agent-society-mcp-client-wrapper

# MCP Wrapper for AgentSociety A library that wraps low-level [MCP](https://github.com/agentsociety/mcp) client functionality, exposing it for easy usage as tools within a [LangChain](https://github.com/langchain-ai/langchain) workflow. This enables seamless integration of remote MCP tools for language model workflows and agent applications. ## Structure ### Library Code: `mcpwrap/` - **`client.py`** — Basic utility to invoke a LangChain model with a set of tools. - **`server_stub.py`** — Async abstraction (`ServerSession`) for connecting to and interfacing with an MCP server, exposing MCP tools for LangChain. - **`multi_mcp_model.py`** — Orchestrates multiple MCP server sessions and integrates their toolsets for use by one `BaseChatModel`. Handles async invocation and batching of tool requests. - **`llm_integration.py`** — Converts MCP tool schemas (JSON Schema) into dynamic, structured LangChain tools using Pydantic models. - *(You may need to install [mcp](https://pypi.org/project/mcp/) and LangChain dependencies.)* ### Example App: `sample/` - Provides a minimal, integration-tested example of how to use this library in practice. (See directory for details.) ## Usage Overview ### 1. Connect MCP servers as tool providers ```python from mcpwrap.server_stub import ServerSession session = ServerSession(name="example", url="http://localhost:8080") await session.initialize() # Async context ``` ### 2. Compose Multi-server Tools for LangChain ```python from langchain_core.language_models.chat_models import BaseChatModel from mcpwrap.multi_mcp_model import MultiMcpModel base_model = ... # Any supported BaseChatModel mcp_model = MultiMcpModel( base_model=base_model, mcp_servers=[session] # Add as many as you like ) await mcp_model.initialize() ``` ### 3. Use in a Chat Workflow ```python results = await mcp_model.ainvoke(messages) # messages: List[BaseMessage] ``` ## Features - **Wraps multiple MCP servers:** Maps their tools to a common namesp…