The Problem
Each MCP server connected to your AI agent adds 500-2000 tokens of tool descriptions to every LLM call. Teams with 5-10 MCP servers burn 5K-20K extra input tokens per message on tool descriptions alone. At $3/million tokens, this costs $15-60/day for a team making 1K messages/day. The LLM also becomes worse at tool selection as the number of tools increases.
The Scavio Solution
Replace multiple search-related MCP servers with Scavio's single MCP server (mcp.scavio.dev/mcp) that covers Google, Amazon, YouTube, Walmart, Reddit, and TikTok. One server replaces up to six, reducing tool description token overhead by up to 80% for search-related tools. The LLM sees fewer tools, improving selection accuracy.
Before
8 MCP servers: Google search, Amazon lookup, YouTube search, Reddit search, weather, database, filesystem, calculator. Tool descriptions: 14K tokens per message. Daily token cost for tool descriptions at 1K messages: $42.
After
5 MCP servers: Scavio (covers Google + Amazon + YouTube + Reddit), weather, database, filesystem, calculator. Tool descriptions: 6K tokens per message. Daily token cost: $18. Savings: $24/day = $720/month.
Who It Is For
AI agent developers and teams with multiple MCP servers connected to Claude Desktop, Cursor, or custom agents who are seeing high token costs from tool descriptions.
Key Benefits
- Replace up to 6 search MCP servers with one Scavio server
- Reduce tool description tokens by 60-80%
- Improve LLM tool selection accuracy with fewer options
- One API key and one config entry instead of six
- Save $500-1000/month in token overhead on active teams
Python Example
# MCP configuration consolidation
# Before: claude_desktop_config.json with multiple search servers
config_before = {
"mcpServers": {
"google-search": {"url": "https://google-mcp.example.com/mcp"},
"amazon-search": {"url": "https://amazon-mcp.example.com/mcp"},
"youtube-search": {"url": "https://youtube-mcp.example.com/mcp"},
"reddit-search": {"url": "https://reddit-mcp.example.com/mcp"},
"weather": {"url": "https://weather-mcp.example.com/mcp"},
"database": {"url": "https://db-mcp.example.com/mcp"},
}
}
# After: consolidated with Scavio
config_after = {
"mcpServers": {
"scavio": {
"url": "https://mcp.scavio.dev/mcp",
"headers": {"Authorization": "Bearer YOUR_SCAVIO_KEY"},
},
"weather": {"url": "https://weather-mcp.example.com/mcp"},
"database": {"url": "https://db-mcp.example.com/mcp"},
}
}
# Token overhead estimate
servers_before = len(config_before["mcpServers"])
servers_after = len(config_after["mcpServers"])
tokens_before = servers_before * 1200 # avg tokens per server
tokens_after = servers_after * 1200
savings = tokens_before - tokens_after
print(f"Servers: {servers_before} -> {servers_after}")
print(f"Token overhead per message: {tokens_before} -> {tokens_after}")
print(f"Savings: {savings} tokens/message")JavaScript Example
// MCP configuration consolidation
// Before: claude_desktop_config.json with multiple search servers
const configBefore = {
mcpServers: {
"google-search": { url: "https://google-mcp.example.com/mcp" },
"amazon-search": { url: "https://amazon-mcp.example.com/mcp" },
"youtube-search": { url: "https://youtube-mcp.example.com/mcp" },
"reddit-search": { url: "https://reddit-mcp.example.com/mcp" },
weather: { url: "https://weather-mcp.example.com/mcp" },
database: { url: "https://db-mcp.example.com/mcp" },
},
};
// After: consolidated with Scavio
const configAfter = {
mcpServers: {
scavio: {
url: "https://mcp.scavio.dev/mcp",
headers: { Authorization: "Bearer YOUR_SCAVIO_KEY" },
},
weather: { url: "https://weather-mcp.example.com/mcp" },
database: { url: "https://db-mcp.example.com/mcp" },
},
};
const serversBefore = Object.keys(configBefore.mcpServers).length;
const serversAfter = Object.keys(configAfter.mcpServers).length;
const tokensBefore = serversBefore * 1200;
const tokensAfter = serversAfter * 1200;
console.log(`Servers: ${serversBefore} -> ${serversAfter}`);
console.log(`Token overhead per message: ${tokensBefore} -> ${tokensAfter}`);
console.log(`Savings: ${tokensBefore - tokensAfter} tokens/message`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Amazon
Product search with prices, ratings, and reviews
YouTube
Video search with transcripts and metadata
Walmart
Product search with pricing and fulfillment data
Community, posts & threaded comments from any subreddit
TikTok
Trending video, creator, and product discovery