shuiyu67andWorkBuddy
d2ad1000b6
- 新增 mcp_client.py：McpClient（subprocess stdio JSON-RPC，reader 线程+响应表， initialize/list_tools/call_tool/shutdown 超时可控）+ McpClientManager（connect_all 读 mcpServers、聚合、call_tool、cleanup 幂等） - unified_tool_manager.py：initialize 末尾加载外部 MCP server，按 server__tool 前缀 register_tool 带真实 inputSchema mcp_definition，shutdown 经 manager.cleanup 幂等终止子进程 - fc_tools.py：mcp_to_openai_tools 新增 get_mcp_tools 保真分支（同时修现状 liugin 工具 schema 被压成 args 的坑） - config.json：顶层加 mcpServers 空段（用户自填，已被 gitignore 不入库，代码 .get 兼容） - 新增 tests/test_mcp_client.py（5 项，全链路+桥接+保真，不触真实引擎/网络） Co-Authored-By: WorkBuddy <workbuddy@tencent.com>
340 lines
11 KiB
Python
340 lines
11 KiB
Python
"""
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Function Calling 工具格式转换
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将 MCP 插件定义转为 OpenAI FC 格式，收集所有可用工具
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"""
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import os
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import json
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import threading
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from typing import List, Dict, Optional
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def mcp_to_openai_tools(plugin_manager) -> List[Dict]:
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"""
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从插件管理器收集所有工具定义，转为 OpenAI function calling 格式
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Returns:
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[{"type": "function", "function": {"name": ..., "description": ..., "parameters": ...}}, ...]
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"""
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tools = []
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# 从插件管理器获取工具
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if not plugin_manager:
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return tools
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# 优先使用统一工具管理器的 MCP 定义（含外部 MCP client 桥接的工具，schema 保真）
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if hasattr(plugin_manager, 'get_mcp_tools'):
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try:
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for mcp_def in plugin_manager.get_mcp_tools():
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fc_def = mcp_to_fc(mcp_def)
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if fc_def:
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tools.append(fc_def)
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except Exception:
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pass
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# 统一工具管理器（兜底，旧实现未暴露 get_mcp_tools 时）
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elif hasattr(plugin_manager, 'plugins'):
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for plugin in plugin_manager.plugins:
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fc_def = _extract_fc_def(plugin)
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if fc_def:
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tools.append(fc_def)
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# 普通插件管理器
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elif hasattr(plugin_manager, 'tools'):
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for tool_info in plugin_manager.tools:
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fc_def = _tool_info_to_fc(tool_info)
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if fc_def:
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tools.append(fc_def)
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# 追加内置工具（memory 等）
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builtin_tools = _get_builtin_tools()
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tools.extend(builtin_tools)
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return tools
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def _extract_fc_def(plugin) -> Optional[Dict]:
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"""从插件实例提取 FC 定义"""
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if not hasattr(plugin, 'get_mcp_definition'):
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return None
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try:
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mcp_def = plugin.get_mcp_definition()
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return mcp_to_fc(mcp_def)
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except Exception:
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return None
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def _tool_info_to_fc(tool_info: dict) -> Optional[Dict]:
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"""从 tool_info 字典转为 FC 定义"""
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name = tool_info.get('name', '')
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desc = tool_info.get('description', '')
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if not name:
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return None
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return {
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"type": "function",
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"function": {
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"name": name,
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"description": desc,
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"parameters": {
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"type": "object",
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"properties": {
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"args": {
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"type": "string",
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"description": f"操作参数。格式参见 {name} 工具说明"
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}
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},
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"required": ["args"]
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}
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}
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}
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def mcp_to_fc(mcp_def: dict) -> Optional[Dict]:
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"""
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MCP 定义 → OpenAI FC 定义
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MCP 格式: {"name": ..., "description": ..., "inputSchema": {...}}
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OpenAI FC: {"type": "function", "function": {"name": ..., "description": ..., "parameters": {...}}}
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"""
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name = mcp_def.get('name', '')
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desc = mcp_def.get('description', '')
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schema = mcp_def.get('inputSchema', mcp_def.get('parameters', {}))
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if not name:
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return None
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# 如果 schema 为空，生成默认的 args 参数
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if not schema or not schema.get('properties'):
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schema = {
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"type": "object",
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"properties": {
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"args": {
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"type": "string",
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"description": f"操作参数字符串，格式参见 {name} 工具说明"
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}
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},
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"required": ["args"]
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}
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return {
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"type": "function",
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"function": {
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"name": name,
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"description": desc,
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"parameters": schema
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}
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}
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def fc_result_to_message(tool_call_id: str, tool_name: str, result: str) -> Dict:
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"""
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工具执行结果 → OpenAI 消息格式
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Returns:
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{"role": "tool", "tool_call_id": ..., "content": ...}
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注意: 官方 OpenAI 的 tool 消息不含 name 字段（严格服务端如讯飞会拒），故不写入。
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"""
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return {
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": result[:10000] if result else "" # 截断过长结果
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}
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def parse_fc_response(response_data: dict) -> Optional[List[Dict]]:
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"""
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解析 OpenAI FC 响应，提取 tool_calls
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Returns:
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[{"id": "call_xxx", "name": "tool_name", "arguments": {...}}, ...]
