allure-mcp-analyzer
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@allure-mcp-analyzerAnalyze the failures in allure-results and tell me which root causes block the release."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
š Allure MCP Analyzer ā AI-driven root-cause analysis for test failures
An MCP server + CLI that reads Allure results, clusters failures by normalized error signature (deterministic, no LLM), and then asks an LLM to explain each cluster ā product bug, test bug, infrastructure, flaky, or data.
This is the pattern I use to turn a 300-red-test CI run into "3 root causes, here is what to do" in seconds. The clustering step is what makes it cheap and reliable: the LLM analyzes a handful of clusters, not every test.
⨠What's inside
Area | Highlights |
Parsing | Reads |
Clustering | Normalizes volatile tokens (ids, hex, timestamps, paths, numbers) ā stable signature ā clusters sorted by impact |
Root-cause analysis | One LLM call per cluster; strict JSON contract ( |
Pluggable LLM |
|
MCP server | Tools: |
CLI |
|
Reports | Markdown (PR comment / Slack) with a summary table + collapsible affected-tests lists; JSON for dashboards |
Agent Skills |
|
CI | Lint + tests on 3.11/3.12, demo report uploaded as an artifact |
Related MCP server: Regression-JIRA Integration System
š Layout
src/allure_mcp_analyzer/
āāā parser.py # Allure results ā TestResult
āāā cluster.py # signature normalization + clustering
āāā rca.py # prompt, LLM call, JSON contract ā RootCause
āāā report.py # Markdown / JSON rendering
āāā llm.py # LLM protocol, FakeLLM, AnthropicLLM
āāā server.py # FastMCP server (5 tools)
āāā cli.py
examples/allure-results/ # 9 sample results ā 3 clusters (assertion, timeout, locator)
tests/ # unittest / pytest
.claude/skills/ # agent skill for CI triage
.mcp.json # register the server with Claude Code / Cursorš Quick start
pip install -e ".[dev]" # core + tests
pytest -q # 17 tests, all offline
# CLI on the bundled example (FakeLLM when no API key is set)
allure-analyze examples/allure-results --top 3Real analysis:
pip install -e ".[llm,mcp]"
export ANTHROPIC_API_KEY=... # see .env.example
export ANTHROPIC_MODEL=... # https://docs.claude.com/en/docs/about-claude/models
allure-analyze ./allure-results --md report.mdš¤ As an MCP server (Claude Code / Cursor)
.mcp.json is already in the repo ā from the repo root:
claude
> Use allure-analyzer to triage ./allure-results and tell me which clusters block the release.Or invoke the bundled skill directly: /allure-triage.
š§ How clustering works
"AssertionError: expected total 59.98 but got 49.99"
"AssertionError: expected total 120.00 but got 100.00"
ā normalize()
"AssertionError: expected total <n> but got <n>" ā one cluster, 3 testsTimeouts on a shared dependency collapse the same way, so an outage shows up as one
infrastructure cluster instead of fifty unrelated reds.
š CI integration
The workflow uploads report.md / report.json as an artifact on every run. To post the
Markdown as a PR comment, pipe it through gh pr comment --body-file report.md.
š License
MIT
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Agentic testing: HyperExecute jobs, test failure triage, SmartUI visual diffs, a11y audits
BuildPulse CI test analytics for AI agents ā flaky tests, coverage, and CI run history.
Direct access to Cypress tests results and accessibility reports in your AI workflow.
Flaky test detection, root cause analysis, and fix suggestions for development teams.
Related MCP Servers
- AlicenseCqualityDmaintenanceA server that converts Allure test reports into LLM-friendly formats, enabling AI models to better analyze test results and provide insights about test failures and potential fixes.111Apache 2.0
- FlicenseNot gradedqualityDmaintenanceEnables intelligent analysis of regression test failures and automatic discovery of solutions in JIRA. Analyzes test logs using AI-driven algorithms and matches errors with relevant JIRA issues through natural language interactions.-
- FlicenseBqualityDmaintenanceProvides tools to analyze test failures, cluster similar failures, and detect flaky tests from input or log files, helping QA teams debug and triage issues.3-
- AlicenseNot gradedqualityDmaintenanceEnables AI models (Claude, ChatGPT, GitHub Copilot) to run and analyze local tests, rerun failures, and orchestrate QA workflows using existing UI and API test frameworks.21MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/gurevich89/allure-mcp-analyzer'
If you have feedback or need assistance with the MCP directory API, please join our Discord server