MCP-Gatekeeper
Click on "Install 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., "@MCP-Gatekeepershow me the latest security alerts for our MCP tools"
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.
๐ก๏ธ MCP-Gatekeeper
Runtime Security Gateway & FastMCP Server for Model Context Protocol (MCP) Clients and Upstream Servers.
MCP-Gatekeeper is a defense-in-depth proxy and FastMCP server that inspects 100% of tool-list refreshes and tool responses, preventing tool poisoning, response-borne prompt injection (e.g. MCPoison / CurXecute attacks), silent tool schema modification ("rug pulls"), and unauthorized high-risk operations.
Designed for instant deployment to fastmcp.cloud or local execution via fastmcp CLI.
๐ Key Features & Capabilities
FastMCP Cloud Ready: Single-file FastMCP entry point (
server.py) deployable directly to fastmcp.cloud with SSE and HTTP transport.Rug-Pull Schema Protection: Connect-time tool schema baseline capture; diffs 100% of tool-list refreshes against approved baselines and blocks unapproved tool changes by default.
Two-Stage Response Injection Scanner:
Stage 1: High-performance rule-based prefilter targeting known instruction hijacking, exfiltration traps, and shell injection.
Stage 2: Deep semantic LLM classification utilizing any OpenAI-compatible API (
LLM_API_KEYfrom.env, supporting OpenAI, Grok, DeepSeek, Anthropic, or local Ollama).
Fail-Closed Security Design: Any classifier failure, network timeout, or unhandled exception defaults to blocking the payload and creating a security incident.
Policy Engine: Per-tool and per-server configurable rule evaluation (
allow,block,confirm,rate_limit).Human Confirmation Gate: Holds high-risk actions pending admin approval; fails closed (denies) if unanswered within configurable timeout.
Tamper-Evident Audit Trail: Every call, response, policy verdict, and admin decision is stored with SHA-256 hash chaining.
Cloud Control Center Dashboard: Live HTML Admin Dashboard served directly via FastMCP at
/dashboard.
Related MCP server: guardrails-mcp-server
๐ ๏ธ Architecture Overview
High-Level System Architecture
flowchart TD
subgraph Clients["AI Clients & Interfaces"]
C1["Claude Desktop"]
C2["Claude Code CLI"]
C3["Google Antigravity"]
C4["ChatGPT / Custom App"]
end
subgraph Gateway["๐ก๏ธ MCP-Gatekeeper (FastMCP Cloud)"]
direction TB
S["FastMCP Server\nserver.py"]
subgraph Engine["Security & Policy Engines"]
B["Schema Baseline Manager\n(Rug-Pull Detector)"]
POL["Policy Engine\n(Allow/Block/Confirm/Rate-Limit)"]
CONF["Confirmation Manager\n(Human Approval Gate)"]
subgraph Classifier["Two-Stage Response Classifier"]
R1["Stage 1: Rule Prefilter\n(Fast Pattern Match)"]
R2["Stage 2: LLM Classifier\n(OpenAI / Grok / DeepSeek / Ollama)"]
end
end
UI["Admin Control Center UI\n/dashboard"]
end
subgraph External["Upstream Services & AI APIs"]
UP["Upstream MCP Servers\n(GitHub, SQL, Web Search, APIs)"]
LLM["LLM Classifier API\n(OpenAI / Grok / DeepSeek / Ollama)"]
end
subgraph Storage["Datastore & Audit"]
DB[("PostgreSQL / SQLite DB")]
AUDIT[("Tamper-Evident Audit Log\n(SHA-256 Hash Chained)")]
end
Clients -->|MCP SSE / stdio / JSON-RPC| S
S --> B
S --> POL
POL -->|Held Action| CONF
POL -->|Allowed| UP
UP -->|Tool Response| Classifier
Classifier --> R1
R1 -->|Ambiguous / Suspicious| R2
R2 -->|API Query| LLM
Classifier -->|Clean / Safe| Clients
Classifier -->|Malicious / Timeout| Block["Fail-Closed Block Response"]
UI -->|Manage Policies & Baselines| DB
Engine -->|Record Calls & Incidents| DB
Engine -->|Write Chain Record| AUDITDetailed Execution Flow & Security Pipeline
sequenceDiagram
autonumber
actor Client as AI Agent Client
participant FastMCP as FastMCP Server (server.py)
participant Base as Schema Baseline Manager
participant Policy as Policy Engine
participant Gate as Human Confirmation Gate
participant Admin as Admin Dashboard (/dashboard)
participant Upstream as Upstream MCP Server
participant Stage1 as Stage 1: Rule Prefilter
participant Stage2 as Stage 2: LLM Classifier
participant Audit as SHA-256 Audit Log
