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Generic APM can show that a request crossed the network or that a downstream service failed without identifying the MCP operation, the tool involved, or whether the tool itself returned an error. To answer “Why are my MCP tool calls failing?” instrument both sides of the MCP request, propagate trace context across the client/server boundary, and record MCP-level result semantics alongside downstream spans. This is an implementation guide, not a first-person account of a build or benchmark.
Why ordinary APM traces can miss an MCP failure
An MCP tool call is not just an HTTP request. The relevant protocol operation is tools/call, and a useful trace needs to identify which tool was invoked and whether the MCP tool result succeeded. A network span can be healthy even when the tool returns an MCP error; conversely, a broad exception may show that something failed without identifying the MCP method or tool.
The MCP Python SDK’s OpenTelemetry documentation says: “Every server you create emits an OpenTelemetry span for every message it handles.” That is a useful starting point, but the server span must carry meaningful MCP and tool attributes, and the client and server traces must be connected. See the MCP Python SDK OpenTelemetry documentation.
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The MCP Python SDK documents the GenAI operation name execute_tool and the gen_ai.tool.name attribute for tool calls. A span with those identifiers is more informative than an undifferentiated request span because it lets you find the operation and the tool involved.
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Also preserve the protocol context needed to diagnose the exchange, such as the MCP method, request identifier, and session or transport context where applicable. Record status and error details in a way that distinguishes a handler exception from a tool result that explicitly reports failure. Avoid adding sensitive request content merely to make spans seem more detailed.
Make MCP result errors visible
A successful transport exchange does not prove that the tool succeeded. The Python SDK documentation says a handler exception or a tool result with is_error=True marks the span as an error. Instrumentation and dashboards should preserve this distinction instead of treating only connection failures or thrown exceptions as errors.
When investigating a failed call, check the MCP span’s status and error metadata as well as the transport span. If the request reached the server and the server returned a tool-level error, that is a different failure location from a request that never completed the MCP exchange.
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Propagate trace context across the client and server
To answer “How do I trace an MCP tool call across the client and server?”, ensure trace context crosses the MCP boundary. The Python SDK describes automatic W3C trace-context propagation when both sides use its SDK. The evolving GenAI semantic-convention guidance specifies MCP request metadata at params._meta as the propagation mechanism. Consult the current OpenTelemetry GenAI MCP semantic conventions and the SDK documentation for the versions you deploy.
Propagation only helps when both ends extract and inject compatible context. If the client and server produce separate traces, inspect their SDK and transport configuration, and confirm the metadata is actually transmitted. Do not assume that seeing spans on both services means they belong to the same trace.
Connect the MCP span to downstream work
A tool handler often calls another service or a database. Instrument those dependencies so their spans are children or otherwise correlated with the MCP server operation. OpenTelemetry’s demo pairs MCP server instrumentation with HTTPX client instrumentation, allowing the tool work and outbound HTTP call to appear in a shared trace. See the OpenTelemetry demo service documentation.
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That trace structure helps distinguish failures in the MCP handler from timeouts or errors in a downstream API. The demo also notes an important gap: its standard-library logs go to stdout and are not correlated with traces by default. A trace may therefore explain the request path while leaving logs as a separate source unless log correlation is configured.
Use a location-based troubleshooting sequence
- Check initialization. If the client never establishes the MCP connection or session, investigate setup, transport, and protocol compatibility before looking for a tool span.
- Check tool listing. If the expected tool is absent, examine the listing operation and server registration rather than assuming a
tools/callfailure. - Find the
tools/callspan. Filter by the MCP method andgen_ai.tool.name, then inspect request identifiers and available session or transport context. - Inspect MCP result status. Determine whether the handler raised an exception or returned a result with
is_error=True; do not infer success from a completed network exchange. - Follow downstream spans. Check correlated HTTP, API, or database operations for errors, timeouts, and latency that could explain the tool result.
- Confirm trace continuity. Verify that trace identifiers match across client and server and that context propagation is enabled for the SDK and transport in use.
- Use logs as supporting evidence. Correlate logs with trace identifiers where possible; stdout logs are not automatically linked to traces in the OpenTelemetry demo.
Choose instrumentation for your SDK and data needs
Instrumentation options are not interchangeable. Compare them against the language and SDK version you run, client and server coverage, transport support, propagation behavior, MCP error semantics, downstream instrumentation, metrics, payload controls, and the queries your backend supports. The OpenTelemetry MCP attribute registry says its legacy MCP attributes moved to the GenAI semantic-conventions repository; check the current definitions instead of adopting old keys uncritically. The conventions are evolving.
One specific limitation matters if you use telemetry.dev’s integration: its documentation describes support for the official TypeScript MCP SDK v2 packages and says MCP v1 is not supported. It does not emit metrics. Arguments and successful tool results are not captured by default; enabling payload capture remains subject to SDK controls such as masking and maximum attribute length. Treat capture as a privacy and data-handling decision, not a default debugging setting. See telemetry.dev’s MCP integration documentation.
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Pick a backend based on the workflow you need
The instrumentation creates trace data; the observability backend determines how you explore and retain it. Google documents a Cloud Trace workflow for remote MCP server calls and OTLP export in its Cloud Trace MCP documentation. Elastic describes an OpenTelemetry-to-Elastic APM walkthrough with trace waterfalls, latency percentiles, error tracking, and service maps in its MCP tool-call walkthrough.
These vendor documents explain their own workflows, not a neutral head-to-head evaluation. Choose based on whether the backend can show the MCP span, its linked downstream work, error details, and the relevant logs for your environment; do not infer that one is universally best from a feature description.
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