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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11When an AI agent does something unexpected, the useful questions are concrete: what request did it send to the tool, what response came back, and can that same exchange be run again against a changed server? Tapesh Chandra Das built mcpscope to address that debugging gap with a proxy that records MCP traffic for inspection and replay.
Why record MCP traffic?
A production failure can be difficult to reproduce in a test environment if the team cannot see the exact tool request and response that preceded it. In his April 1, 2026 DEV Community article, mcpscope’s author, Tapesh Chandra Das, describes building the tool to make those interactions inspectable and repeatable. He calls it “a transparent proxy.”
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That framing matters: mcpscope is intended to sit between an MCP client and server, intercepting JSON-RPC messages without requiring changes to the server. Das says it records requests, responses, latency, and errors. These are the project’s described capabilities; the article does not report independently measured deployment results.
How mcpscope’s record-and-replay loop works
- Put the proxy in the path. Configure the client to connect through mcpscope, or launch the MCP server through the proxy, using the setup appropriate to the transport.
- Capture representative interactions. Exercise the server through the normal client or test workflow so the proxy sees the requests and responses you want to reproduce.
- Export and inspect traces. Select the interactions to retain, then inspect and sanitize them before storing them or adding them to version control.
- Replay against the changed server. Run the captured set in CI or another test environment to check whether those requests still produce acceptable results.
- Define what counts as a regression. Choose whether failures include protocol errors, changed responses, latency thresholds, or schema differences. Decide how to treat volatile fields such as timestamps or generated identifiers.
Das’s article gives sample commands for a local server, a Python server, and an HTTP upstream. Its export example uses a limit of 200 traces, and a replay example includes a maximum-latency setting of 500 milliseconds and an option to fail on errors. Those are example command values, not validated defaults, benchmark results, or guarantees about performance.
What the dashboard and schema diff are for
Inspecting activity
The article describes a local dashboard at http://localhost:3000 that displays tool calls, latency percentile histograms, and error timelines. Percentile labels such as P50, P95, and P99 help organize observed latency, but their presence does not establish a performance result; the article reports no measured deployment statistics.
Checking for tool-schema changes
A separate workflow in the article compares a baseline tool-schema snapshot with a snapshot generated from the current server, for example during a pull request. A diff command can be configured to return a nonzero exit code when schemas change. That can flag upstream changes for review; a schema delta alone does not establish whether application behavior is broken or whether the change is intentional.
What replay can—and cannot—prove
Replay makes a selected set of recorded protocol exchanges repeatable under the chosen request-matching and response-comparison rules. It can help answer whether a changed server still handles those captured interactions as expected. It does not prove that the traces cover all relevant cases, that a full agent workflow is correct, or that downstream agent behavior will remain sound. Those require suitable scenario coverage and, where needed, separate tests of the client and agent.
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Comparison policy is part of the test, not an afterthought. Dynamic IDs, timestamps, or nondeterministic tool output may create differences unrelated to a meaningful regression. Teams need explicit rules for matching requests, ignoring expected volatile fields, deciding when to update recorded expectations, and failing CI. The policy should preserve meaningful changes rather than suppressing them for convenience.
Related MCP recording approaches
Other projects document related workflows, but their transports, replay models, and controls are not interchangeable with mcpscope’s described feature set.
| Tool | Documented approach | Useful distinction |
|---|---|---|
| mcp-recorder | Stores exchanges in cassette files; documents both replaying recorded responses to test a client and resending recorded requests to verify a changed server. | Its documentation covers HTTP, including Streamable HTTP/SSE, and stdio; matching strategies, CI integration, ignore controls, cassette updates, and specific redaction options are also documented. |
| mcptoolkit-mock | Documents proxy mode that forwards traffic to a real server while writing request/response pairs to JSONL, then replays the captured file. | Its guide also describes importing test execution logs. |
| mcporter | Documents capture of JSON-RPC traffic and replay of recorded responses without contacting the live server. | Its documentation warns that recordings can contain sensitive data. |
When evaluating an approach, check what is actually under test: a client against recorded responses, or a changed server receiving recorded requests. Then verify transport and integration fit, request matching and comparison controls, CI behavior, and how captured data is protected. Documentation for one tool should not be read as evidence that another offers the same feature.
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Protect trace data before keeping or sharing it
Requests and tool results may contain user arguments or private content. mcporter’s documentation specifically cautions that recordings may include credentials, private content, or customer data, and recommends review and redaction before sharing or committing. mcp-recorder documents its own controls for redacting server URL paths, named environment values, and patterns, and says HTTP headers are not stored in its cassettes. Those controls are specific to mcp-recorder; the cited mcpscope article does not establish equivalent redaction features.
- Inspect what your chosen recorder captures, including arguments, results, and connection details.
- Remove or mask sensitive values before traces leave a controlled environment.
- Limit access and retention, and avoid committing production traces by default.
- Check the actual tool’s redaction behavior rather than assuming another recorder’s protections apply.
Installation and availability
Das’s article gives this Go installation command: go install github.com/td-02/mcp-observer@latest. It describes mcpscope as open source. The hosted cloud version is described as a roadmap item, not as a currently available service. The cited article and comparison documentation report no named study statistics or independently measured results for mcpscope.
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