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Gsep MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "gsep-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "gsep-mcp"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

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:

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about Gsep MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "gsep-mcp": { "command": "npx", "args": ["-y", "gsep-mcp"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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