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Gsep MCP

AI agent security via MCP: C3 firewall, C4 immune system, C5 action guard, self-evolving prompts.

Install Gsep MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "gsep-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@gsep/mcp"
      ]
    }
  }
}

Settings β†’ Developer β†’ Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passedβ€” started and listed 10 tools correctly (3d ago).

Overview

Tools10

What Gsep MCP lets your agent do. Expand a tool for its parameters.

Verified live~1.9k tokens of context

Security & health

What it can reach and how reliably it runs β€” from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

11/11 checks healthy over the last 57d

What it can do

From the 10 tools this server returned when we ran it.

Runs commands Β· 1Can delete or modify data Β· 1Makes network requests Β· 1Read-only tools Β· 8
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceNot published
Package age183 days
Maintainers1

Observed 2026-11-05 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 15d ago via OSV.dev Β· @gsep/mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

npm downloads178Package downloads in the last 30 days.
Last commit5mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed10Callable tools this server registers over MCP.

From the project README

GSEP-MCP β€” AI Agent Security via Model Context Protocol

npm version License MCP Registry Powered by GSEP

The only MCP server that protects your AI agent instead of just extending it.

"me encanta saber que no borrarΓ‘ nada de mi pc" β€” First GSEP user, unprompted

Website Β· GSEP Core Β· npm Β· Discord


At a Glance

MetricValue
MCP Tools10
Prompt injection patterns (C3)53
Destructive action patterns (C5)80+
Behavioral immune checks (C4)6
Chromosome layers6 (C0–C5)
LLM providers supported5 (Claude, GPT-4, Gemini, Ollama, Perplexity)
Transport modes2 (stdio + HTTP/SSE)
Setup time< 2 minutes

What is GSEP-MCP?

There are 9,400+ MCP servers. All of them give your agent new tools β€” Notion, GitHub, Slack, databases.

GSEP-MCP is different. It gives your agent security, safety, and self-improvement β€” without writing a single line of code.

Code
OTHER MCP SERVERS          GSEP-MCP
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”       β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚  Give agent      β”‚       β”‚  Protect agent from          β”‚
β”‚  new tools       β”‚  vs   β”‚  prompt injection             β”‚
β”‚                  β”‚       β”‚  Block destructive actions    β”‚
β”‚  More features   β”‚       β”‚  Detect infected responses    β”‚
β”‚                  β”‚       β”‚  Self-evolving prompts        β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜       β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Works with: Claude Desktop, Cursor, Windsurf, Cline, Continue, n8n, Make, any MCP client.


Integrations

GSEP-MCP supports two transports: stdio (for desktop apps and IDEs) and HTTP (for servers, backends, and automation platforms). Pick the one that matches your environment.


stdio Transport (Desktop / IDE)

stdio is the simplest transport. The MCP client launches GSEP-MCP as a subprocess and communicates via stdin/stdout. No port, no server, no network.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

config.json
{
  "mcpServers": {
    "gsep": {
      "command": "npx",
      "args": ["-y", "@gsep/mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Restart Claude Desktop. Your agent is now protected.

Cursor

Add to .cursor/mcp.json in your project (or global ~/.cursor/mcp.json):

config.json
{
  "mcpServers": {
    "gsep": {
      "command": "npx",
      "args": ["-y", "@gsep/mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

config.json
{
  "mcpServers": {
    "gsep": {
      "command": "npx",
      "args": ["-y", "@gsep/mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Cline / Continue / Any MCP-compatible IDE

Add the same config block to your IDE's MCP settings file. GSEP-MCP is compatible with any client that implements the MCP protocol.

OpenClaw / Genome

config.json
{
  "mcpServers": {
    "gsep": {
      "command": "npx",
      "args": ["-y", "@gsep/mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GSEP_PRESET": "full"
      }
    }
  }
}

With Ollama (local models β€” no API key needed)

config.json
{
  "mcpServers": {
    "gsep": {
      "command": "npx",
      "args": ["-y", "@gsep/mcp"],
      "env": {
        "OLLAMA_HOST": "http://localhost:11434",
        "GSEP_PRESET": "full"
      }
    }
  }
}

HTTP Transport (Servers / Backends / Automation)

HTTP mode runs GSEP-MCP as a standalone server. Use this when your agent lives in a backend, a cloud service, or an automation platform.

Start the server:

bash
ANTHROPIC_API_KEY=sk-ant-... npx @gsep/mcp --http
# MCP endpoint:  http://localhost:3100/mcp
# OpenAI gateway: http://localhost:3100/v1/chat/completions
# Health check:  http://localhost:3100/health

Session model (v1.0.3+): Send initialize first β€” the server returns an mcp-session-id header. Include that header in all subsequent requests. Do not open a new connection per call.

