steven_li
30ab74ffb2
- 集成MCP连接管理器，支持MCP服务器连接 - 添加多种内置工具：ClarifyTool、CronTool、DelegateTool、ExecuteCodeTool、 PatchFileTool、ProcessTool、SendMessageTool、SpawnTool、TerminalTool、 TodoTool、WebFetchTool、WebSearchTool、WriteFileTool等 - 实现工具注册和装配功能 - 添加技能选择上下文参数 - 支持思考模式控制参数thinking_enabled feat(coordinator): 重构任务执行计划器参数命名 - 将learning_candidate_enabled重命名为allow_candidate_generation - 更新TeamGraphScheduler中的参数传递 - 修改LocalAgentRunner中的相关参数处理 - 更新README文档中的相应描述 refactor(context): 标准化工具调用参数格式 - 添加_json导入用于参数序列化 - 实现_provider_tool_calls方法标准化OpenAI兼容的工具调用载荷 - 修复工具调用中参数非字符串类型的序列化问题 refactor(session): 优化消息历史记录过滤逻辑 - 修改get_messages_as_conversation为基于运行状态过滤消息 - 排除未完成、失败或错误结束的运行记录 - 改进对话历史的可见性控制机制 fix(store): 修复FTS索引重建逻辑 - 添加异常处理防止FTS索引创建失败 - 实现_rebuild_fts_index方法重新构建全文搜索索引 - 优化索引触发器和表的维护流程
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""MCP tool wrappers for Beaver's tool contract."""
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from __future__ import annotations
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import asyncio
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from dataclasses import dataclass
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import json
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from typing import Any, Awaitable, Callable
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from beaver.tools.base import BaseTool, ToolContext, ToolResult, ToolSpec
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def _tool_schema(tool_def: Any) -> dict[str, Any]:
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schema = getattr(tool_def, "inputSchema", None) or getattr(tool_def, "input_schema", None)
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if isinstance(schema, dict):
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return schema
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return {"type": "object", "properties": {}}
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def _tool_name(tool_def: Any) -> str:
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return str(getattr(tool_def, "name", "") or "")
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def _tool_description(tool_def: Any) -> str:
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return str(getattr(tool_def, "description", "") or _tool_name(tool_def))
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def _mcp_result_to_text(result: Any) -> str:
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parts: list[str] = []
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for block in list(getattr(result, "content", []) or []):
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text = getattr(block, "text", None)
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parts.append(str(text if text is not None else block))
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if not parts and getattr(result, "structuredContent", None) is not None:
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return json.dumps(getattr(result, "structuredContent"), ensure_ascii=False, indent=2)
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return "\n".join(parts) or "(no output)"
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@dataclass(slots=True)
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class MCPToolWrapper(BaseTool):
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server_id: str
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tool_def: Any
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call_tool: Callable[[str, dict[str, Any]], Awaitable[Any]]
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tool_timeout: int = 30
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sensitive: bool = False
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kind: str = "online"
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category: str = "online"
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display_name: str = ""
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@property
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def original_name(self) -> str:
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return _tool_name(self.tool_def)
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@property
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def spec(self) -> ToolSpec:
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return ToolSpec(
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name=f"mcp_{self.server_id}_{self.original_name}",
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description=_tool_description(self.tool_def),
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input_schema=_tool_schema(self.tool_def),
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toolset=f"mcp-{self.server_id}",
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metadata={
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"server_id": self.server_id,
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"original_tool_name": self.original_name,
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"kind": self.kind,
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"category": self.category,
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"display_name": self.display_name or self.server_id,
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"transport": "mcp",
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},
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)
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async def invoke(self, arguments: dict[str, Any], context: ToolContext) -> ToolResult:
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try:
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result = await asyncio.wait_for(
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self.call_tool(self.original_name, dict(arguments or {})),
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timeout=max(1, int(self.tool_timeout or 30)),
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)
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return ToolResult(
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success=True,
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content=_mcp_result_to_text(result),
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tool_name=self.spec.name,
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raw_output=result,
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)
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except Exception as exc:
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return ToolResult(
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success=False,
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content=f"MCP tool {self.server_id}.{self.original_name} failed: {exc}",
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tool_name=self.spec.name,
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error=str(exc),
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)
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