the spec, blind to the implementation, so the test catches the bug instead of mirroring it) and tighter limit-aware failover. Big inter-agent "bus" ideas (recursive spawn, shared state, wire protocol) are positioned honestly as a direction, never sold as a shipped protocol — see docs/ARCHITECTURE.md.

---

References

The design choices above aren't vibes — each maps to a finding in the literature. Every entry was checked against its source (authors + venue), because a tool that sells "honest cross-vendor verification" should get its own citations right.

Paper	ID	What it backs here
Du et al. — Improving Factuality and Reasoning via Multiagent Debate	2305.14325	debate: models critiquing each other beat one model alone
ReConcile — Round-Table Conference Improves Reasoning	2309.13007	debate convergence + confidence-weighted consensus
Mixture-of-Agents	2406.04692	layered aggregation across diverse models (and its limits)
Chain-of-Agents	2406.02818	role-specialized multi-agent pipelines
CriticGPT — LLM Critics Help Catch LLM Bugs	2407.00215	review_diff / security_review: an LLM critic catches bugs humans miss
Perez et al. — Discovering Language Model Behaviors (sycophancy)	2212.09251	why a same-family judge is weak → cross-vendor jury + peer anonymization
Wynn, Satija & Hadfield — Talk Isn't Always Cheap	2509.05396	debate failure modes → fail-closed verdicts, bounded rounds
CONSENSAGENT — Consensus via Sycophancy Mitigation (Findings of ACL 2025)	ACL 2025	sycophancy in consensus → "earn their seat" / anonymized peers
Maryanskyy — When Agents Disagree: The Selection Bottleneck	2603.20324	consensus: selection > synthesis (the deterministic peer-vote default)

A citation hygiene note. Talk Isn't Always Cheap (2509.05396) is Wynn, Satija & Hadfield — a popular council framework miscites it as "Xiong et al." We double-check attributions before repeating them, and flag it because honesty is the whole pitch.

Development

uv venv && uv pip install -e . pytest pytest-asyncio
pytest -q          # unit + integration (cross-host) tests; no real CLI or network needed

License

MIT

---

_{one side · bridged to a council}