MCP Failure Lab

MCP serverAI & models

Your AI can stress-test the tools it relies on by deliberately causing small, controlled failures and checking what keeps working. This community-built toolkit brings chaos engineering and resilience testing to your setup. The point is to surface weak spots during a test instead of during real work.

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What your AI can do with it

  • Run controlled failure tests against your AI's tools
  • Check what keeps working when a failure hits
  • See how setups recover after something breaks
  • Find weak spots before they cause real problems

Getting started

  1. Save this item in Your setup as a reference.
  2. Read the source or reference documentation for its setup requirements. Saving it here does not connect it to your AI.
  3. Check this page for availability before trying to install it through Ahel.

From the project's README

As published by anilloutombam/mcp-failure-lab in README.md.

Reproduce MCP timeouts, cancellation races, transport loss, and invalid responses with repeatable tests and CI reports.

Documentation · Compatibility · Project page

Example output (duration varies):

$ npx mcp-failure-lab demo
MCP Failure Lab — Demo
Running a real 500ms delay scenario...

Scenario: Deterministic delay demo
Outcome: success
Duration: ~500 ms
Assertions: passed

Why this exists

Real MCP clients behave differently when things break. A timeout may leave a connection usable; an interrupted response may close it. A malformed reply may be rejected by one client and accepted by another.

Failure Lab makes those cases repeatable so you can check both the failed call and what happens next.

Real-world findings

  • Python SDK #3522: Python mcp 2.2.0 stayed closed after an interrupted HTTP response, rejected the next request, and raised an ExceptionGroup during cleanup.
  • Rust SDK #1283: rmcp 3.4.0 accepted an invalid response containing both result and error over stdio and HTTP.
  • Ruby SDK #589: mcp 1.6.1 accepted responses missing jsonrpc or declaring jsonrpc: "1.0" over stdio and HTTP in all three repeats. The Ruby report records all 60 executions; the results and upstream finding are included in the Observatory export.
  • Java SDK 2.0.1 accepted jsonrpc: "1.0" over both transports (#1156). Over stdio, responses missing jsonrpc or containing both result and error left the next ping timing out (#1157). See the Java report.
  • Duplicate-response comparison: all five tested SDKs completed the next ping. TypeScript reported the duplicate through its error callback; the other harnesses surfaced no call-level duplicate error.

The SDK comparisons describe specific tested versions, not every release. See the versioned reports for reproduction steps and limitations.

Quick start

Requires Node.js 22.19.0 or newer and npm.

npx mcp-failure-lab demo

The demo needs no API key, external server, or global installation. To test your own MCP client, connect it to Failure Lab over stdio or local Streamable HTTP:

npx mcp-failure-lab serve
# Or:
npx mcp-failure-lab serve --transport http

The stdio process waits for a client; it is not an interactive terminal command. Configure your client to launch it, or follow the getting started guide. Press Ctrl+C to stop a manually started server.

HTTP defaults to http://127.0.0.1:3000/mcp. It provides neither authentication nor TLS; do not expose it to an untrusted network.

Test your own MCP server

External targets support HTTP and stdio. After the repository setup, install the official GitHub MCP server with its executable on PATH and set GITHUB_PERSONAL_ACCESS_TOKEN in your environment. Then run:

npm run dev -- run examples/scenarios/github-get-me.json \
  --target examples/targets/github-stdio.json

The example calls GitHub's read-only get_me tool. Its target configuration uses envFrom to pass the token from your environment rather than storing it in JSON. For your own scenario and target configuration, use:

npx mcp-failure-lab run scenario.json --target target.json

External runs check tool results, deadlines, and adapter setup and cleanup. They do not inject faults into another server; Failure Lab is not a proxy.

See the external targets guide for prerequisites, credential handling, and configuration.

What Failure Lab can break

The built-in server exposes these tools for testing client behavior:

ToolBehavior
pingReturns a deterministic health response
protocol_ping_livenessSends a bounded protocol ping during its in-flight call
delayWaits for a bounded duration before returning
hangRemains pending until the client cancels
disconnectInterrupts the active transport while a request is in flight
malformed_messageViolates one selected JSON-RPC response rule exactly once
duplicate_responseSends the same JSON-RPC response twice for one request
response_after_cancellationSends one late response for a cancelled stdio request
session_lossInvalidates the caller's legacy HTTP session

Protocol ping is distinct from the ping tool. Built-in scenarios default to MCP 2026-07-28; protocol liveness requires scenario protocolVersion: "2025-11-25". Both public transports accept legacy clients.

See the fault tools reference for timing bounds, activation, cancellation, cleanup, and transport limits.

How recovery testing works

Trigger a fault, record its outcome, then make a second request on the same connection. A detected fault does not prove recovery; the follow-up must succeed too.

Scenario observe calls run after the primary call, including errors and timeouts. For example, duplicate-response.json calls duplicate_response, then uses ping to check that the connection remains usable:

# From a repository checkout
npm run dev -- run examples/scenarios/duplicate-response.json

This checks post-fault behavior, not an automatic recovery policy. A stdio disconnect terminates the server process and requires a new process and connection.

See Scenarios for outcome, duration, result, and observer assertions.

SDK / transport evidence

MCP Failure Observatory collects SDK and transport evidence. The repository reports preserve tested versions, methods, and limitations.

Results vary by SDK, version, transport, and protocol. A passing run is evidence for that combination, not a guarantee for every client.

CI usage

Save a scenario file with explicit expectations and timeouts, then produce a JUnit report:

npx mcp-failure-lab run scenario.json --report junit > junit.xml

Pin the Failure Lab package version in CI so upgrades do not change the test environment unexpectedly.

Exit codes: 0 means expectations passed, 1 means the scenario could not be loaded or executed, and 2 means an assertion failed. An expected timeout can pass; an unexpected success can fail.

See Reporting for JSON, JUnit, and lifecycle diagnostics.

Documentation

Detailed guides live at mcplab.dev: Getting started · CLI · Architecture · Troubleshooting.

Contributing

See CONTRIBUTING.md for setup, tests, and the contribution workflow. Planned work is tracked in GitHub Issues.

If Failure Lab helps you test an MCP integration, consider starring the repository.

License

MIT

Signals

GitHub stars
3
Last commit
Oct 2026
Weekly downloads
401
Weekly_downloads
393 weekly_downloads
Advanced
Delivery
mcp-failure-lab MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
io-github-anilloutombam-mcp-failure-lab
Source
github.com/anilloutombam/mcp-failure-lab