The Problem
AI agents connecting to multiple MCP servers have no centralized way to monitor server health, aggregate tool catalogs, or route tool calls efficiently. Each agent-server connection is managed independently, making failover and load balancing impossible.
The Scavio Solution
Build an MCP routing layer that aggregates tool catalogs from multiple MCP servers, implements health checks per server, and routes tool invocations to the correct (and healthy) server. The router exposes a single MCP endpoint to agents, abstracting the multi-server complexity.
Before
Before the routing layer, an enterprise agent connected to 4 MCP servers individually. When the search server went down, the agent lost all search tools but continued operating with degraded capabilities. No one noticed for 6 hours until a user reported ungrounded responses.
After
After implementing the routing layer, health checks detected the search server failure within 60 seconds and triggered a PagerDuty alert. The routing layer's fallback configuration redirected search tool calls to a secondary provider. Agent functionality was uninterrupted.
Who It Is For
Platform engineers building multi-MCP-server architectures for enterprise AI agent deployments that require high availability and centralized monitoring.
Key Benefits
- Centralized health monitoring for all MCP servers
- Automatic failover when a server goes down
- Single connection point for agents (simplified agent config)
- Tool namespace management prevents collisions
- Request logging and latency tracking across all servers
Python Example
import requests, time, threading
from collections import defaultdict
SERVERS = {
'search': {'url': 'https://mcp.scavio.dev/mcp', 'headers': {'x-api-key': 'KEY'}},
'database': {'url': 'http://localhost:3001/mcp', 'headers': {}},
}
HEALTH = defaultdict(lambda: {'healthy': True, 'last_check': 0, 'failures': 0})
def health_check():
while True:
for name, server in SERVERS.items():
try:
r = requests.options(server['url'], timeout=5)
if r.status_code < 400:
HEALTH[name] = {'healthy': True, 'last_check': time.time(), 'failures': 0}
else:
HEALTH[name]['failures'] += 1
except Exception:
HEALTH[name]['failures'] += 1
if HEALTH[name]['failures'] >= 3:
HEALTH[name]['healthy'] = False
print(f'ALERT: MCP server {name} is unhealthy')
time.sleep(30)
threading.Thread(target=health_check, daemon=True).start()
def route_tool_call(tool_name: str, args: dict) -> dict:
server_name = _resolve_server(tool_name)
if not HEALTH[server_name]['healthy']:
raise RuntimeError(f'Server {server_name} is unhealthy')
server = SERVERS[server_name]
# Forward the tool call to the appropriate server
return requests.post(server['url'], headers=server['headers'],
json={'tool': tool_name, 'arguments': args}, timeout=15).json()
def _resolve_server(tool_name: str) -> str:
search_tools = ['google_search', 'reddit_search', 'youtube_search', 'amazon_search']
return 'search' if tool_name in search_tools else 'database'JavaScript Example
const SERVERS = {
search: { url: 'https://mcp.scavio.dev/mcp', headers: { 'x-api-key': process.env.SCAVIO_API_KEY } },
database: { url: 'http://localhost:3001/mcp', headers: {} },
};
const health = new Map();
setInterval(async () => {
for (const [name, server] of Object.entries(SERVERS)) {
try {
const r = await fetch(server.url, { method: 'OPTIONS', signal: AbortSignal.timeout(5000) });
health.set(name, { healthy: r.ok, failures: 0 });
} catch {
const prev = health.get(name) || { failures: 0 };
prev.failures++;
prev.healthy = prev.failures < 3;
health.set(name, prev);
if (!prev.healthy) console.error(`ALERT: MCP server ${name} is unhealthy`);
}
}
}, 30_000);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
YouTube
Video search with transcripts and metadata
Amazon
Product search with prices, ratings, and reviews
Walmart
Product search with pricing and fulfillment data