I still remember the first time our analytics lead asked me, "Can the LLM just run SQL for us?" That single Slack message kicked off a four-week sprint to ship our internal Model Context Protocol (MCP) gateway, and the journey from a brittle prototype to a production-grade tool-calling bridge against PostgreSQL is exactly what I want to walk you through today. In this tutorial I will show you how to spin up your own MCP server, wire it to a real Postgres instance, route every LLM call through HolySheep AI, and measure the result. If you have ever tried to bolt an LLM onto an existing database and watched the tokens explode or the latency spike, this guide is for you.

The Customer Story: Why We Rebuilt Our Tool-Calling Layer

Before we get into the code, let me give you the real-world context. The team I work with supports a Series-A cross-border e-commerce platform based in Shenzhen that runs inventory, pricing, and order analytics on top of a 14 TB PostgreSQL cluster. Their previous tool-calling setup relied on a direct OpenAI function-calling integration fronted by a hand-rolled proxy. Three pain points were killing them:

They moved to HolySheep AI as their LLM routing layer for two reasons: the rate of ¥1 = $1 (which gives them roughly an 85%+ saving versus the previous ¥7.3/$1 effective rate they were absorbing), and the fact that HolySheep exposes an OpenAI-compatible https://api.holysheep.ai/v1 endpoint that drop-in replaces the old base URL. WeChat and Alipay billing also made finance approval trivial, and the <50 ms edge latency made the cross-border round-trip bearable. Sign up here to grab free credits on registration and follow along with the same setup we used.

What MCP Actually Is, in 90 Seconds

MCP, the Model Context Protocol, is an open standard for letting an LLM client (Claude Desktop, Cursor, your own agent runtime) discover and invoke "tools" exposed by a remote server. Each tool is described by a JSON schema; the client sends arguments; the server runs them and returns structured results. In our case the server speaks the MCP protocol on one side and the PostgreSQL wire protocol on the other. The LLM never sees raw SQL — it sees typed tool calls like list_tables, describe_table, and run_readonly_query.

Architecture Overview

Our gateway has four moving parts:

Step 1: Project Skeleton

mkdir pg-mcp-gateway && cd pg-mcp-gateway
python -m venv .venv && source .venv/bin/activate
pip install "mcp[server]" fastapi uvicorn asyncpg httpx \
            opentelemetry-api opentelemetry-sdk \
            opentelemetry-exporter-otlp pydantic-settings python-dotenv

cat > .env <<'EOF'
HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
PG_DSN=postgresql://reader:[email protected]:5432/analytics
PG_READONLY_ROLE=llm_reader
LOG_LEVEL=INFO
EOF

Step 2: The PostgreSQL Adapter (Hard-Guarded Read-Only Path)

# pg_adapter.py
import os, asyncpg
from typing import Any

DSN = os.environ["PG_DSN"]
_pool: asyncpg.Pool | None = None

FORBIDDEN = ("insert","update","delete","drop","alter",
             "truncate","grant","revoke","create","copy")

async def init_pool() -> None:
    global _pool
    _pool = await asyncpg.create_pool(
        dsn=DSN, min_size=2, max_size=10, command_timeout=15
    )

async def close_pool() -> None:
    if _pool:
        await _pool.close()

async def list_tables() -> list[dict[str, Any]]:
    async with _pool.acquire() as conn:
        rows = await conn.fetch("""
            SELECT table_schema, table_name
            FROM information_schema.tables
            WHERE table_schema NOT IN ('pg_catalog','information_schema')
            ORDER BY 1,2;
        """)
    return [dict(r) for r in rows]

async def describe_table(schema: str, table: str) -> list[dict[str, Any]]:
    async with _pool.acquire() as conn:
        rows = await conn.fetch("""
            SELECT column_name, data_type, is_nullable
            FROM information_schema.columns
            WHERE table_schema=$1 AND table_name=$2
            ORDER BY ordinal_position;
        """, schema, table)
    return [dict(r) for r in rows]

async def run_readonly_query(sql: str, limit: int = 200) -> dict[str, Any]:
    head = sql.strip().lower().lstrip("(")
    if not (head.startswith("select") or head.startswith("with")):
        return {"error": "Only SELECT/WITH statements are allowed."}
    if any(tok in head for tok in FORBIDDEN):
        return {"error": "Statement contains a forbidden token."}
    async with _pool.acquire() as conn:
        await conn.execute("SET LOCAL statement_timeout = '5s';")
        rows = await conn.fetch(
            f"SELECT * FROM ({sql}) AS _q LIMIT {int(limit)};"
        )
    cols = list(rows[0].keys()) if rows else []
    return {"columns": cols,
            "rows":   [list(r.values()) for r in rows],
            "row_count": len(rows)}

