mcp-sandbox

mcp-sandbox

A public MCP server providing test fixtures for developers and QA, with a single tool 'identity' that returns a unique canary string to verify the server's response.

Category
Visit Server

README

mcp-sandbox

A free, open MCP server for developers and GenAI evaluations.

Every tool returns a stable, improbable canary token โ€” something a model would never volunteer on its own. If the token shows up in a response, the call really reached this Model Context Protocol (MCP) server. If it doesn't, the tool was never invoked. That makes it a clean fixture for learning MCP, wiring up clients, and asserting on tool calls in CI and evals.

๐ŸŒ Live server: https://mcp.heptafox.com/mcp ย ยทย  ๐Ÿ“– Guide: mcp.heptafox.com


What is mcp-sandbox?

A public MCP server of deterministic test fixtures. Each tool returns a fixed canary string instead of real data, so you can prove a response originated from the server rather than the model's training or context.

Why use it?

When you're building or testing an agent, you usually don't want to point it at a production MCP server. You want a deterministic endpoint that always returns a known value. mcp-sandbox is that endpoint:

  • ๐ŸŽ“ Learn MCP โ€” connect a real server in minutes, no API keys or data setup.
  • ๐Ÿ”Œ Test clients โ€” confirm your client actually calls tools and relays output.
  • โœ… Verify integrations โ€” prove the MCP layer works end to end.
  • ๐Ÿšฆ Smoke tests โ€” a one-call health check for CI/CD pipelines.
  • ๐Ÿงช Automated evals โ€” assert on a stable token, not on fuzzy model prose.

Who is it for?

Developers learning MCP ยท AI application & agent-framework developers ยท QA engineers ยท GenAI evaluation frameworks ยท CI/CD integration testing.

Supported MCP clients

Works with any MCP-compatible client over streamable HTTP, including:

Claude Desktop ยท Claude Code ยท Cursor ยท Google ADK ยท LangChain ยท CrewAI ยท OpenAI Agents SDK ยท MCP Inspector

Features

  • โœจ Stable canary tokens โ€” deterministic output you can assert on exactly.
  • ๐ŸŒ Streamable HTTP transport โ€” the modern MCP transport, endpoint /mcp.
  • ๐Ÿ”‘ Bearer-token auth check โ€” a tool to verify auth reached the server.
  • ๐Ÿชถ Zero setup โ€” no API keys, no accounts, no data.
  • ๐Ÿ†“ Free & open source โ€” Apache-2.0.

Learning progression

The tools are ordered so you can climb from "is it connected?" to "did auth arrive?" one concept at a time:

Level Tool Auth Params Teaches
1 identity No No Connectivity
2 echo No Yes Passing parameters
3 auth_check Bearer No Authentication

Tools

identity โ€” connectivity check

No params. Returns a fixed ID string. Show it verbatim to confirm the server answered.

MCP-SANDBOX canary 7Q9X-ZK42-VORTEX-PARSNIP

echo โ€” parameter passing

Params: message: str, repeat: int = 1. Sends your text through the server and returns it inside a fixed wrapper, repeated repeat times.

MCP-SANDBOX echo 4F7T-ECHO-START >>> your text <<< 4F7T-ECHO-END

auth_check โ€” authentication

No params (reads the Authorization header). Verifies the server saw your Bearer token.

MCP-SANDBOX auth OK 9B3K-AUTHED-MARIGOLD      # valid token
MCP-SANDBOX auth FAIL 9B3K-NOAUTH-CINDER      # missing/invalid token

Authentication

auth_check is gated by a public development Bearer token:

Authorization: Bearer mcp-sandbox-dev-C7H2-KPMERCHANT

This is a shared smoke-test token for integration testing โ€” it is public and is NOT a production credential. It only gates auth_check.

