About flakyguard
flakyguard analyzes per-test outcomes across CI runs to classify tests, calculate flakiness rates, and recommend quarantine when a threshold is met. It provides this analysis through a CLI or local MCP server, using only the supplied run data.
Key features
- 01Per-test pass, fail, and skip history
- 02Evidence-based test classification
- 03Flakiness rate calculation
- 04Threshold-based quarantine list
- 05Input validation and verdict
Use cases
- 01Assessing tests across CI runs
- 02Identifying tests with inconsistent results
- 03Selecting tests for quarantine review
MCP support
Exposes an `analyze_test_flakiness` tool to MCP clients. The tool accepts CI run results and optional classification and quarantine thresholds, then returns test histories, a quarantine list, and a verdict.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
1Saved tool list · updated 1w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| runs | array | Yes | CI runs to analyze (at least one) |
| min_runs | integer | No | Minimum evidence runs (passes + failures) before a test is classified as stable or flaky (optional, default 2) |
| quarantine_threshold | number | No | Flakiness-rate threshold for the quarantine list (optional, default 0.5) |
Details
From the project READMEflakyguard
"That test is flaky" is usually a feeling. flakyguard turns it into evidence. Feed it the per-test outcomes from a set of CI runs and it builds the record for every test:
- Per-test history — how many runs contained the test, and how many times it passed, failed, or was skipped
- Evidence runs — passes plus failures; a skip is recorded but never counts as evidence, because a test that did not run proves nothing either way
- Classification — a test needs a minimum amount of
evidence (two evidence runs by default) before it earns
a verdict:
stable_passif it never failed,stable_failif it never passed,flakyif it did both, andinsufficient_datauntil then - Flakiness rate — the minority outcome's share of the evidence runs, so a test that splits its results evenly rates 0.5 and a mostly reliable one rates close to 0
- Quarantine list — the flaky tests whose rate meets the quarantine threshold (0.5 by default), worst first, each entry carrying the pass/fail counts behind its rate
- Verdict —
quarantine_recommendedwhen the list is non-empty, otherwiseclean
Rates are rounded to four decimal places. Everything is
computed locally from the runs you supply: no network, no
configuration, no history kept between calls. Bad input —
a run with no results, a status outside
passed / failed / skipped, the same test twice in
one run — is rejected with an error naming the offending
path, and no partial analysis is produced.
CLI
npx @reneturcios/flakyguard analyze --input args.json
npx @reneturcios/flakyguard --version
args.json contains the full arguments object:
{
"runs": [
{
"run_id": "run-101",
"results": [
{ "test": "checkout totals", "status": "passed" },
{ "test": "search ranking", "status": "failed" }
]
},
{
"run_id": "run-102",
"results": [
{ "test": "checkout totals", "status": "failed" },
{ "test": "search ranking", "status": "failed" }
]
}
],
"min_runs": 2,
"quarantine_threshold": 0.5
}
min_runs and quarantine_threshold are optional. The
analysis is printed as JSON.
Exit codes: 0 on success, 1 on a runtime error
(including an unreadable or invalid input file, or
invalid input), 2 on a usage error.
MCP server
npx -y @reneturcios/flakyguard mcp
MCP client config:
{
"mcpServers": {
"flakyguard": {
"command": "npx",
"args": ["-y", "@reneturcios/flakyguard", "mcp"]
}
}
}
Tools
-
analyze_test_flakiness— Analyze test flakiness across CI runs.runs(array, required, at least one) — runs as{run_id, results}objects, where each result is{test, status}andstatusis one ofpassed,failed,skipped; a test may not appear twice in one runmin_runs(integer, optional, >= 1, default 2) — minimum evidence runs before a test is classified as stable or flakyquarantine_threshold(number, optional, 0 to 1, default 0.5) — flakiness-rate threshold for the quarantine list
Returns
{run_count, test_count, tests, quarantine, summary, verdict}.
Docker
docker run -i ghcr.io/cloudcorprecords/flakyguard
docker run --rm -v "$PWD:/w" ghcr.io/cloudcorprecords/flakyguard analyze --input /w/args.json
Development
npm install
npm run build # tsc -> dist/
npm test # tsx --test test/*.test.ts (pure local, no network)
npm run dev # run the CLI from source
License
MIT — Copyright (c) 2026 Rene Turcios
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.