opentelemetry-instrumentation-mcp
OpenTelemetry mcp instrumentation
Decision gist · record as of 2026-08-14
Yes, if you are building or running MCP-based agentic workflows and need observability. The package is actively maintained, has no known vulnerabilities, and integrates cleanly into the OpenTelemetry ecosystem. Install it when you want to trace tool calls and LLM interactions; skip it if you have no observability infrastructure or are not using the MCP framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; requires an OpenTelemetry SDK and exporter already configured in your application.
- Low friction install with four runtime dependencies on the OpenTelemetry ecosystem.
- Active maintenance with a recent release (4 days old) and an established repository.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note material changes.
last release 2026-08-10 (4 days) · last repo commit 2026-08-10 · 7,377 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,716,888 downloads/mo, #1,865 on PyPI
Alternatives
Verify before relying
pip install opentelemetry-instrumentation-mcp
from opentelemetry.instrumentation.mcp import McpInstrumentor
McpInstrumentor().instrument()- Whether this instrumentation works with all MCP framework versions or has specific version constraints beyond what the fact sheet shows.
- What span attributes are captured beyond prompts, completions, and embeddings.
- Performance overhead of instrumentation on typical agentic workflows.
What it is and what it does
This package integrates the MCP Python SDK with OpenTelemetry to emit distributed traces from agentic workflows. When you call `McpInstrumentor().instrument()`, it hooks into MCP framework calls to generate spans that track tool invocations, prompts sent to LLMs, and completions received—giving you visibility into how your agent is using tools and interacting with language models.
By default, the instrumentation logs the actual content of prompts, completions, and embeddings to span attributes, which helps with debugging and evaluation but may expose sensitive user data. You can disable this by setting `TRACELOOP_TRACE_CONTENT=false` to reduce trace size and protect privacy. The package depends on the OpenTelemetry API and instrumentation base libraries, plus semantic convention definitions for AI workloads.
Use it for
- Debug agentic workflows by viewing the full trace of tool calls, prompts, and LLM responses in your observability backend.
- Monitor production MCP applications to detect tool call failures, latency spikes, or unexpected LLM behavior.
- Evaluate agent performance by analyzing spans to understand which tools are called most frequently and how long each step takes.
- Reduce trace volume in privacy-sensitive environments by disabling content logging while keeping structural tracing intact.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or running MCP-based agentic workflows and need observability.
The package is actively maintained, has no known vulnerabilities, and integrates cleanly into the OpenTelemetry ecosystem. Install it when you want to trace tool calls and LLM interactions; skip it if you have no observability infrastructure or are not using the MCP framework.
Install
opentelemetry-instrumentation-mcp on PyPI
Before you install
Low friction install with four runtime dependencies on the OpenTelemetry ecosystem. Active maintenance with a recent release (4 days old) and an established repository.
Requires Python 3.10 or later; requires an OpenTelemetry SDK and exporter already configured in your application.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note material changes.
Quickstart
pip install opentelemetry-instrumentation-mcp
from opentelemetry.instrumentation.mcp import McpInstrumentor
McpInstrumentor().instrument()Verify before relying
- Whether this instrumentation works with all MCP framework versions or has specific version constraints beyond what the fact sheet shows.
- What span attributes are captured beyond prompts, completions, and embeddings.
- Performance overhead of instrumentation on typical agentic workflows.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesopentelemetry-apiopentelemetry-instrumentationopentelemetry-semantic-conventions-aiopentelemetry-semantic-conventions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,716,888 / month, #1,865 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: opentelemetry_instrumentation_mcp-0.62.3-py3-none-any.whl
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See also opentelemetry-instrumentation-openai-agents · opentelemetry-instrumentation-bedrock · opentelemetry-instrumentation-haystack · opentelemetry-instrumentation-crewai · openinference-instrumentation-mcp · opentelemetry-instrumentation-django · opentelemetry-instrumentation-groq · opentelemetry-instrumentation-ollama · opencensus-ext-logging · oneagent-sdk