Add initial implementation of MCP server and related tools for BMI calculation

labelme-and-yolo-format.py

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+# Labelme 标注与 YOLO 格式

mcp-from-scratch-gpt.py

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+"""
+Minimal MCP stdio server
+Only uses Python standard library: sys + json
+
+Tool:
+    calculate_bmi(height, weight)
+"""
+
+import sys
+import json
+
+
+PROTOCOL_VERSION = "2026-07-28"
+
+SERVER_INFO = {
+    "name": "bmi-mcp-server",
+    "version": "0.1.0"
+}
+
+
+# --------------------------------------------------
+# JSON-RPC helpers
+# --------------------------------------------------
+
+def rpc_result(request_id, result):
+    return {
+        "jsonrpc": "2.0",
+        "id": request_id,
+        "result": result
+    }
+
+
+def rpc_error(request_id, code, message, data=None):
+    error = {
+        "code": code,
+        "message": message
+    }
+
+    if data is not None:
+        error["data"] = data
+
+    return {
+        "jsonrpc": "2.0",
+        "id": request_id,
+        "error": error
+    }
+
+
+def result_meta():
+    return {
+        "io.modelcontextprotocol/serverInfo": SERVER_INFO
+    }
+
+
+def tool_error(request_id, message):
+    """
+    MCP request itself is valid,
+    but the tool cannot execute successfully.
+    """
+    return rpc_result(
+        request_id,
+        {
+            "resultType": "complete",
+            "_meta": result_meta(),
+            "content": [
+                {
+                    "type": "text",
+                    "text": message
+                }
+            ],
+            "isError": True
+        }
+    )
+
+
+# --------------------------------------------------
+# MCP tool definition
+# --------------------------------------------------
+
+BMI_TOOL = {
+    "name": "calculate_bmi",
+    "description": "Calculate BMI from height in meters and weight in kilograms.",
+    "inputSchema": {
+        "type": "object",
+        "properties": {
+            "height": {
+                "type": "number",
+                "description": "Height in meters"
+            },
+            "weight": {
+                "type": "number",
+                "description": "Weight in kilograms"
+            }
+        },
+        "required": [
+            "height",
+            "weight"
+        ],
+        "additionalProperties": False
+    }
+}
+
+
+# --------------------------------------------------
+# MCP methods
+# --------------------------------------------------
+
+def handle_discover(request):
+    return rpc_result(
+        request["id"],
+        {
+            "resultType": "complete",
+            "supportedVersions": [
+                PROTOCOL_VERSION
+            ],
+            "capabilities": {
+                "tools": {
+                    "listChanged": False
+                }
+            },
+            "_meta": result_meta(),
+            "instructions": "Provides a BMI calculation tool.",
+            "ttlMs": 3600000,
+            "cacheScope": "public"
+        }
+    )
+
+
+def handle_tools_list(request):
+    return rpc_result(
+        request["id"],
+        {
+            "resultType": "complete",
+            "_meta": result_meta(),
+            "tools": [
+                BMI_TOOL
+            ],
+            "ttlMs": 300000,
+            "cacheScope": "public"
+        }
+    )
+
+
+def handle_tools_call(request):
+    request_id = request["id"]
+
+    params = request.get("params", {})
+
+    name = params.get("name")
+    arguments = params.get("arguments", {})
+
+    # -------------------------------
+    # Protocol-level error
+    # -------------------------------
+
+    if name != "calculate_bmi":
+        return rpc_error(
+            request_id,
+            -32602,
+            f"Unknown tool: {name}"
+        )
+
+    # -------------------------------
+    # Tool input validation
+    # -------------------------------
+
+    height = arguments.get("height")
+    weight = arguments.get("weight")
+
+    if height is None:
+        return tool_error(
+            request_id,
+            "height is required"
+        )
+
+    if weight is None:
+        return tool_error(
+            request_id,
+            "weight is required"
+        )
+
+    # bool is a subclass of int in Python,
+    # so explicitly reject True / False.
+    if (
+        not isinstance(height, (int, float))
+        or isinstance(height, bool)
+    ):
+        return tool_error(
+            request_id,
+            "height must be a number"
+        )
+
+    if (
+        not isinstance(weight, (int, float))
+        or isinstance(weight, bool)
+    ):
+        return tool_error(
+            request_id,
+            "weight must be a number"
+        )
+
+    if height <= 0:
+        return tool_error(
+            request_id,
+            "height must be greater than 0"
+        )
+
+    if weight <= 0:
+        return tool_error(
+            request_id,
+            "weight must be greater than 0"
+        )
+
+    # -------------------------------
+    # Actual business logic
+    # -------------------------------
+
+    bmi = weight / (height ** 2)
+    bmi = round(bmi, 2)
+
+    return rpc_result(
+        request_id,
+        {
+            "resultType": "complete",
+            "_meta": result_meta(),
+            "content": [
+                {
+                    "type": "text",
+                    "text": str(bmi)
+                }
+            ],
+            "isError": False
+        }
+    )
+
+
+# --------------------------------------------------
+# Protocol validation / dispatch
+# --------------------------------------------------
+
+def process(request):
+
+    # JSON-RPC message must be an object
+    if not isinstance(request, dict):
+        return rpc_error(
+            None,
+            -32600,
+            "Invalid Request"
+        )
+
+    if request.get("jsonrpc") != "2.0":
+        return rpc_error(
+            request.get("id"),
+            -32600,
+            "Invalid Request"
+        )
+
+    # No id means notification.
+    # This simple server does not need to respond.
+    if "id" not in request:
+        return None
+
+    method = request.get("method")
+
+    if not isinstance(method, str):
+        return rpc_error(
+            request.get("id"),
+            -32600,
+            "Invalid Request"
+        )
+
+    # Modern MCP request metadata
+    params = request.get("params", {})
+    meta = params.get("_meta", {}) if isinstance(params, dict) else {}
+
+    requested_version = meta.get(
+        "io.modelcontextprotocol/protocolVersion"
+    )
+
+    if requested_version != PROTOCOL_VERSION:
+        return rpc_error(
+            request.get("id"),
+            -32022,
+            "Unsupported protocol version",
+            {
+                "supported": [
+                    PROTOCOL_VERSION
+                ],
+                "requested": requested_version
+            }
+        )
+
+    # -------------------------------
+    # MCP method dispatcher
+    # -------------------------------
+
+    if method == "server/discover":
+        return handle_discover(request)
+
+    if method == "tools/list":
+        return handle_tools_list(request)
+
+    if method == "tools/call":
+        return handle_tools_call(request)
+
+    return rpc_error(
+        request.get("id"),
+        -32601,
+        f"Method not found: {method}"
+    )
+
+
+# --------------------------------------------------
+# stdio transport
+# --------------------------------------------------
+
+def send(message):
+    text = json.dumps(
+        message,
+        ensure_ascii=False,
+        separators=(",", ":")
+    )
+
+    sys.stdout.write(text + "\n")
+    sys.stdout.flush()
+
+
+def main():
+
+    # stdin remains open while the MCP server is alive
+    for line in sys.stdin:
+
+        line = line.strip()
+
+        if not line:
+            continue
+
+        try:
+            request = json.loads(line)
+
+        except json.JSONDecodeError:
+            send(
+                rpc_error(
+                    None,
+                    -32700,
+                    "Parse error"
+                )
+            )
+            continue
+
+        try:
+            response = process(request)
+
+            if response is not None:
+                send(response)
+
+        except Exception as e:
+            # Logs MUST go to stderr, never stdout
+            print(
+                f"Internal error: {e}",
+                file=sys.stderr,
+                flush=True
+            )
+
+            send(
+                rpc_error(
+                    request.get("id")
+                    if isinstance(request, dict)
+                    else None,
+                    -32603,
+                    "Internal error"
+                )
+            )
+
+
+if __name__ == "__main__":
+    main()

