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Zero-Trust MCP Gateway

Put AI agents behindpolicy before execution.

Enforce identity, least privilege, deterministic risk, policy, and governed approvals before MCP tool calls reach downstream systems.

Enforcement stack

Every tool call earns the right to execute.

Gatekeeper sits between AI agents and MCP servers, evaluates the full security context, and forwards only allowed actions.

01

Identity & least privilege

Authenticate known agents, then enforce exact allow and deny permissions with least-privilege defaults.

02

Deterministic risk & policy

Score every request from tool metadata and workflow context, then apply priority-ordered, fail-closed policy.

03

Governed approvals

Route sensitive actions through role-bound approvals with escalation, timeouts, fallback reviewers, and controlled break-glass.

04

Exact approval binding

Bind approval to the reviewed tool, destination, arguments, environment, and security context—and allow one execution only.

05

Session-aware protection

Track sensitive-data access across an agent session and block unsafe external sends before downstream execution.

06

Simulation & audit

Dry-run proposed policy behavior with zero downstream actions and retain structured, redacted decision evidence.

Request path

One control plane. A deterministic decision.

Gatekeeper authenticates the agent, checks permissions and session state, scores risk, evaluates policy, and invokes approval workflows when required.

Read the architecture on GitHub
01AI agentMCP tool request
02GatekeeperIdentity → permission → context → risk → policy → approval
ALLOWDENYAPPROVE
03Downstream MCPAllowed calls only

V2 evaluation

Evidence, not security theater.

Results from the repository’s generated deterministic evaluation report. Blocked paths recorded zero downstream execution.

12/12evaluation scenarios correct
0unauthorized downstream executions
0replay successes
0approval-bypass successes
0context-tampering successes
1/1binding mutations detected
0simulation downstream executions
1/1policy conflicts failed closed
0.0067 msmean deterministic evaluation latency
0.0072 msP95 deterministic evaluation latency

Latency measures the local in-process deterministic evaluator only; it excludes network and downstream tool latency.Inspect the full report ↗

Pricing

$0Free

Clone it. Run it. Evaluate it.

Gatekeeper is available free from the public GitHub repository, including the gateway, safe demo MCP servers, policy configuration, tests, and the V2 evaluation harness.

View on GitHub

Quick start

Run Gatekeeper locally.

  1. 1git clone https://github.com/Grevanur/gatekeeper-mcp.git
  2. 2cd gatekeeper-mcp
  3. 3python3 -m venv .venv && source .venv/bin/activate
  4. 4python -m pip install -e '.[dev]' && cp .env.example .env
  5. 5uvicorn app.main:app --reload

Then open http://127.0.0.1:8000/docs for the local API documentation.

Scope, stated plainly

What V2 does not claim.

The current deterministic evaluation does not prove resistance to prompt injection, malicious downstream-server internals, covert channels, compromised hosts or approver accounts, enterprise identity federation, or distributed consistency failures.