從 0 開發一個 MCP Server
構建 AI 工具調用能力

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "search_articles",
    "arguments": { "query": "MCP 部署", "limit": 5 }
  },
  "id": 42
}

pip install "mcp[cli]"

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("hello-server")

@mcp.tool()
def greet(name: str) -> str:
    """回傳問候語，確認 MCP Server 正常運作"""
    return f"你好，{name}！MCP Server 已成功啟動。"

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

@mcp.tool()
def search_articles(query: str, limit: int = 10) -> list[dict]:
    """以關鍵字全文搜尋文章"""
    ...

@mcp.tool()
def get_article(article_id: str) -> dict:
    """依文章 ID 取得完整內文"""
    ...

@mcp.tool()
def list_categories() -> list[str]:
    """回傳可用分類清單"""
    ...

@mcp.tool()
def create_note(title: str, body: str, tags: list[str] | None = None) -> dict:
    """建立新的內部備忘錄"""
    ...

@mcp.tool()
def summarize_text(text: str, max_chars: int = 300) -> str:
    """將長文壓縮至指定字數以內"""
    ...

@mcp.resource("kb://articles/{article_id}")
def article_resource(article_id: str) -> str:
    return load_markdown(article_id)

@mcp.resource("file://config/{name}")
def config_resource(name: str) -> str:
    return Path(f"./config/{name}.json").read_text(encoding="utf-8")

@mcp.prompt()
def qa_from_context(question: str, context_uri: str) -> str:
    """依上下文 URI 內容以 QA 格式回答"""
    ctx = resolve_resource(context_uri)
    return f"""僅以下列上下文為依據回答問題。
上下文:
{ctx}

問題: {question}"""

@mcp.prompt()
def summarize_article(article_id: str, style: str = "bullet") -> str:
    body = load_markdown(article_id)
    return f"請以{style}格式摘要以下文章:\n\n{body}"

if __name__ == "__main__":
    mcp.run(
        transport="streamable-http",
        host="0.0.0.0",
        port=8080,
        path="/mcp"
    )

npx @modelcontextprotocol/inspector python server.py

curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1}'

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY server.py .
RUN useradd -m mcpuser
USER mcpuser
EXPOSE 8080
HEALTHCHECK CMD curl -f http://localhost:8080/mcp || exit 1
CMD ["python", "server.py"]

docker build -t kb-mcp-server .
docker run -d --name kb-mcp -p 8080:8080 \
  -e DATABASE_URL="postgresql://..." \
  -e API_KEY="sk-..." \
  kb-mcp-server

import chromadb
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("knowledge-base")
client = chromadb.PersistentClient(path="./chroma_data")
collection = client.get_or_create_collection("notes")

@mcp.tool()
def semantic_search(query: str, n: int = 5) -> str:
    results = collection.query(query_texts=[query], n_results=n)
    docs = results.get("documents", [[]])[0]
    return "\n---\n".join(docs) if docs else "找不到相符內容。"

@mcp.tool()
def ingest_note(text: str, source: str) -> str:
    collection.add(documents=[text], metadatas=[{"source": source}], ids=[source])
    return f"已索引: {source}"