mcp-from-scratch-mytry.py

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+"""只用 sys + json，从零实现一个 MCP Server
+提供 calculate_bmi(height, weight) 工具。
+"""
+
+import sys
+import json
+
+def process(request):
+    if request.get("method") == "ping":
+        result = {
+            "jsonrpc": "2.0",
+            "id": request.get("id"),
+            "result": "pong"
+        }
+        return result
+    if request.get("method") == "tools/list":
+        result = {
+            "jsonrpc": "2.0",
+            "id": 1,
+            "result": {
+                "tools": [
+                {
+                    "name": "calculate_bmi",
+                    "description": "Calculate BMI from height and weight",
+                    "inputSchema": {
+                    "type": "object",
+                    "properties": {
+                        "height": {
+                        "type": "number"
+                        },
+                        "weight": {
+                        "type": "number"
+                        }
+                    },
+                    "required": [
+                        "height",
+                        "weight"
+                    ]
+                    }
+                }
+                ]
+            }
+        }
+        return result
+    if request.get("method") == "tools/call":
+        name = request.get("params", {}).get("name")
+        arguments = request.get("params", {}).get("arguments", {})
+        if name == "calculate_bmi":
+            height = arguments.get("height")
+            weight = arguments.get("weight")
+            if height is not None and weight is not None:
+                bmi = weight / (height ** 2)
+                result = {
+                    "jsonrpc": "2.0",
+                    "id": request.get("id"),
+                    "result": {
+                        "resultType": "complete",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": str(bmi)
+                            }
+                        ],
+                        "isError": False
+                    }
+                }
+                return result
+        
+    return None
+
+for line in sys.stdin:
+    request = json.loads(line)
+
+    output = process(request)
+    if output is not None:
+        print(json.dumps(output, ensure_ascii=False), flush=True)