OpenAI-Compatible Gateway

Gateway Mode lets existing OpenAI-compatible apps adopt GSEP by changing their baseURL. The server uses the LLM provider configured in its environment, then wraps every request in GSEP protection and evolution.

server.ts
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.GSEP_GATEWAY_KEY,
  baseURL: 'http://localhost:3100/v1',
});

const completion = await client.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [{ role: 'user', content: 'Refactor this repo safely.' }],
});

Supported endpoints:

  • GET /v1/models
  • POST /v1/chat/completions
  • POST /v1/responses

Streaming is intentionally rejected for now; use non-streaming calls until the streaming safety pipeline is implemented.

n8n

  1. Start GSEP-MCP server (locally or on Railway/Render)
  2. In your n8n workflow add an HTTP Request node:
    • Method: POST
    • URL: http://your-gsep-server:3100/mcp
    • Body (JSON):
    config.json
    {
      "jsonrpc": "2.0",
      "id": 1,
      "method": "tools/call",
      "params": {
        "name": "gsep_chat",
        "arguments": {
          "genome_id": "n8n-agent",
          "message": "{{ $json.message }}",
          "user_id": "{{ $json.userId }}"
        }
      }
    }
    
    • Header: mcp-session-id: {{ $json.sessionId }}

For n8n: initialize once at workflow start, store the mcp-session-id, and reuse it across nodes.

Make (Integromat)

Use the HTTP β†’ Make a request module pointing to http://your-gsep-server:3100/mcp with the same JSON-RPC 2.0 payload above.

Python (Django / FastAPI / Celery)

Install the MCP Python SDK:

Terminal
pip install mcp httpx
server.ts
# gsep_client.py
import asyncio
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

GSEP_URL = "http://localhost:3100/mcp"

async def gsep_chat(genome_id: str, message: str, user_id: str = "user") -> dict:
    async with streamablehttp_client(GSEP_URL) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool("gsep_chat", {
                "genome_id": genome_id,
                "message": message,
                "user_id": user_id,
            })
            return result

async def gsep_scan_input(content: str) -> dict:
    async with streamablehttp_client(GSEP_URL) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool("gsep_scan_input", {
                "content": content,
                "source": "user",
            })
            return result

In a Celery task:

server.ts
# tasks.py
from celery import shared_task
import asyncio
from .gsep_client import gsep_chat, gsep_scan_input

@shared_task
def process_message(genome_id: str, message: str, user_id: str):
    scan = asyncio.run(gsep_scan_input(message))
    if scan.get("blocked"):
        return {"blocked": True, "reason": scan.get("detections")}
    return asyncio.run(gsep_chat(genome_id, message, user_id))

Node.js / TypeScript Backend

Terminal
npm install @modelcontextprotocol/sdk
server.ts
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';

const client = new Client({ name: 'my-backend', version: '1.0.0' });
const transport = new StreamableHTTPClientTransport(new URL('http://localhost:3100/mcp'));

await client.connect(transport);

const result = await client.callTool('gsep_chat', {
  genome_id: 'my-agent',
  message: userMessage,
  user_id: userId,
});

console.log(result);

Deploy on Railway

  1. Create a new Railway service
  2. Set start command: npx @gsep/mcp --http
  3. Set environment variables:
Code
ANTHROPIC_API_KEY=sk-ant-...
GSEP_PRESET=full
GSEP_HTTP_HOST=0.0.0.0
GSEP_HTTP_PORT=$PORT
  1. Your Django/Celery service connects via Railway internal networking:
python
GSEP_URL = "http://gsep-mcp.railway.internal:$PORT/mcp"

Generic HTTP (any language)

Any HTTP client that supports JSON-RPC 2.0 works. The pattern is always:

Code
# Step 1 β€” Initialize (once per session)
POST /mcp
Content-Type: application/json

{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"my-client","version":"1.0.0"}}}

# Response includes header: mcp-session-id: <uuid>

# Step 2 β€” Call any tool (reuse session ID)
POST /mcp
Content-Type: application/json
mcp-session-id: <uuid from step 1>

{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"gsep_chat","arguments":{"genome_id":"my-agent","message":"Hello","user_id":"user-1"}}}

How It Works

Every message through your agent flows through the GSEP pipeline:

Reviews

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Frequently Asked Questions about Gsep MCP

gsep-mcp is an MCP server that protects AI agents with security, safety, and self-improvement capabilities. It helps detect prompt injection, block destructive actions, identify infected responses, and support self-evolving prompts through its C3 firewall, C4 immune system, and C5 action guard. It works with MCP-compatible clients and two transport modes.

Run gsep-mcp with npx using `npx -y @gsep/mcp`, then add it to your MCP client configuration. It supports clients including Claude Desktop, Cursor, Windsurf, Cline, Continue, n8n, Make, and other MCP-compatible applications.

Yes, gsep-mcp supports stdio and HTTP/SSE transports. Use stdio for desktop applications and IDEs, where the client launches the server as a subprocess, or HTTP for servers, backends, and automation platforms.

gsep-mcp provides a C3 firewall with 53 prompt-injection patterns, a C5 action guard with more than 80 destructive-action patterns, and a C4 immune system with six behavioral checks. It also supports infected-response detection and self-evolving prompts.

gsep-mcp supports Claude, GPT-4, Gemini, Ollama, and Perplexity. Ollama can be used with local models without an API key, according to the integration configuration.

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