Scavio for MCP Context Budget Management

The Problem

MCP tool descriptions are loaded into the LLM context on every turn. Each tool adds 200-500 tokens of description. With 10+ tools, the overhead is 3-5K tokens per turn, inflating costs by 20-30% and reducing effective context window for actual user queries.

How Scavio Helps

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

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Agent has 12 MCP tools connected. Tool descriptions consume 4.2K tokens per turn. After implementing on-demand loading: search MCP loads only when user message contains 'search', 'find', 'look up', or 'latest'. Average overhead drops to 1.1K tokens. LLM cost reduced 25%.":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

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

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your mcp context budget management solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.