rag.py

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+"""稀疏检索 / 简单 RAG"""
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+
+# 1. 简单切块
+def chunk_text(text, chunk_size=60, overlap=15):
+    chunks = []
+
+    start = 0
+
+    while start < len(text):
+        end = start + chunk_size
+
+        chunk = text[start:end]
+        chunks.append(chunk)
+
+        if end >= len(text):
+            break
+
+        start = end - overlap
+
+    return chunks
+
+
+# 2. 知识库原文
+document = """
+职工请事假，应当提前一个工作日通过OA系统提交申请，
+经部门负责人审批同意后方可休假。
+
+职工因疾病无法正常工作的，可以申请病假。
+病假应当提供医院出具的相关证明材料。
+
+员工因工作需要出差的，应提前提交出差申请。
+住宿费用按照公司差旅管理制度规定的标准报销。
+
+职工连续工作满一年后可以享受带薪年休假。
+年休假天数根据职工累计工作年限确定。
+""".replace("\n", "")
+
+
+# 3. 文档切块
+chunks = chunk_text(
+    document,
+    chunk_size=60,
+    overlap=15
+)
+
+print("=== chunks ===")
+
+for i, chunk in enumerate(chunks):
+    print(i, chunk)
+
+
+# 4. 给所有 chunk 建 TF-IDF 索引
+vectorizer = TfidfVectorizer(
+    analyzer="char",
+    ngram_range=(1, 3)
+)
+
+doc_vectors = vectorizer.fit_transform(chunks)
+
+
+# 5. 用户问题
+query = "事假怎么申请？"
+
+
+# 6. Query 转成同一个 TF-IDF 空间
+query_vector = vectorizer.transform([query])
+
+
+# 7. 计算 query 与所有 chunk 的相似度
+scores = cosine_similarity(
+    query_vector,
+    doc_vectors
+)[0]
+
+
+# 8. Top-K
+top_k = 3
+
+top_indices = scores.argsort()[::-1][:top_k]
+
+
+print("\n=== query ===")
+print(query)
+
+print("\n=== retrieval result ===")
+
+for index in top_indices:
+    print(
+        f"score={scores[index]:.4f}",
+        chunks[index]
+    )

text-cls-baseline.py

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+"""文本分类 baseline"""
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report
+
+
+# 1. 数据
+texts = [
+    "我要申请退款",
+    "这个订单可以退款吗",
+    "钱什么时候退回来",
+    "我要取消订单退款",
+    "商品有问题申请退款",
+    "怎么退货退款",
+
+    "我的密码忘记了",
+    "怎么修改登录密码",
+    "账号登录不上去了",
+    "如何重置密码",
+    "账户无法登录",
+    "密码在哪里修改",
+
+    "快递什么时候到",
+    "物流怎么还没更新",
+    "我的包裹到哪里了",
+    "查询一下配送进度",
+    "快递一直没有收到",
+    "帮我查一下物流"
+]
+
+labels = [
+    "refund", "refund", "refund",
+    "refund", "refund", "refund",
+
+    "account", "account", "account",
+    "account", "account", "account",
+
+    "delivery", "delivery", "delivery",
+    "delivery", "delivery", "delivery"
+]
+
+
+# 2. 切分训练集 / 验证集
+X_train, X_val, y_train, y_val = train_test_split(
+    texts,
+    labels,
+    test_size=0.33,
+    random_state=42,
+    stratify=labels
+)
+
+
+# 3. 文本 -> TF-IDF 稀疏向量
+vectorizer = TfidfVectorizer(
+    analyzer="char",
+    ngram_range=(1, 3)
+)
+
+X_train_vec = vectorizer.fit_transform(X_train)
+X_val_vec = vectorizer.transform(X_val)
+
+
+# 4. 训练分类器
+model = LinearSVC(C=1.0)
+model.fit(X_train_vec, y_train)
+
+
+# 5. 验证
+pred = model.predict(X_val_vec)
+
+print("accuracy:", accuracy_score(y_val, pred))
+print(classification_report(y_val, pred))
+
+
+# 6. 推理
+test_texts = [
+    "我的东西怎么还没送过来",
+    "登录密码忘了怎么办",
+    "我要把这个订单退掉"
+]
+
+X_test = vectorizer.transform(test_texts)
+pred = model.predict(X_test)
+
+for text, label in zip(test_texts, pred):
+    print(text, "->", label)