Quick Start

Use the hosted server

Claude Code โ€” one command:

claude mcp add --transport http mcp-sandbox https://mcp.heptafox.com/mcp \
  --header "Authorization: Bearer mcp-sandbox-dev-C7H2-KPMERCHANT"

Claude Desktop โ€” config is stdio-only, so bridge through mcp-remote in claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-sandbox": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote", "https://mcp.heptafox.com/mcp",
        "--header", "Authorization: Bearer mcp-sandbox-dev-C7H2-KPMERCHANT"
      ]
    }
  }
}

Raw HTTP โ€” call the endpoint directly:

curl -X POST https://mcp.heptafox.com/mcp \
  -H "Authorization: Bearer mcp-sandbox-dev-C7H2-KPMERCHANT" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"identity","arguments":{}}}'

Endpoint is /mcp โ€” no trailing slash. /mcp/ 307-redirects to /mcp, and some clients drop the POST body across the redirect. Always use /mcp.

Run it locally

uv sync
uv run server.py        # guide at http://127.0.0.1:8000/ , MCP at /mcp

Inspect it with the MCP Inspector โ€” point it at http://127.0.0.1:8000/mcp:

npx @modelcontextprotocol/inspector

Examples

Ask any connected model to call a tool, then assert on the token it relays:

Prompt: Call the mcp-sandbox identity tool and show me what it returned.

Expected: MCP-SANDBOX canary 7Q9X-ZK42-VORTEX-PARSNIP

If the token appears, the call reached this server. If the model answers without it, the tool was never invoked.

QA & GenAI Evaluations

In evals, assert on the raw tool result (result.content[0].text), not on the model's natural-language summary โ€” the token is stable, the wording isn't.

import asyncio
from fastmcp import Client

async def main():
    async with Client("https://mcp.heptafox.com/mcp") as c:
        result = await c.call_tool("identity", {})
        assert "7Q9X-ZK42-VORTEX-PARSNIP" in result.content[0].text

asyncio.run(main())

Roadmap

  • More beginner-friendly tools (error simulation, streaming, structured output)
  • OAuth and additional authentication examples
  • Client integration examples (Cursor, Google ADK, LangChain, CrewAI, OpenAI Agents SDK)
  • Ready-made evaluation scenarios and tutorials

Have an idea? Open an issue or discussion.

Contributing

Contributions are welcome โ€” especially beginner-friendly tools, client examples, and docs. New tools are just @mcp.tool functions in server.py, each returning a unique canary string. See CONTRIBUTING.md to get started.

Deploy your own

The hosted instance runs on AWS Lightsail (Ubuntu) behind nginx + Let's Encrypt. To self-host: uv sync, run via deploy/mcp-sandbox.service (systemd), front with deploy/nginx.conf + certbot --nginx -d <domain>, and open ports 80/443 only โ€” the app binds to localhost and nginx proxies /mcp in. Step-by-step below.

<details> <summary>Full deploy steps (AWS Lightsail, Ubuntu)</summary>

  1. Instance + DNS โ€” create an Ubuntu Lightsail instance, point your domain's A record at its static IP, and open ports 80 + 443 in the Lightsail firewall. Leave 8000 closed; the app listens on localhost only.
  2. App
    curl -LsSf https://astral.sh/uv/install.sh | sh
    git clone <repo> ~/mcp-sandbox && cd ~/mcp-sandbox && uv sync
    
  3. systemd โ€” copy deploy/mcp-sandbox.service to /etc/systemd/system/, then:
    sudo systemctl daemon-reload
    sudo systemctl enable --now mcp-sandbox
    
  4. nginx + TLS
    sudo apt install -y nginx certbot python3-certbot-nginx
    # deploy/nginx.conf is set for mcp.heptafox.com; copy to /etc/nginx/sites-available/,
    # symlink into sites-enabled/, then:
    sudo certbot --nginx -d mcp.heptafox.com
    sudo systemctl reload nginx
    

Verify: systemctl status mcp-sandbox and curl -i https://<domain>/mcp.

</details>

License

Apache License 2.0. By contributing you agree your contributions are licensed under Apache-2.0.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured