What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open standard for connecting AI assistants to external data sources and tools. MCP agents are autonomous systems that use LLMs to make decisions, call tools, and interact with their environment.

Key MCP Components

MCP Servers

Expose tools, resources, and prompts that agents can access

MCP Clients

AI applications that connect to servers and orchestrate agent actions

Tool Calling

Agents invoke functions with structured arguments

State Management

Track conversation history and context across interactions

MCP is gaining adoption with implementations from Anthropic (Claude Desktop), Cursor, and open-source communities building servers for databases, APIs, and file systems.

Why TOON for MCP Agents?

1. Reduced Token Usage in Tool Arguments

MCP agents frequently call tools with structured data. TOON reduces the token overhead of passing arguments, allowing more tool calls per context window.

JSON Tool Arguments (158 tokens)

{
  "tool": "query_database",
  "arguments": {
    "records": [
      {"id": 1, "name": "Alice", "age": 30},
      {"id": 2, "name": "Bob", "age": 25},
      {"id": 3, "name": "Charlie", "age": 35}
    ]
  }
}

TOON Tool Arguments (89 tokens)

tool: query_database
arguments:
  records[3]{id,name,age}:
    1,Alice,30
    2,Bob,25
    3,Charlie,35
  .-

43.7% token savings - enables more complex agent workflows

2. Faster Tool Response Parsing

When MCP servers return large datasets (database queries, API responses), TOON's compact format reduces parsing time and LLM processing overhead.

Example: Database Query Response

# MCP Server returns TOON
results[150]{user_id,email,signup_date,status}:
  1001,alice@example.com,2024-01-15,active
  1002,bob@example.com,2024-01-16,active
  ... (148 more rows)
.-

LLM can quickly scan tabular structure vs. parsing nested JSON arrays with keys repeated 150 times.

3. Multi-Agent Communication Efficiency

In multi-agent systems, agents pass messages and state between each other. TOON reduces the communication overhead, allowing more agents to collaborate within the same context window.

Agent State Transfer Example

# Agent A → Agent B: Share discovered opportunities
agent: data_analyzer
to: decision_maker
timestamp: 2025-01-15T14:30:00Z
findings[4]{opportunity,confidence,expected_roi,risk_level}:
  expand_asia,0.87,2.4x,medium
  automate_ops,0.92,3.1x,low
  new_product,0.73,4.2x,high
  partnership,0.81,1.8x,low
.-

Implementation Example: MCP Server with TOON

Python MCP Server Example

from mcp.server import Server
from toon import encode, decode

# Initialize MCP server
server = Server("database-query-server")

@server.tool()
async def query_users(filters: dict) -> str:
    """Query users from database and return in TOON format"""
    
    # Fetch from database
    users = await db.query("SELECT * FROM users WHERE ...", filters)
    
    # Convert to TOON for efficient transmission
    toon_result = encode(users, delimiter=",")
    
    return toon_result

@server.tool()
async def analyze_data(toon_data: str) -> dict:
    """Receive TOON data from agent and analyze"""
    
    # Decode TOON back to Python objects
    records = decode(toon_data)
    
    # Perform analysis
    analysis = {
        "total": len(records),
        "avg_age": sum(r["age"] for r in records) / len(records),
        "top_city": most_common([r["city"] for r in records])
    }
    
    return analysis

if __name__ == "__main__":
    server.run()

TypeScript MCP Client Example

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { encode, decode } from "toon-format";

const client = new Client({
  name: "agent-orchestrator",
  version: "1.0.0"
});

// Connect to MCP server
await client.connect(transport);

// Agent calls tool with TOON-encoded data
const filters = [
  { field: "status", operator: "=", value: "active" },
  { field: "age", operator: ">", value: 25 }
];

// Encode filters as TOON
const toonFilters = encode(filters);

// Call MCP tool
const result = await client.callTool({
  name: "query_users",
  arguments: {
    filters: toonFilters  // Pass TOON instead of JSON
  }
});

// Result comes back in TOON format
const users = decode(result.content[0].text);

console.log(`Retrieved ${users.length} users with 40% fewer tokens`);

Real-World MCP + TOON Use Cases

🤖 Autonomous Database Agent

An MCP agent monitors database metrics, detects anomalies, and takes corrective actions. TOON reduces the token cost of streaming real-time metrics by 45%.

metrics[12]{timestamp,cpu_pct,mem_pct,disk_io,query_time}:
  14:00,45.3,62.1,1234,89
  14:01,47.8,63.4,1456,92
  ... (10 more samples)
.-

💬 Multi-Agent Research Team

Three specialized agents (Researcher, Analyzer, Writer) collaborate to produce reports. TOON enables efficient state sharing between agents within a single context window.

Agent communication protocol:

Researcher → Analyzer: Sources and data (TOON encoded)
Analyzer → Writer: Insights and statistics (TOON encoded)
Writer → User: Final report (Markdown)

🔧 Development Tool Agent

MCP agent analyzes codebases, suggests improvements, and executes refactoring. TOON efficiently represents file structures, dependencies, and code metrics.

Performance Benchmarks

Scenario	JSON Tokens	TOON Tokens	Savings
Tool call with 50 records	843 tokens	412 tokens	51.1%
Agent state transfer	1,234 tokens	687 tokens	44.3%
Database query response (200 rows)	3,456 tokens	1,823 tokens	47.2%
Multi-agent message batch (10 msgs)	2,108 tokens	1,134 tokens	46.2%

Average Token Savings: 47.2%

Enables 1.9x more tool calls or agent interactions per context window

Getting Started with MCP + TOON

Step 1: Install MCP SDK

# Python
pip install mcp

# JavaScript/TypeScript
npm install @modelcontextprotocol/sdk

Step 2: Install TOON Library

# Python
pip install toon-format

# JavaScript/TypeScript
npm install toon-format

Step 3: Modify Tool Responses

Update your MCP server tools to return TOON-encoded data instead of JSON:

# Before
return json.dumps(records)

# After
return toon.encode(records)

Step 4: Update Agent Prompts

Inform your agent that tool responses are in TOON format and provide parsing guidance in the system prompt.

MCP Agents & TOON Integration