MCP On-Demand Context Savings

The Problem

MCP servers expose tool descriptions that get loaded into the LLM context on every conversation turn. With 10+ MCP tools connected, 3-5K tokens are consumed just listing available tools before the user's actual question is processed. This inflates cost and reduces effective context window.

The Scavio Solution

Audit which MCP tools are actually called per session. Replace always-on tool registrations with on-demand loading: only connect the Scavio MCP server when a search-related question is detected. Use a lightweight classifier or keyword match to decide when to load the search tool.

Who It Is For

AI agent builders managing multiple MCP connections, teams optimizing LLM context usage and costs, developers building multi-tool Claude or GPT agents.

Key Benefits

• Reduce MCP tool description overhead by 60-80%
• On-demand tool loading preserves context window
• Scavio single MCP covers 5 platforms (fewer tools to register)
• Intent-based loading is simple keyword matching
• Cost savings compound across thousands of daily conversations

Python Example

# On-demand MCP loading pattern (pseudocode)
# Instead of registering all MCP tools at startup:

SEARCH_TRIGGERS = ['search', 'find', 'look up', 'what is', 'latest', 'current price']

def should_load_search_mcp(user_message: str) -> bool:
    return any(trigger in user_message.lower() for trigger in SEARCH_TRIGGERS)

# In your agent loop:
# if should_load_search_mcp(message):
#     connect_mcp('https://mcp.scavio.dev/mcp', headers={'x-api-key': key})
# else:
#     skip search MCP, save ~1K tokens of tool descriptions

JavaScript Example

// On-demand MCP loading pattern
const SEARCH_TRIGGERS = ['search', 'find', 'look up', 'what is', 'latest', 'current price'];

function shouldLoadSearchMcp(userMessage) {
  return SEARCH_TRIGGERS.some(t => userMessage.toLowerCase().includes(t));
}

// In agent loop:
// if (shouldLoadSearchMcp(message)) {
//   await connectMcp('https://mcp.scavio.dev/mcp', { headers: { 'x-api-key': key } });
// }

Platforms Used