Step 3: The MCP Server

# server.py
import os, json
from pathlib import Path
from dotenv import load_dotenv
load_dotenv(Path(__file__).parent / ".env")

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import pg_adapter as pg

app = Server("pg-mcp-gateway")

@app.list_tools()
async def list_tools():
    return [
        Tool(name="list_tables",
             description="List user schemas and tables.",
             inputSchema={"type":"object","properties":{}}),
        Tool(name="describe_table",
             description="Describe the columns of a schema.table.",
             inputSchema={"type":"object",
                          "properties":{"schema":{"type":"string"},
                                        "table": {"type":"string"}},
                          "required":["schema","table"]}),
        Tool(name="run_readonly_query",
             description="Execute a read-only SELECT/WITH and return rows.",
             inputSchema={"type":"object",
                          "properties":{"sql":{"type":"string"},
                                        "limit":{"type":"integer",
                                                 "default":200}},
                          "required":["sql"]}),
    ]

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "list_tables":
        data = await pg.list_tables()
    elif name == "describe_table":
        data = await pg.describe_table(arguments["schema"],
                                       arguments["table"])
    elif name == "run_readonly_query":
        data = await pg.run_readonly_query(arguments["sql"],
                                           arguments.get("limit", 200))
    else:
        data = {"error": f"Unknown tool: {name}"}
    return [TextContent(type="text",
                        text=json.dumps(data, default=str))]

async def main():
    await pg.init_pool()
    try:
        async with stdio_server() as (r, w):
            await app.run(r, w, app.create_initialization_options())
    finally:
        await pg.close_pool()

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

Step 4: Routing LLM Traffic Through HolySheep

# holysheep_router.py
import os, httpx
from typing import Any

BASE_URL = os.environ.get("HOLYSHEEP_BASE_URL",
                          "https://api.holysheep.ai/v1")
API_KEY  = os.environ["HOLYSHEEP_API_KEY"]


2026 list prices, USD per 1M tokens (published data, HolySheep AI model card).

PRICING = {
    "gpt-4.1":           {"in": 8.00,  "out": 8.00},
    "claude-sonnet-4.5": {"in": 15.00, "out": 15.00},
    "gemini-2.5-flash":  {"in": 2.50,  "out": 2.50},
    "deepseek-v3.2":     {"in": 0.42,  "out": 0.42},
}

async def chat(model: str, messages: list[dict],
               tools: list[dict] | None = None,
               tool_choice: Any = "auto") -> dict:
    payload: dict[str, Any] = {"model": model, "messages": messages}
    if tools:
        payload["tools"] = tools
        payload["tool_choice"] = tool_choice
    headers = {"Authorization": f"Bearer {API_KEY}",
               "Content-Type":  "application/json"}
    async with httpx.AsyncClient(timeout=30.0) as client:
        r = await client.post(f"{BASE_URL}/chat/completions",
                              headers=headers, json=payload)
        r.raise_for_status()
        return r.json()

def estimate_cost(model: str,
                  prompt_tokens: int,
                  completion_tokens: int) -> float:
    p = PRICING.get(model, PRICING["deepseek-v3.2"])
    return ((prompt_tokens    / 1_000_000) * p["in"]
          + (completion_tokens / 1_000_000) * p["out"])

Step 5: Migration Playbook From the Old Provider

The actual cut-over from the legacy proxy took one afternoon. Three steps:

  1. Base URL swap. Change the routing base from api.openai.com/v1 to https://api.holysheep.ai/v1 in every service config — no SDK rewrite needed.
  2. Key rotation. Mint a fresh YOUR_HOLYSHEEP_API_KEY, deploy in shadow mode writing logs only, then flip the active key per environment.
  3. Canary deploy. Route 5% of agent traffic to the new gateway, watch P95 latency and tool

    Related Resources

    Related Articles