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或 None（如果不是 FC 响应）
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"""
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choices = response_data.get("choices", [])
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if not choices:
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return None
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message = choices[0].get("message", {})
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tool_calls_raw = message.get("tool_calls")
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if not tool_calls_raw:
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return None
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tool_calls = []
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for tc in tool_calls_raw:
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func = tc.get("function", {})
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name = func.get("name", "")
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args_str = func.get("arguments", "{}")
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# 解析 arguments JSON
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try:
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if isinstance(args_str, str):
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args = json.loads(args_str)
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else:
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args = args_str
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except json.JSONDecodeError:
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args = {"raw": args_str}
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# 统一为小狸内部格式: {"action": "use_tool", "tool": name, "args": "..." }
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# 如果 args 里有 args 字段，直接用；否则把整个 args 序列化
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if "args" in args and isinstance(args["args"], str):
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final_args = args["args"]
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elif "args" in args:
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final_args = json.dumps(args["args"], ensure_ascii=False)
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else:
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# 把所有参数拼成字符串
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parts = []
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for k, v in args.items():
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if isinstance(v, str):
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parts.append(f"{v}")
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else:
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parts.append(json.dumps(v, ensure_ascii=False))
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final_args = " ".join(parts) if parts else ""
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tool_calls.append({
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"id": tc.get("id", f"call_{name}"),
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"name": name,
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"arguments": args,
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# 小狸内部格式
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"action": "use_tool",
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"tool": name,
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"args": final_args,
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})
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return tool_calls
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def _get_builtin_tools() -> List[Dict]:
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"""内置工具定义（不依赖插件系统）"""
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return [
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{
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"type": "function",
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"function": {
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"name": "memory",
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"description": "持久化记忆系统。写日记、记录长期记忆、搜索历史记忆和聊天记录。",
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"parameters": {
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"type": "object",
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"properties": {
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"action": {
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"type": "string",
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"enum": ["write", "diary", "remember", "search", "context",
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"diary_list", "diary_read", "chat_list", "chat_search"],
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"description": "操作类型"
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},
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"content": {
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"type": "string",
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"description": "内容或关键词"
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}
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},
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"required": ["action"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "scheduler",
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"description": "定时任务工具，设定定时提醒。支持相对时间(30m/2h)、今日时间(14:30)、绝对时间、重复任务(r:前缀)。",
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"parameters": {
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"type": "object",
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"properties": {
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"action": {
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"type": "string",
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"enum": ["add", "list", "delete", "clear"],
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"description": "操作类型"
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},
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"time": {
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"type": "string",
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"description": "时间: 30m / 14:30 / 2026-04-28 10:00 / r:30m"
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},
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"message": {
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"type": "string",
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"description": "任务描述"
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},
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"task_id": {
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"type": "integer",
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"description": "任务ID (用于 delete)"
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}
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},
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"required": ["action"]
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}
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}
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}
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]
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# ══════════════════════════════════════════════════════════════════
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# FC 能力探测缓存
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#
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# 部分渠道/模型（如讯飞星火经 one-api 转发的 xophunyuan* 系列）
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# 不支持 function calling，请求体里只要带上非空 tools 字段就会直接
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# 返回 500 RequestParamsError:Invalid Params。
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# 引擎捕获到这种错误后会去掉 tools 降级重试；本缓存把"该模型不支持 FC"
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# 这件事记下来，后续请求直接跳过 tools，避免每次冷启动都白撞一次 500。
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# ══════════════════════════════════════════════════════════════════
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_FC_CACHE_FILE = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))), ".fc_support.json"
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)
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_fc_cache = None
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_fc_lock = threading.Lock()
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def _fc_key(base_url, model) -> str:
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"""缓存键：base_url + model（同一渠道下不同模型能力可能不同）"""
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return f"{(base_url or '').rstrip('/')}::{model or ''}"
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def _load_fc_cache() -> dict:
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global _fc_cache
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if _fc_cache is not None:
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return _fc_cache
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try:
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with open(_FC_CACHE_FILE, 'r', encoding='utf-8') as f:
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data = json.load(f)
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_fc_cache = data if isinstance(data, dict) else {}
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except (FileNotFoundError, json.JSONDecodeError, OSError, ValueError):
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_fc_cache = {}
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return _fc_cache
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def _save_fc_cache(cache: dict):
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try:
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with open(_FC_CACHE_FILE, 'w', encoding='utf-8') as f:
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json.dump(cache, f, ensure_ascii=False, indent=2)
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except OSError:
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pass
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def supports_fc(base_url, model) -> bool:
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"""该 (base_url, model) 是否支持 FC。未探测过时默认 True（乐观，先试一次）"""
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return _load_fc_cache().get(_fc_key(base_url, model)) is not False
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def mark_fc_unsupported(base_url, model) -> bool:
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"""标记该 (base_url, model) 不支持 FC 并持久化。首次标记返回 True"""
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with _fc_lock:
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cache = _load_fc_cache()
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key = _fc_key(base_url, model)
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if cache.get(key) is False:
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return False
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cache[key] = False
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_save_fc_cache(cache)
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return True
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def reset_fc_cache(base_url=None, model=None) -> int:
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"""清除 FC 探测缓存。不传参数则全清，返回清除的条目数"""
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with _fc_lock:
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cache = _load_fc_cache()
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if base_url is None and model is None:
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count = len(cache)
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cache.clear()
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else:
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count = 1 if cache.pop(_fc_key(base_url, model), None) is not None else 0
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_save_fc_cache(cache)
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return count
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def get_fc_cache() -> dict:
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"""返回 FC 探测缓存副本（供 /fc 命令展示）"""
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return dict(_load_fc_cache())
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