The Problem
Production AI agents using MCP servers have no built-in observability. When an agent's search quality degrades, there is no way to tell whether the MCP server is down, the search provider is rate-limited, or the agent is making poor tool selections. Teams discover problems only when users complain about bad answers, by which point the damage is done.
The Scavio Solution
Build a health reporting layer that monitors MCP tool calls: success rate, latency, result count, and error types. Log every MCP tool invocation with its response metadata. Set up alerts for anomalies: success rate below 95%, median latency above 3 seconds, or zero-result rate above 20%. For Scavio's MCP server, monitor each of the 11 tools independently since Google, Reddit, and YouTube have different baseline performance characteristics. The health data also informs routing decisions: if youtube_search latency spikes, the agent can temporarily prefer google_search for video queries.
Before
Before health reporting, the team learned about MCP server issues from user complaints. A 2-hour YouTube search outage went unnoticed because only 8% of queries used that tool. When it was discovered, three customer escalations were already in queue.
After
After implementing health reporting, the team gets Slack alerts within 5 minutes of any MCP tool degradation. The agent automatically routes around degraded tools, and the on-call engineer can diagnose whether the issue is on the MCP server, the upstream provider, or the agent's tool selection logic.
Who It Is For
DevOps and AI platform teams running production agents that depend on MCP servers for search and need observability into tool-level performance.
Key Benefits
- Real-time monitoring of every MCP tool invocation
- Per-tool success rate and latency tracking
- Automatic alerting on degradation
- Routing decisions informed by health data
- Root cause isolation between MCP server, provider, and agent
Python Example
import requests, os, time, json
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
PLATFORMS = ['google', 'reddit', 'youtube', 'amazon', 'walmart']
def health_check() -> dict:
report = {}
for platform in PLATFORMS:
start = time.time()
try:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': platform, 'query': 'test'}, timeout=10)
latency = round(time.time() - start, 2)
data = resp.json()
result_count = len(data.get('organic', []))
report[platform] = {'status': 'ok', 'latency_s': latency, 'results': result_count}
except Exception as e:
report[platform] = {'status': 'error', 'error': str(e)}
return report
print(json.dumps(health_check(), indent=2))JavaScript Example
const PLATFORMS = ['google', 'reddit', 'youtube', 'amazon', 'walmart'];
async function healthCheck() {
const report = {};
for (const platform of PLATFORMS) {
const start = Date.now();
try {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ platform, query: 'test' })
});
const data = await resp.json();
report[platform] = { status: 'ok', latencyMs: Date.now() - start, results: (data.organic || []).length };
} catch (e) { report[platform] = { status: 'error', error: e.message }; }
}
return report;
}
console.log(JSON.stringify(await healthCheck(), null, 2));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