1e74ae3cd6
- 新增 OpenAI-compatible LLM client，支持 base_url、api_key、model 配置 - 固化意图识别、参数抽取、部署计划生成的结构化 JSON 提示词 - 增加 MCP client 配置读取和真实 session 接入说明 - 实现 checkpoint 自动保存、resume 断点续跑和已完成步骤跳过 - 实现人工确认流程，支持失败 IP 回滚 approve/reject - 新增 chat 常驻式 CLI 对话框，支持自然语言分析、参数设置、执行确认、状态查看、回滚确认和续跑 - 同步 README，补充 LLM、MCP、checkpoint、confirm/resume、chat 使用方式 - 增加相关单元测试，覆盖 LLM client、MCP 配置、确认/续跑和交互式 CLI
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
"""MCP client adapters.
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The Agent only needs a synchronous `call_tool(name, arguments)` surface. This
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module adapts simple callables or SDK-like sessions to that surface without
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forcing the rest of the codebase to import a concrete MCP SDK.
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"""
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from __future__ import annotations
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import json
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from collections.abc import Callable
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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@dataclass(frozen=True)
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class McpClientConfig:
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"""Configuration needed after a real MCP session has been created."""
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server_name: str = "pam-node"
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tool_names: dict[str, str] = field(default_factory=dict)
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@classmethod
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def from_mapping(cls, payload: dict[str, Any]) -> "McpClientConfig":
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tool_names = payload.get("tool_names") or payload.get("tools") or {}
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if not isinstance(tool_names, dict):
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raise ValueError("MCP tool_names must be an object")
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return cls(
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server_name=str(payload.get("server_name", "pam-node")),
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tool_names={str(key): str(value) for key, value in tool_names.items()},
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)
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def load_mcp_client_config(path: str | Path) -> McpClientConfig:
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payload = json.loads(Path(path).read_text(encoding="utf-8"))
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if not isinstance(payload, dict):
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raise ValueError("MCP client config must be a JSON object")
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return McpClientConfig.from_mapping(payload)
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class FunctionMcpToolClient:
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"""Wrap a plain Python callable as an MCP tool client."""
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def __init__(self, caller: Callable[[str, dict[str, Any]], Any]) -> None:
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self.caller = caller
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def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> Any:
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return self.caller(tool_name, arguments)
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class SessionMcpToolClient:
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"""Adapt SDK-like sessions exposing `call_tool`.
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The adapter accepts common result shapes:
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- raw dict/list/string
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- object with `structuredContent`
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- object with `content`, where text content may contain JSON
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"""
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def __init__(self, session: Any) -> None:
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if not hasattr(session, "call_tool"):
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raise TypeError("MCP session must expose call_tool")
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self.session = session
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def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> Any:
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result = self.session.call_tool(tool_name, arguments)
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return normalize_mcp_sdk_result(result)
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def normalize_mcp_sdk_result(result: Any) -> Any:
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if hasattr(result, "structuredContent"):
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structured = getattr(result, "structuredContent")
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if structured is not None:
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return structured
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if hasattr(result, "content"):
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content = getattr(result, "content")
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text_parts: list[str] = []
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for item in content or []:
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text = getattr(item, "text", None)
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if text is not None:
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text_parts.append(text)
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if text_parts:
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joined = "\n".join(text_parts)
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try:
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return json.loads(joined)
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except json.JSONDecodeError:
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return joined
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return result
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