TraceHawk vs Langfuse: MCP Support in 2026

Model Context Protocol has become the dominant way AI agents interact with external tools. In 2025, MCP went from an Anthropic experiment to the default integration standard — GitHub, Linear, Slack, and hundreds of other tools now ship official MCP servers. If you're building an agent today, you're almost certainly using MCP.

That shift broke a core assumption in most observability tools: that tool calls are simple function invocations you instrument manually. MCP tool calls are network calls through a protocol layer — they have server identity, protocol-level errors, and latency characteristics that matter independently from the LLM call that triggered them. We looked at how TraceHawk and Langfuse handle this — and the gap is significant.

Feature comparison

What TraceHawk actually captures for MCP

Every MCP tool call generates a span with span_kind: MCP. The span carries the server name, tool name, full input parameters, duration, and status — captured automatically via the OpenTelemetry instrumentation layer. No manual logging required.

{
  "span_kind": "MCP",
  "mcp.server_name": "github",
  "mcp.tool_name": "create_pull_request",
  "mcp.tool_input": {
    "title": "Add auth middleware",
    "base": "main",
    "head": "feature/auth"
  },
  "duration_ms": 340,
  "status": "ok",
  "trace_id": "3e4f5a6b...",
  "parent_span_id": "1a2b3c4d"
}

MCP Analytics dashboard

These spans feed a dedicated MCP Analytics view — you can see per-server call frequency, error rate, p95 latency, and a time-of-day heatmap. When a server degrades (latency spikes, error rate increases), TraceHawk surfaces a degraded badge and can fire an alert through Slack, PagerDuty, or webhook.

What Langfuse shows for MCP

Langfuse traces LLM calls well — it's built around the prompt/completion paradigm. For MCP tool calls, you can log them manually as observations, but there's no automatic MCP instrumentation, no server-level analytics, and no degraded-server alerting. The MCP layer is invisible by default.

This isn't a criticism — Langfuse was built when MCP didn't exist. It excels at prompt versioning and dataset management. But if MCP is central to your agent architecture, you need tooling built for it.

When to use TraceHawk vs Langfuse

Use TraceHawk when

• ✓Your agents use MCP servers — filesystem, GitHub, Slack, databases
• ✓You need per-server latency and error analytics, not just per-LLM-call
• ✓You're running production agents and need alerting on MCP degradation
• ✓Cost attribution across MCP + LLM in one view matters
• ✓You want zero-config instrumentation via OpenLLMetry

Use Langfuse when

• →Prompt versioning and A/B testing is your primary need
• →You have an existing dataset management workflow built around Langfuse
• →Your agents are LLM-only with minimal tool use
• →You need human annotation on LLM outputs at scale

Conclusion

Both tools are genuinely good at what they were designed for. Langfuse is the best option if prompt management is your core workflow. TraceHawk is purpose-built for the MCP era — if your agents call external tools through MCP servers, it's the only option that treats those calls as first-class observability signals.

The free tier of both tools is comparable (50K spans/events per month). You can try TraceHawk with your existing Claude Code, LangGraph, or CrewAI agent in about 5 minutes — no changes to your agent code beyond a two-line init.