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MCP-Allure

by crisschan
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Python
Hybrid

MCP-Allure

MCP-Allure is a MCP server that reads Allure reports and returns them in LLM-friendly formats.

Motivation

As AI and Large Language Models (LLMs) become increasingly integral to software development, there is a growing need to bridge the gap between traditional test reporting and AI-assisted analysis. Traditional Allure test report formats, while human-readable, aren't optimized for LLM consumption and processing.

MCP-Allure addresses this challenge by transforming Allure test reports into LLM-friendly formats. This transformation enables AI models to better understand, analyze, and provide insights about test results, making it easier to:

  • Generate meaningful test summaries and insights

  • Identify patterns in test failures

  • Suggest potential fixes for failing tests

  • Enable more effective AI-assisted debugging

  • Facilitate automated test documentation generation

By optimizing test reports for LLM consumption, MCP-Allure helps development teams leverage the full potential of AI tools in their testing workflow, leading to more efficient and intelligent test analysis and maintenance.

Problems Solved

  • Efficiency: Traditional test reporting formats are not optimized for AI consumption, leading to inefficiencies in test analysis and maintenance.

  • Accuracy: AI models may struggle with interpreting and analyzing test reports that are not in a format optimized for AI consumption.

  • Cost: Converting test reports to LLM-friendly formats can be time-consuming and expensive.

Key Features

  • Conversion: Converts Allure test reports into LLM-friendly formats.

  • Optimization: Optimizes test reports for AI consumption.

  • Efficiency: Converts test reports efficiently.

  • Cost: Converts test reports at a low cost.

  • Accuracy: Converts test reports with high accuracy.

Installation

To install mcp-repo2llm using uv:

{
  "mcpServers": {
    "mcp-allure-server": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
      ]
    }
  }
}

Tool

get_allure_report

  • Reads Allure report and returns JSON data

  • Input:

    • report_dir: Allure HTML report path

  • Return:

    • String, formatted JSON data, like this:

{
    "test-suites": [
        {
            "name": "test suite name",
            "title": "suite title",
            "description": "suite description",
            "status": "passed",
            "start": "timestamp",
            "stop": "timestamp",
            "test-cases": [
                {
                    "name": "test case name",
                    "title": "case title",
                    "description": "case description",
                    "severity": "normal",
                    "status": "passed",
                    "start": "timestamp",
                    "stop": "timestamp",
                    "labels": [

                    ],
                    "parameters": [

                    ],
                    "steps": [
                        {
                            "name": "step name",
                            "title": "step title",
                            "status": "passed",
                            "start": "timestamp",
                            "stop": "timestamp",
                            "attachments": [

                            ],
                            "steps": [

                            ]
                        }
                    ]
                }
            ]
        }
    ]
}

Available Tools

1 tool
get_allure_reportC

read allure report and return json data

ParametersJSON Schema
NameRequiredDescriptionDefault
results_dirYes

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions reading and returning JSON data, but fails to disclose critical traits: whether this is a read-only operation (implied by 'read' but not explicit), potential side effects (e.g., file access permissions), error handling, or performance aspects (e.g., large report processing). The description adds minimal value beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise—a single sentence with no wasted words—and front-loaded with the core action. However, it is under-specified rather than efficiently concise; it could benefit from slightly more detail without losing brevity. It earns a high score for structure but loses a point due to under-specification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a tool to read and parse reports), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It does not explain the return format beyond 'json data' (e.g., structure, keys), error conditions, or dependencies. For a tool with one parameter and no structured support, more context is needed to be adequately helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, with 1 parameter ('results_dir') undocumented in the schema. The description does not compensate by explaining what 'results_dir' means (e.g., path to Allure results directory, expected format), its role in reading the report, or any constraints. This leaves the parameter's semantics unclear, failing to add meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'read[s] allure report and return[s] json data', which provides a basic verb+resource combination ('read allure report') and outcome ('return json data'). However, it's vague about what constitutes an 'allure report' (e.g., test results, specific file format) and doesn't differentiate from siblings (though none exist). It avoids tautology by not merely restating the name, but lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool—there are no alternatives mentioned (sibling tools are none), no context about prerequisites (e.g., requires an existing report directory), and no exclusions or comparisons. The description implies usage for reading reports but offers no further direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.7/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'get_allure_report' has a single, clearly defined purpose of reading Allure reports and returning JSON data.

Naming Consistency5/5

The single tool name 'get_allure_report' follows a clear verb_noun pattern (get + report). Since there is only one tool, consistency is inherently perfect with no deviations to assess.

Tool Count2/5

The server has only one tool, which feels thin for a reporting domain like Allure. A single tool for reading reports lacks essential operations such as generating, listing, or managing reports, making it under-scoped for typical use cases.

Completeness2/5

The tool surface is severely incomplete for an Allure reporting server. It only provides a 'get' operation for reports, missing critical functions like creating reports, listing available reports, updating report data, or deleting reports, which are essential for full lifecycle coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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