Platform / AI Monitoring

Monitor every layer of your AI infrastructure.

From MCP servers to inference providers, APIContext monitors the full AI stack — so you can see which providers are performing, catch schema drift before agents fail, and align every model with the right resilience and data protection boundary.

MCP server monitoringInference provider APIsTool contract checksMulti-step workflows
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MCP · live check · every 30s
125+global monitoring locations
24/7continuous AI infrastructure monitoring
OTELnative spans on every call
<5mto start monitoring your AI stack
End-to-End AI Infrastructure Monitoring

Every tool call. Every inference request. Verified outside-in.

APIContext monitors both MCP server tool calls and inference provider APIs — validating schema, latency, availability, and contract correctness at every layer of your AI stack.

tools/list24ms3 tools registered
search_docs(q='refunds')412msschema passed
query_db(sql='SELECT…')188ms14 rows
send_email(to='x@y')2.1sover SLO threshold
{
  "content": [{
    "type": "text",
    "text": "Refund policy...",
    "source": "docs/refunds.md"
  }],
  "isError": false
}
Inference provider monitoring

Know which AI providers are actually performing.

APIContext monitors inference provider APIs — OpenAI, Anthropic, Azure OpenAI, Google Gemini, and others — from the same global locations as your users. See latency, availability, and throughput side by side so you route to the provider that earns it.

Latency and availability across major inference providers
p50, p95, and p99 per model and endpoint
Instant alerts when a provider degrades
Coverage125+ data centers · all major clouds
All regions healthy
AWSAzureGoogleIBMAkamai
Full payloads

Simulate how a real AI client calls your tools.

APIContext runs full MCP sessions with realistic argument distributions. This is not a synthetic ping against a health check. See how your MCP servers work in production.

Replay captured sessions from real agents
Fuzz tool arguments within schema bounds
Verify tools/list stability across deploys
mcp-session.tsTypeScript
// simulate an AI client using your MCP server
import { mcpSession, assert } from '@apicontext/mcp';

export default mcpSession({
  server: 'https://mcp.acme.com',
  transport: 'sse',
  auth: oauth({ scope: 'mcp:read' }),
}, async (s) => {
  const tools = await s.listTools();
  assert.count(tools, 3);
  const r = await s.call('search_docs', { q: 'refunds' });
  assert.schema(r, 'ToolResult.v1');
  assert.contains(r.content[0].text, 'policy');
});
Provider alignment

Match each AI provider to your product needs.

Not every workload has the same availability requirement or data sensitivity. APIContext gives you the performance evidence to route high-stakes workloads to proven providers, keep sensitive prompts within compliant boundaries, and test failover paths before you need them.

SLO verification per provider and model
Data residency and boundary checks
Failover readiness testing across provider pairs
slo-board.yaml · 30-day window5/6 on target
serviceobjectivetargetliveburn rate
Payments API
Availability99.95%99.97%0.4×
Payments API
p95 latency250ms218ms0.6×
Accounts API
Availability99.9%99.94%0.3×
Plaid · supplier
Availability99.5%99.21%4.1×
Stripe · supplier
Availability99.95%99.96%0.5×
Auth · OAuth
Error rate0.1%0.04%0.2×
config as-codereview via PRlast sync · 12s ago
Works across multi-step journeys

Connect MCP servers, endpoints, and APIs end-to-end.

Connect synthetic journeys across internal and external MCP servers, HTTP endpoints, third parties, and APIs to verify end-to-end resilience.

Monitor both internal and external MCP servers in one flow
OAuth 2.1, PAT, mTLS, signed HMAC
Run from global POPs or VPC collectors
global reachability · mcp.acme.com
Key Features

Everything you need in production.

Safety checks

Flag tools that are unexpected and resources that are out of specification.

Latency SLOs

Separate SLOs for tools/list, individual tool calls, and inference provider endpoints — p50, p95, and p99.

Instant alerting

Ping Slack, PagerDuty, Incident.io, ServiceNow, and more when contracts break or providers degrade.

Per-tool uptime

Tool-level availability rather than just server-level availability.

Provider comparison

Side-by-side latency and availability across inference providers — so you always know who's performing.

OTEL native

Every MCP call and inference request emits OpenTelemetry spans — route signal to any compatible backend.

Signal ships OTEL-native into every tool your SRE team already uses

DatadogDynatraceSplunkGrafanaNew RelicHoneycombAkamaiPagerDutySlackOpsGenie
FAQ

Frequently asked questions

What is MCP monitoring?

MCP monitoring is the continuous outside-in verification of MCP servers — the tool-serving endpoints that AI agents call to perform actions. It involves running synthetic MCP sessions from external locations, exercising initialization, tool discovery, and real tool calls, then validating response schema, latency, and semantic correctness of each result.

Why do MCP servers need external monitoring?

MCP servers are called by AI models rather than human developers, which means failures are harder to detect through normal operational channels. A tool that returns a malformed schema or drifts its response format can silently degrade agent behavior — causing incorrect actions or task failures — without triggering conventional alerts. Continuous external monitoring provides the same reliability guarantees for AI tool infrastructure that SREs expect for production APIs.

What does APIContext check on each MCP tool call?

For each monitored MCP tool, APIContext verifies the server responds correctly to initialize and tools/list; declared tool schema matches actual response; tool call results match expected schemas and value ranges; latency is within acceptable bounds; and OTEL spans are generated at every step. Schema drift is flagged with a before/after diff.

Does monitoring an MCP server require changes to the server itself?

No. APIContext operates as an external MCP client — no code changes to your MCP server are required. You provide the server endpoint and authentication credentials; APIContext handles the MCP session lifecycle, tool enumeration, and continuous check execution from global locations.

What is AI inference provider monitoring?

Inference provider monitoring tracks the latency, availability, and API contract health of the LLM providers your applications call — including OpenAI, Anthropic, Azure OpenAI, Google Gemini, and others. APIContext runs synthetic inference requests from global locations and surfaces p50/p95/p99 latency, uptime, and schema conformance so you know which providers are performing and can route workloads accordingly.

How does APIContext help teams choose between inference providers?

APIContext gives you independent, continuous measurement of every provider's performance — not marketing claims or infrequent benchmarks. You can set SLOs per provider and model, receive alerts when a provider degrades, verify that data stays within required geographic or compliance boundaries, and test failover paths so your applications switch cleanly when a primary provider slips.

Explore more from APIContext

Start monitoring your AI infrastructure in 3 minutes.

Point APIContext at your MCP server or inference provider. We connect, enumerate, and start running synthetic sessions in under five minutes.

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