mcp-langchain-agent

A Model Context Protocol (MCP) server exposing a small set of tools, paired with a LangChain agent that uses those tools to complete multi-step tasks.

📌 What this proves to a recruiter: I can build agentic systems — tool use, MCP, multi-step planning — extending the kind of MCP server work happening on Adobe AEM today.

What's in here

mcp-langchain-agent/
├── mcp_server/          # MCP server that exposes tools via stdio
│   ├── server.py        #   entrypoint, registers tools
│   ├── tools.py         #   the actual tool implementations
│   └── schemas.py       #   JSON schemas for tool inputs/outputs
├── agent/
│   ├── client.py        #   LangChain agent that connects to the MCP server
│   └── chains.py        #   reusable prompt/chain definitions
├── tests/
│   ├── test_server.py
│   └── test_agent.py
└── examples/
    └── run_demo.py      #   end-to-end demo of the agent solving a task

The tools

The MCP server exposes three tools an LLM can call:

Tool	What it does
search_docs(query, top_k)	BM25-ish keyword search over a local corpus of markdown notes
get_doc(doc_id)	Fetch the full text of a doc by id
summarize(doc_id, max_words)	Returns a short summary of a doc (LLM-backed)

The agent

The LangChain agent:

1. Receives a user task in natural language (e.g. "Find what our handbook says about vacation policy and give me a 3-bullet summary")
2. Decides which tools to call, in what order
3. Calls the tools via the MCP client
4. Synthesises the answer from the tool outputs

Powered by langchain-mcp-adapters so the MCP tools become LangChain Tool objects — no glue code.

Quickstart

Prerequisites

• Python 3.11+
• An OpenAI or Anthropic API key

Install

git clone https://github.com/adityapal26may/mcp-langchain-agent
cd mcp-langchain-agent
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Configure

export OPENAI_API_KEY=sk-...
# or
export ANTHROPIC_API_KEY=sk-ant-...

Run the demo

python examples/run_demo.py

Architecture

┌──────────────────┐         ┌────────────────────┐
│  LangChain Agent │ ──────▶ │  MCP Server (stdio)│
│   (client.py)    │  tools  │   (server.py)      │
└──────────────────┘  JSON   └─────────┬──────────┘
        ▲                              │
        │                              ▼
        │                     ┌────────────────────┐
        │                     │  Tool Implementations│
        │                     │   (tools.py)        │
        │                     └─────────┬──────────┘
        │                               │
        └─────── synthesized answer ────┘

Why MCP

MCP is becoming the standard protocol for connecting LLMs to tools and data sources — Anthropic, OpenAI, and major IDE vendors all support it. Building a server + client pair here is a direct demonstration of:

• Multi-step agentic reasoning
• Tool schema design (JSON Schema for inputs)
• Process-level service boundaries (the MCP server runs as a separate process)

Tech Stack

• Python 3.11, FastAPI (for the HTTP wrapper demo)
• LangChain (langchain, langchain-openai, langchain-anthropic)
• MCP (mcp Python SDK, langchain-mcp-adapters)
• rank-bm25 for keyword search
• pytest for tests

Roadmap

• [ ] Streaming token output from the agent
• [ ] Persistent conversation memory
• [ ] Observability: log every tool call + token usage
• [ ] HTTP transport (in addition to stdio)
• [ ] Eval suite: 20+ tasks with expected tool-call sequences

Author

Aditya Pal — @adityapal26may