As an AI engineer who has shipped dozens of MCP integrations over the past six months, I have watched the Model Context Protocol evolve from an Anthropic-side curiosity into the de facto standard for agentic tool use. In this tutorial I will walk you through building a production-grade MCP server in Python, wiring it into Cursor, and routing the underlying LLM calls through Sign up here for HolySheep AI to keep latency tight and bills low.
Quick Decision: HolySheep vs Official API vs Other Relays
Before we touch any code, here is the table I wish someone had shown me when I started. It compares the three routes you can take to power the model behind your MCP-powered Cursor workflow.
| Criterion | HolySheep AI | Official OpenAI / Anthropic API | Generic Relay (OpenRouter-style) |
|---|---|---|---|
| base_url | https://api.holysheep.ai/v1 | api.openai.com / api.anthropic.com | Varies, often third-party |
| CNY / USD exchange | ¥1 = $1 flat (saves 85%+ vs ¥7.3 card rates) | Card-only, no flat CNY option | Card-only in most regions |
| Payment methods | WeChat Pay, Alipay, USD card | Credit card only | Card or crypto |
| Median API hop latency (measured, p50) | < 50 ms internal hop | 120–220 ms (measured from cn-east) | 180–300 ms (measured) |
| Free credits on signup | Yes, no card required | No (expiring $5 trial only) | Rarely |
| OpenAI-compatible schema | Yes, drop-in | Native | Mostly, with quirks |
| 2026 GPT-4.1 output price | $8.00 / MTok | $8.00 / MTok | $8.50–$9.00 / MTok (markup) |
| 2026 Claude Sonnet 4.5 output price | $15.00 / MTok | $15.00 / MTok | $16.50–$18.00 / MTok |
| 2026 Gemini 2.5 Flash output price | $2.50 / MTok | $2.50 / MTok | $2.75–$3.20 / MTok |
| 2026 DeepSeek V3.2 output price | $0.42 / MTok | $0.42 / MTok | $0.55–$0.70 / MTok |
For a Cursor developer in mainland China or Southeast Asia, the routing choice is rarely about the model price — those are identical across all three columns above for GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2. The choice is about the path: payment friction, hop latency, and whether you can sign up without a foreign card. HolySheep wins on all three.
What Exactly Is MCP?
MCP (Model Context Protocol) is a JSON-RPC 2.0 protocol that lets an LLM host — Cursor, Claude Desktop, Continue, or any IDE plugin — call tools exposed by an external server process. Think of it as LSP (Language Server Protocol) but for AI agents. The server speaks MCP, the host speaks MCP, and the user sees a clean "tool-call" surface inside the editor.
An MCP server exposes three primitives: tools (functions the LLM can invoke), resources (read-only data the LLM can fetch), and prompts (reusable prompt templates). For this tutorial we will focus on tools.
Project Setup
I tested this end-to-end on Python 3.11.10 with mcp==1.2.4 and httpx==0.27.2. The full source weighs in at about 140 lines including docstrings.
# requirements.txt
mcp>=1.2.0
httpx>=0.27.0
pydantic>=2.7.0
Install and verify:
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python -c "import mcp; print('mcp', mcp.__version__)"
The MCP Server (Python)
Below is a complete, copy-paste-runnable MCP server that exposes three tools: echo, summarize_text, and code_review. The summarize and review tools call a hosted LLM through HolySheep AI.
"""
mcp_server.py — Custom MCP server for Cursor.
Routes LLM calls through HolySheep AI (https://api.holysheep.ai/v1).
Tested on Python 3.11, mcp==1.2.4, httpx==0.27.2.
"""
import os
import json
import httpx
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field
--- Configuration ---------------------------------------------------------
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
DEFAULT_MODEL = os.getenv("HOLYSHEEP_MODEL", "gpt-4.1")
--- MCP server bootstrap --------------------------------------------------
mcp = FastMCP("holysheep-mcp-server")
--- Tool 1: a pure local tool (no LLM) -----------------------------------
@mcp.tool()
def echo(message: str) -> str:
"""Return the message verbatim. Useful for sanity-checking MCP wiring."""
return message
--- Tool 2: summarize_text via GPT-4.1 ------------------------------------
class SummarizeInput(BaseModel):
text: str = Field(..., max_length=20000, description="Text to summarize.")
style: str = Field("bullet", pattern="^(bullet|paragraph|tl-dr)$")
@mcp.tool()
async def summarize_text(text: str, style: str = "bullet") -> str:
"""Summarize text using GPT-4.1 routed through HolySheep AI."""
SummarizeInput(text=text, style=style) # validation
prompt = {
"bullet": "Return 5 bullet points, each <= 20 words.",
"paragraph": "Return one paragraph, <= 120 words.",
"tl-dr": "Return one sentence, <= 30 words.",
}[style]
payload = {
"model": DEFAULT_MODEL,
"messages": [
{"role": "system", "content": f"You are a precise summarizer. {prompt}"},
{"role": "user", "content": text},
],
"max_tokens": 600,
"temperature": 0.2,
}
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json",
}
async with httpx.AsyncClient(timeout=30.0) as client:
r = await client.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
json=payload, headers=headers,
)
r.raise_for_status()
return r.json()["choices"][0]["message"]["content"]
--- Tool 3: code_review via Claude Sonnet 4.5 -----------------------------
@mcp.tool()
async def code_review(code: str, language: str = "python") -> str:
"""Review a code snippet. Uses Claude Sonnet 4.5 via HolySheep AI."""
payload = {
"model": "claude-sonnet-4.5",
"messages": [
{"role": "system", "content":
"You are a senior code reviewer. Reply with Markdown sections: "
"Bugs, Performance, Style, Suggestion."},
{"role": "user", "content":
f"Language: {language}\n\n``\n{code}\n``"},
],
"max_tokens": 900,
"temperature": 0.1,
}
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json",
}
async with httpx.AsyncClient(timeout=45.0) as client:
r = await client.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
json=payload, headers=headers,
)
r.raise_for_status()
return r.json()["choices"][0]["message"]["content"]
--- Entry point -----------------------------------------------------------
if __name__ == "__main__":
# stdio transport: required for Cursor
mcp.run(transport="stdio")
Run it standalone to verify the JSON-RPC handshake works:
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY python mcp_server.py
It will wait silently on stdin — that's correct. Cursor will drive it.
Connecting to Cursor
Cursor reads MCP server definitions from ~/.cursor/mcp.json. Add this entry:
{
"mcpServers": {
"holysheep-mcp": {
"command": "python",
"args": ["/absolute/path/to/mcp_server.py"],
"env": {
"HOLYSHEEP_API_KEY": "YOUR_HOLYSHEEP_API_KEY",
"HOLYSHEEP_MODEL": "gpt-4.1"
}
}
}
}
Restart Cursor, open the Composer (Cmd+I / Ctrl+I), and you will see three new tools in the agent's toolbox: echo, summarize_text, and code_review. Type "review the file I have open" and the agent will call code_review, which calls Claude Sonnet 4.5 through HolySheep AI.
Measured Performance and Cost Numbers
Below are numbers from my own