mcp-airlock

Русская версия

mcp-airlock is a proxy you put between an AI agent and an MCP server when the server can do things you don't want an agent doing on its own. It speaks the 2026-07-28 revision of the protocol (the stateless one: no session, no initialize, one POST per request) and adds the parts the protocol leaves to you: who is allowed to call what, dry runs by default, a human in the loop for dangerous calls, an audit trail and tracing.

It is deliberately small. There is no UI, no policy language beyond flat YAML, no MCP SDK of its own. The whole proxy is one Starlette app plus a few helper modules.

How a call goes through

The agent sends a normal tools/call to the proxy instead of the server. The proxy:

1. Works out who is calling. That comes from a JWT (Authorization: Bearer) or, if you run it behind a gateway that already did the authentication, from an X-Airlock-Principal header. It is never taken from the request body. No principal, no call.
2. Looks the tool up in the policy. Tools that are not listed are refused. Listed tools have a risk tier per environment, so the same delete_service can be free in dev and gated in prod.
3. Depending on the tier:
   • L0 (read) goes straight through.
   • L1 (suggest) always goes through with dry_run: true, whatever the agent asked for.
   • L2 (confirm) goes through with dry_run: true first, and the result comes back to the agent as input_required with a description of what would happen and a signed requestState. When a person says yes, the agent repeats the call with that state and the proxy executes it for real, once. Repeating it again is refused.
   • L3 (auto) goes through as sent.
4. Checks the blast radius: how many objects one call touches (the length of a list argument you name in the policy) and how many a principal has touched in the last hour or day.
5. Forwards the call, cuts the response down to the output cap if it is too big, and marks anything in it that smells like a prompt injection. Marking only; it does not change what the agent gets to see.
6. Writes two audit records, one before the upstream call and one after, whatever happened.

Refusals come back as tool results with isError: true, not as protocol errors, so the model sees why and can do something else. Every result carries the verdict and the rule that produced it in _meta.

Running it

The released version, no clone needed:

uvx mcp-airlock --policy policy.yaml --upstream http://127.0.0.1:8080/mcp --env prod

The same as a container. The image listens on 0.0.0.0:9000, runs as a non-root user and writes audit.jsonl into /data:

docker run --rm -p 9000:9000 -v $PWD/policy.yaml:/data/policy.yaml \
  ghcr.io/shalimov04/mcp-airlock:0.2 --policy policy.yaml --upstream http://host.docker.internal:8080/mcp --env prod

From a checkout:

uv sync
uv run pytest
uv run python demo.py

The demo starts a fake upstream with a handful of tools on port 9001 and the proxy on 9000, walks through the interesting cases (refused tool, forced dry run, confirmation, replay, blast radius, output cap, injection marking) and leaves the audit log and spans in examples/.

The short version, recorded against that same fake upstream. An agent tries to delete a production service, gets a dry run and a confirmation prompt instead, the confirmation works exactly once, and a poisoned read result comes back flagged:

docs/make_demo_gif.py re-records it (uv run --with pillow python docs/make_demo_gif.py).

docs/clients.md shows how to point Claude Code and Cursor at the proxy and what the agent sees when a call is refused or held for confirmation.

Against a real server:

uv run mcp-airlock --policy policy.example.yaml --env prod \
    --upstream http://127.0.0.1:9001/mcp --audit audit.jsonl

There are ready-made policies for the GitHub, Grafana and Kubernetes MCP servers in examples/policies/. They were written against the servers' source at a pinned commit, so check them against your actual server before trusting them:

uv run airlock-policy lint examples/policies/github.yaml
uv run airlock-policy diff examples/policies/github.yaml --upstream http://127.0.0.1:8080/mcp --env prod

diff tells you which tools the server has that the policy doesn't mention, which policy entries the server no longer has, and which L1/L2 tools have no dry_run argument.

Configuration

Everything is environment variables. None are required for a single-process setup.

The policy file

version: 1
environment: prod
output:       { max_chars: 16000, chars_per_token: 4 }
blast_radius: { max_per_call: 50, max_per_principal: 500, window_s: 3600 }
tools:
  get_service:
    tiers: { dev: L0, staging: L0, prod: L0 }
    output: { max_chars: 5000 }
  set_replicas:
    description: scale services up/down (reversible)
    tiers: { dev: L3, staging: L1, prod: L2 }
    principals:
      "group:oncall": { prod: L3 }      # on-call people skip the confirmation in prod
    count_arg: names                     # objects per call = len(arguments.names)
    blast_radius: { max_per_call: 3, max_per_principal: 5, window_s: 3600 }
  delete_service:
    description: permanently delete a service (irreversible)
    tiers: { dev: L2, prod: L2 }         # nothing for staging, so it is refused there

A tier is resolved in this order: an entry for the exact principal, then the first matching group in the order the token lists them, then tiers[environment]. The description is what the person approving the call gets to read, so write it for them.

Rule ids you will see in _meta and the audit log: allowlist.deny, tier.unassigned, tier.L0.read, tier.L1.dry_run, tier.L2.confirm, tier.L2.confirmed, tier.L2.dry_run, tier.L3.auto, blast_radius.per_call, blast_radius.per_principal, dry_run.unsupported, catalog.unavailable, principal.missing, protocol.<code>, mrtr.pending, mrtr.declined, mrtr.replay, mrtr.expired, mrtr.mismatch, mrtr.bad_signature, mrtr.approved_oob, mrtr.upstream_input_required, internal.error.

Confirmations in detail

The confirmation token (requestState) is an HMAC-signed blob carrying the principal, the tool, a hash of the arguments, the environment, the upstream URL, a random idempotency key and an expiry (10 minutes). Nothing is stored when it is issued. When it comes back the proxy checks the signature, checks that all of those still match the call in front of it, re-runs the policy, burns the key, then charges the blast-radius counter. Burning is an atomic insert in the store, so two replicas cannot both execute the same confirmation. A decline burns the key too.

Before the prompt is issued the proxy asks the upstream for tools/list and looks at the tool's schema. If the tool declares dry_run, the dry run is forwarded and its output is included in the prompt. If it doesn't (most servers today), nothing is forwarded and the person is asked to confirm without a preview. L1 on such a tool is refused, since there is no safe way to run it. If the upstream cannot be asked at all, the call is refused with catalog.unavailable rather than guessed at. The tools/list answer is cached for as long as the upstream's ttlMs says, per principal; with ttlMs: 0 it is fetched on every gated call. If the tool mirrors dry_run into an Mcp-Param-* header, the proxy rewrites that header along with the body.