@tool
def search_jira(query: str) -> str:
 """Search Jira tickets"""
 ...

@tool
def query_database(sql: str) -> str:
 """Execute a SQL query"""
 ...

agent = create_react_agent(model=llm, tools=[search_jira, query_database, ...])

┌────────────────────────────────────────────────────────────────┐
│ MCP Architecture │
├──────────────────┬──────────────────────────────────────────────┤
│ Host │ The environment hosting the Agent: │
│ │ Claude Desktop, Claude Code, custom apps │
│ │ Contains one or more MCP Clients │
├──────────────────┼──────────────────────────────────────────────┤
│ Client │ Protocol client embedded in the Host │
│ │ Manages connections and communication │
│ │ with MCP Servers │
├──────────────────┼──────────────────────────────────────────────┤
│ Server │ Independent process exposing Tools │
│ │ Can also expose Resources and Prompts │
└──────────────────┴──────────────────────────────────────────────┘

Transport options:
 Local: stdio (subprocess stdin/stdout)
 Remote: HTTP + SSE (Server-Sent Events)

@lc_tool
def calculator(expression: str) -> str:
 """Evaluate a simple arithmetic expression."""
 ...

@lc_tool
def text_stats(text: str) -> str:
 """Return word count, sentence count, and character count."""
 ...

@lc_tool
def weather_mock(city: str) -> str:
 """Return mock weather data for a city."""
 ...

traditional_tools = [calculator, text_stats, weather_mock]
agent = create_react_agent(model=llm, tools=traditional_tools)

Q: What is 2 ** 10 + 100 / 4?
A: The result of 2 ** 10 + 100 / 4 is 1049.0.

Q: Analyze this text: 'Python is elegant. It is readable. Everyone loves it!'
A: 9 words, 3 sentences, 53 characters.

Q: What's the weather in Beijing?
A: sunny, 25°C, humidity 40%.

Tools defined in THIS file: ['calculator', 'text_stats', 'weather_mock']

If Agent B also needs these tools: copy-paste or re-import
If you update calculator's logic: find every Agent file that uses it
If the tools are written in TypeScript: this approach doesn't work at all

# tools_server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("demo-tools")

@mcp.tool()
def calculator(expression: str) -> str:
 """Evaluate a simple arithmetic expression (e.g. '2 ** 10', '100 / 7')."""
 ...

@mcp.tool()
def text_stats(text: str) -> str:
 """Return word count, sentence count, and character count for the given text."""
 ...

@mcp.tool()
def weather_mock(city: str) -> str:
 """Return mock weather data for a city (demo only — not real data)."""
 ...

if __name__ == "__main__":
 mcp.run(transport="stdio")

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

server_params = StdioServerParameters(command="python", args=["tools_server.py"])

async with stdio_client(server_params) as (read, write):
 async with ClientSession(read, write) as session:
 await session.initialize()

 # Dynamic discovery — no tool names hardcoded in client code
 tools_result = await session.list_tools()

Server: demo-tools
Discovered 3 tools:
 ● calculator — Evaluate a simple arithmetic expression (e.g. '2 ** 10', '100 / 7').
 ● text_stats — Return word count, sentence count, and character count for the given text.
 ● weather_mock — Return mock weather data for a city (demo only — not real data).

calculator('2 ** 10 + 100 / 4') → 2 ** 10 + 100 / 4 = 1049.0
weather_mock('Shanghai') → {"city": "Shanghai", "temp": 22, "condition": "cloudy", "humidity": 75}
text_stats(...) → {"words": 9, "sentences": 3, "chars": 53}

from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient(
 {
 "demo-tools": {
 "command": "python",
 "args": ["tools_server.py"],
 "transport": "stdio",
 }
 }
)
mcp_tools = await client.get_tools()
# → ['calculator', 'text_stats', 'weather_mock']
# Tool definitions live in the Server, not in this file

agent = create_react_agent(model=llm, tools=mcp_tools)
result = await agent.ainvoke({"messages": [HumanMessage(multi_q)]})

Questions:
 1. What is sqrt(144) + 2 ** 8?
 2. What's the weather in Shenzhen?
 3. Count the stats for: 'MCP is a protocol. It standardizes tools. Agents love it!'

Answers:
 1. The result of sqrt(144) + 2 ** 8 is 260.
 2. (LLM called weather_mock, but added a "not real-time data" disclaimer
 because the tool description says "demo only")
 3. word count: 15, sentence count: 1, character count: 52

Dimension Function Calling MCP
──────────────────────────────────────────────────────────────────────
Tool definition In Agent code In independent MCP Server
Tool discovery Hardcoded import list_tools() (dynamic)
Multi-agent reuse Copy-paste or re-import All Agents connect to same Server
Update tool logic Edit every Agent file Edit Server only
Cross-language Same language only Any language (JSON-RPC)
Invocation In-process function call Cross-process JSON-RPC
Startup overhead None subprocess launch
Best fit Single Agent / small projects Multi-Agent / team collaboration

Official MCP Servers (@modelcontextprotocol/):
 server-filesystem — local file read/write
 server-github — GitHub repo, issues, PR operations
 server-postgres — PostgreSQL queries
 server-brave-search — web search (requires Brave API key)

Community maintained:
 Search "awesome-mcp-servers" for the full list
 Includes: Jira, Slack, Notion, Linear, Figma, and dozens more

Claude Code's tools are MCP:
 → file read/write, terminal commands, code search…
 → all run through this same protocol