Skip to main content
Glama
sudo-hrmn

MCP-Gatekeeper

by sudo-hrmn

๐Ÿ›ก๏ธ MCP-Gatekeeper

Runtime Security Gateway & FastMCP Server for Model Context Protocol (MCP) Clients and Upstream Servers.

MCP-Gatekeeper is a defense-in-depth security 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"), SSRF exploits, and unauthorized high-risk operations.

Designed for instant deployment to fastmcp.cloud or local execution via fastmcp CLI.


๐Ÿš€ Key Features & Capabilities

  1. FastMCP Cloud Ready & Serverless Storage: Single-file FastMCP entry point (server.py) deployable directly to fastmcp.cloud with dynamic OS temp directory SQLite resolution (tempfile.gettempdir()).

  2. 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.

  3. SSRF Upstream Protection: Validates upstream URLs against forbidden hostnames, loopbacks (127.0.0.1, localhost), internal subnets (10.x.x.x, 192.168.x.x), and cloud metadata IPs (169.254.169.254).

  4. 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_KEY from .env, supporting OpenAI, Grok, DeepSeek, Anthropic, or local Ollama).

  5. Fail-Closed Security Design: Any classifier failure, network timeout, or unhandled exception defaults to blocking the payload and creating a security incident.

  6. Admin Authentication & Authorization Gate: Holds high-risk actions pending human approval (resolve_human_approval); requires valid admin_key matching ADMIN_API_KEY.

  7. Tamper-Evident Audit Trail: Every call, response, policy verdict, and admin decision is stored with SHA-256 hash chaining and hard query limits to prevent DoS attacks.

  8. Auth-Protected Admin Dashboard: Live HTML Admin Dashboard served directly via FastMCP at /dashboard?admin_key=YOUR_KEY.


Related MCP server: guardrails-mcp-server

๐Ÿ› ๏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["๐Ÿ›ก๏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)"]
            SSRF["SSRF Validator\n(Private IP / Metadata Shield)"]
            POL["Policy Engine\n(Allow/Block/Confirm/Rate-Limit)"]
            CONF["Confirmation Manager\n(Auth-Gated Approval)"]
            
            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?admin_key=..."]
    end

    subgraph External["Upstream Services & AI APIs"]
        UP["Upstream MCP Servers\n(GitHub, SQL, Web Search, APIs)"]
        LLM["LLM Classifier API\n(Groq / OpenAI / DeepSeek / Ollama)"]
    end

    subgraph Storage["Datastore & Audit"]
        DB[("SQLite DB (tempdir dynamic)")]
        AUDIT[("Tamper-Evident Audit Log\n(SHA-256 Hash Chained)")]
    end

    Clients -->|MCP SSE / stdio / JSON-RPC| S
    S --> SSRF
    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| AUDIT

Detailed Execution Flow & Security Pipeline

sequenceDiagram
    autonumber
    actor Client as AI Agent Client
    participant FastMCP as FastMCP Server (server.py)
    participant SSRF as SSRF & URL Shield
    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 / register_upstream
    FastMCP->>SSRF: 2. Validate URL safety (Block Private/Metadata IPs)
    alt Invalid Scheme or SSRF IP Target
        SSRF-->>FastMCP: SSRF Risk Detected
        FastMCP-->>Client: Return Error: Upstream URL rejected
    end
    
    FastMCP->>Base: 3. 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: 4. 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: 5. Create Pending Approval Request
        Gate->>Admin: Notify Admin on Dashboard
        Admin->>Gate: 6. Admin Approves / Denies (with admin_key)
        alt Denied, Missing Key, 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: 7. 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: 8. 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: 9. Return Verified Clean Response
        end
    else Stage 1 Clean
        FastMCP->>Audit: Write Hash-Chained Audit Entry
        FastMCP-->>Client: 9. Return Verified Clean Response
    end

๐Ÿ”‘ Environment Configuration

When deploying to FastMCP Cloud, configure these environment variables in your FastMCP Cloud Project Settings (or via .env for local execution):

# LLM Security Classifier API Key (Supports Groq, OpenAI, DeepSeek, Ollama)
LLM_API_KEY="your-llm-api-key-here"
LLM_API_URL="https://api.groq.com/openai/v1/chat/completions"
LLM_MODEL="allam-2-7b"

ADMIN_API_KEY="trust-gateway-admin-key-secret"
DATABASE_URL="sqlite+aiosqlite:///mcp_trust_gateway.db"
FAIL_CLOSED=true
CLASSIFIER_TIMEOUT_SECONDS=10.0
CONFIRMATION_TIMEOUT_SECONDS=60

โ˜๏ธ Live Deployment & Client Integration

1. FastMCP Cloud Deployment

This project is deployed live on FastMCP Cloud:

  • Live SSE Endpoint: https://mcp-gatekeeper-1.fastmcp.app/mcp

  • Admin Dashboard: https://mcp-gatekeeper-1.fastmcp.app/dashboard?admin_key=trust-gateway-admin-key-secret


2. Client Configurations

๐Ÿค– Google Antigravity & Claude Desktop (mcp_config.json)

{
  "mcpServers": {
    "mcp-gatekeeper": {
      "url": "https://mcp-gatekeeper-1.fastmcp.app/mcp"
    }
  }
}

๐Ÿ’ป Claude Code (CLI)

claude mcp add mcp-gatekeeper --transport sse \
  https://mcp-gatekeeper-1.fastmcp.app/mcp

๐Ÿงช Testing & Security Verification

Run the full pytest suite:

.venv/bin/pytest -v

Metrics Achieved

  • ๐Ÿ“Š Passed Unit & Security Tests: 12 / 12 passed

  • ๐Ÿ“Š Adversarial Catch Rate: 100%

  • ๐Ÿ“Š False Positive Rate: 0%


๐Ÿ“ Key Security Features & Design Decisions

  1. Fail-Closed Default: All ambiguous responses, classifier timeouts, network issues, or unapproved schema modifications fail closed (block action and alert admins).

  2. SSRF Prevention: All upstream URLs are sanitized to prevent internal port scanning and cloud metadata exfiltration (169.254.169.254).

  3. Auth-Gated Control Operations: Dashboard and approval actions (resolve_human_approval) require ADMIN_API_KEY verification.

  4. Credential Redaction: Secrets, API tokens, and passwords matching sensitive keys are automatically redacted before saving to audit storage.

  5. 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.

  6. Tamper-Evident Hash Chaining: Every log entry computes SHA256(actor | action | target | details | prev_hash | timestamp) ensuring non-repudiation and detection of log tampering.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

No tool schema history has been recorded yet.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    A
    maintenance
    Security gateway for MCP tool calls. Sits between your LLM client and MCP servers, enforcing per-tool policies (allow/block/approve/read-only), logging every call, and pausing dangerous operations for human approval in terminal or Slack.
    4
    1
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    MCP server for AI agent security guardrails. Provides input validation, prompt injection detection, PII redaction, output filtering, policy enforcement, rate limiting, and comprehensive audit logging.
    76
    1
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    A zero-trust security gateway for MCP tool calls, inspecting tool identity, arguments, execution decisions, and returned content before risk reaches your coding agent.
    Apache 2.0
  • A
    license
    Not graded
    quality
    D
    maintenance
    A defensive gateway and firewall for AI agents using MCP servers, scanning tool calls, responses, and manifests for prompt injection, secrets, dangerous commands, and drift before allowing execution.
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sudo-hrmn/MCP-Gatekeeper'

If you have feedback or need assistance with the MCP directory API, please join our Discord server