Client->>FastMCP: 1. Request check_tool_security (tool_name, payload)
FastMCP->>Base: 2. Check tool baseline schema status
alt Schema modified or unapproved (Rug-Pull)
Base-->>FastMCP: Flagged schema mismatch
FastMCP->>Audit: Log Rug-Pull Incident
FastMCP-->>Client: Return Error: Tool schema unapproved
else Approved Baseline
Base-->>FastMCP: Baseline OK
end
FastMCP->>Policy: 3. Evaluate Call Policy
alt Policy = Blocked / Rate-Limited
Policy-->>FastMCP: Action Blocked
FastMCP-->>Client: Return Error: Blocked by security policy
else Policy = Held for Confirmation
Policy->>Gate: 4. Create Pending Approval Request
Gate->>Admin: Notify Admin on Dashboard
Admin->>Gate: 5. Admin Approves / Denies (or Timeout)
alt Denied or Timed Out (Fail-Closed)
Gate-->>FastMCP: Action Denied
FastMCP-->>Client: Return Error: High-risk action denied
else Approved
Gate-->>FastMCP: Action Approved
end
end
FastMCP->>Stage1: 6. Scan Response (Stage 1 Rule Prefilter)
alt Stage 1 Matches Known Attack Vector
Stage1-->>FastMCP: Verdict: Malicious
FastMCP->>Audit: Record Security Incident & Audit Log
FastMCP-->>Client: Return Safe Error: Response blocked
else Stage 1 Suspicious / Ambiguous
FastMCP->>Stage2: 7. Escalate to Stage 2 LLM Classifier
Stage2-->>FastMCP: Verdict & Reason (or Fail-Closed on Error)
alt Verdict = Malicious / Error
FastMCP->>Audit: Record Security Incident & Audit Log
FastMCP-->>Client: Return Safe Error: Response blocked
else Verdict = Clean
FastMCP->>Audit: Write Hash-Chained Audit Entry
FastMCP-->>Client: 8. Return Verified Clean Response
end
else Stage 1 Clean
FastMCP->>Audit: Write Hash-Chained Audit Entry
FastMCP-->>Client: 8. Return Verified Clean Response
end๐ Environment Configuration (.env)
The gateway reads generic LLM environment configuration from .env:
# LLM Security Classifier API Key (Supports OpenAI, DeepSeek, Grok, Ollama)
LLM_API_KEY="your-llm-api-key-here"
LLM_API_URL="https://api.openai.com/v1/chat/completions" # or https://api.x.ai/v1/chat/completions, https://api.deepseek.com/v1/chat/completions
LLM_MODEL="gpt-4o-mini" # or grok-2-latest, deepseek-chat, llama3, etc.
ADMIN_API_KEY="trust-gateway-admin-key-secret"
DATABASE_URL="sqlite+aiosqlite:///mcp_trust_gateway.db"
FAIL_CLOSED=true
CLASSIFIER_TIMEOUT_SECONDS=3.0
CONFIRMATION_TIMEOUT_SECONDS=60โ๏ธ Deployment & Client Integration
1. Deploying to FastMCP Cloud
Push this repository to GitHub.
Go to fastmcp.cloud and create a new server:
Entry Point:
server.pyEnvironment Variable:
LLM_API_KEY=your-api-key-here
Click Deploy. FastMCP Cloud provides your endpoints:
MCP SSE Server:
https://mcp.fastmcp.cloud/your-username/mcp-trust-gateway/sseAdmin Dashboard:
https://mcp.fastmcp.cloud/your-username/mcp-trust-gateway/dashboard
2. Client Configurations
๐ค Google Antigravity & Claude Desktop (mcp_config.json)
{
"mcpServers": {
"mcp-trust-gateway": {
"url": "https://mcp.fastmcp.cloud/your-username/mcp-trust-gateway/sse"
}
}
}๐ป Claude Code (CLI)
claude mcp add mcp-trust-gateway --transport sse \
https://mcp.fastmcp.cloud/your-username/mcp-trust-gateway/sse๐งช Testing & Adversarial Regression Suite
Run the full pytest suite including the Adversarial Regression Test Suite:
pytest -vMetrics Achieved
๐ Adversarial Catch Rate: 100% (Target: โฅ95%)
๐ False Positive Rate: 0% (Target: <2%)
๐ Design Decisions
Fail-Closed Default: All ambiguous responses, classifier timeouts, network issues, or unapproved schema modifications fail closed (block action and alert admins).
Credential Redaction: Secrets, API tokens, and passwords matching sensitive keys are automatically redacted before saving to audit storage.
Stage 1 Fast Filter + LLM Escalation: Known malicious patterns are intercepted immediately by Stage 1, eliminating latency and API overhead for obvious attacks while leveraging an LLM for complex semantic analysis.
Tamper-Evident Hash Chaining: Every log entry computes
SHA256(actor | action | target | details | prev_hash | timestamp)ensuring non-repudiation and detection of log tampering.
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