Deploying MCP Servers

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Why deploy plain MCP servers?

• Standard MCP interface – Serve tools, resources, and prompts that any MCP client can consume.
• Managed runtime – Containers, TLS, load balancing, and scaling handled automatically.
• Security – Secrets vault, per-client API keys, optional unauthenticated mode for public registries.
• Observability – Consistent logging + OTEL pipeline and CLI integration (mcp-agent cloud logger tail).
• Upgrade path – When you are ready for long-running workflows, migrate the project to MCPApp without changing deployment tooling.

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Side-by-side capabilities

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FastMCP example

# main.py
from fastmcp import FastMCP

mcp = FastMCP("utilities")

@mcp.tool()
async def hash_text(text: str, algorithm: str = "sha256") -> str:
    """Generate a hash of text using hashlib."""
    import hashlib
    h = hashlib.new(algorithm)
    h.update(text.encode())
    return h.hexdigest()

@mcp.resource("utils://algorithms")
async def list_algorithms():
    return {"algorithms": ["md5", "sha1", "sha256", "sha3_512"]}

if __name__ == "__main__":
    mcp.run()

name: utilities-server
description: "Handy utility tools exposed over MCP"

execution_engine: asyncio           # No workflows required

mcp:
  servers:
    utilities:
      command: "uvx"
      args: ["python", "main.py"]

logger:
  transports: [console]
  level: info

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MCPApp-only example (no workflows)

from mcp_agent.app import MCPApp

app = MCPApp(name="markdown_tools")

@app.tool
async def to_title_case(text: str) -> str:
    return text.title()

@app.tool
async def count_words(text: str) -> int:
    return len(text.split())

if __name__ == "__main__":
    import asyncio
    asyncio.run(app.run())

name: markdown-tools
execution_engine: asyncio
logger:
  transports: [console]
  level: info

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Deploy the server

1. Authenticate (once per environment):
   mcp-agent login          # stores MCP_API_KEY
   
2. Deploy from the directory that contains main.py and mcp_agent.config.yaml:
   mcp-agent deploy utilities-server \
     --app-description "Utility functions as MCP tools"
   
   • Use --config-dir path/to/server if deploying from a monorepo.
   • Add --no-auth to expose a public endpoint (e.g., for ChatGPT Apps).
   • Use --non-interactive in CI/CD to reuse stored secrets.
3. Describe the deployment:
   mcp-agent cloud servers describe utilities-server
   
   Copy the Server URL for client integration.

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Configure clients

mcp-agent install https://<app_id>.deployments.mcp-agent.com/sse \
  --client cursor

mcp-agent configure https://<app_id>.deployments.mcp-agent.com/sse \
  --client vscode --write

mcp-agent install <server-url> --client chatgpt

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Operate and monitor

• mcp-agent cloud servers list – inventory of deployed servers.
• mcp-agent cloud logger tail utilities-server --follow – live logs.
• mcp-agent cloud servers delete utilities-server – tear down when finished.
• mcp-agent cloud configure --id <url> – collect user-specific secrets if needed.

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Upgrade path to full agents

# Initial FastMCP tool
@mcp.tool()
async def fetch_issue(repo: str, number: int) -> dict: ...

# Later: wrap inside MCPApp to reuse the tool and add workflows
from mcp_agent.app import MCPApp
app = MCPApp(name="issue_triage")

@app.tool
async def fetch_issue(repo: str, number: int) -> dict: ...

@app.async_tool
async def triage_issue(repo: str, number: int) -> dict:
    # Durable workflow that assigns, notifies, etc.
    ...

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Checklist for MCP server deployments

• Entrypoint (main.py) starts the server when executed.
• mcp_agent.config.yaml references the command + args.
• requirements.txt or pyproject.toml included.
• Secrets captured in mcp_agent.secrets.yaml (if needed).
• .mcpacignore excludes large/unnecessary files.
• Deployment tested locally with uv run main.py or npx @modelcontextprotocol/inspector.
• Documentation for consumers (URL, sample tool calls, auth mode).

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Resources

• Example repository: examples/cloud/mcp
• FastMCP documentation: github.com/jlowin/fastmcp
• Deployment quickstart →
• Authentication options →