Skip to main content
Glama
cqfn
by cqfn

MCP-Server für Aibolit, Java Code Analyzer

DevOps von Rultor.com

machen Codecov Code-Treffer Lizenz

Wenn Sie KI-Agenten wie Claude Code , Cursor oder Windsurf zur Code-Refaktorierung verwenden, könnte Ihnen dieser MCP- Server von Nutzen sein. Wenn Sie Ihren KI-Agenten bitten, den Code zu verbessern, fragt er sich möglicherweise, was genau verbessert werden muss. Leider übersieht er dabei oft wichtige Probleme. KI-Agenten achten konzeptbedingt eher auf kosmetische Probleme, die für sie leicht zu erreichen sind. Dieser MCP- Server gibt Ihrem Agenten einen Hinweis: Welches ist das kritischste Designproblem im Code? Anschließend refaktorisiert der Agent den Code und behebt das Problem.

Installieren Sie zunächst Node , Npm , Python , Pip und aibolit :

aibolit --version

Fügen Sie dann diesen MCP- Server zu Claude Code hinzu (oder bearbeiten Sie einfach ~/claude.json , aber das wird nicht empfohlen):

claude mcp add aibolit npx aibolit-mcp-server@0.0.4

Starten Sie dann Claude Code neu und stellen Sie ihm eine Frage in dieser Art: „Finden Sie das kritischste Designproblem in meiner Codebasis und beheben Sie es.“

So können Sie beitragen

Um dieses Projekt zu testen, führen Sie einfach die folgenden Befehle aus (Sie müssen Node 18+, Npm und GNU make installiert haben):

npm install
make

Wenn nach Ihren Änderungen alles korrekt erstellt wird, senden Sie eine Pull-Anfrage.

Available Tools

1 tool
find_the_most_critical_design_issueA

Analyze one Java file. Find the most serious design flaw. It must need immediate refactoring. Ignore cosmetic or minor issues. Fix the one problem that will best improve code quality. Code quality means maintainability, readability, loose coupling, and high cohesion. Point out the problem and where it is in the file.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYes

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description partially discloses behavior: it analyzes one Java file, ignores cosmetic issues, and identifies the problem with its location. However, it does not specify whether the tool is read-only or if it makes changes, nor how the analysis is performed or what the output format is.

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

Conciseness5/5

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

The description is extremely concise at 6 sentences, front-loaded with the core action, and each sentence adds unique value: scope, focus, exclusion criteria, quality definition, and output. No redundant or unnecessary information.

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

Completeness3/5

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

The description is adequate for a simple tool with one parameter and no output schema, explaining the core function clearly. However, it lacks details on output format, performance expectations, or edge cases (e.g., empty file, multiple flaws), which would enhance completeness given the tool's analytical nature.

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

Parameters3/5

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

The only parameter is 'path', and the description clarifies it expects a Java file path, adding context beyond the bare schema. However, it does not detail the format or any constraints on the path, and schema description coverage is 0%, so the description provides minimal additional meaning.

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

Purpose5/5

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

The tool name and description clearly indicate the tool identifies the most serious design flaw in a Java file, emphasizing critical issues over cosmetic ones. It distinguishes itself from a general code review tool by focusing on immediate refactoring needs.

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

Usage Guidelines4/5

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

The description specifies when to use the tool (to find critical design issues) and provides criteria for what constitutes a serious flaw (maintainability, readability, loose coupling, high cohesion). It implicitly advises against using it for minor issues, and with no sibling tools, this provides sufficient context.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • Changedfind_the_most_critical_design_issue1 field changed
      • removedInput schema / additionalProperties
        Removed value: -false
  2. 1 tool update
    • First observedfind_the_most_critical_design_issue

TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined.

Naming Consistency5/5

Single tool, so no inconsistency. The name follows a verb_noun pattern (find_issue).

Tool Count3/5

One tool for a specific purpose (finding design issues in one file) is borderline but acceptable for a narrow scope.

Completeness2/5

The server only covers a single operation (analyze one file for one issue). Lacks features like batch processing, multiple issue types, or suggestions, leaving significant gaps for a design review tool.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Related MCP Connectors

Related MCP Servers

  • A
    license
    A
    quality
    D
    maintenance
    A Model Context Protocol server that enhances AI-generated code quality through comprehensive analysis across 10 critical dimensions, helping identify issues before they become problems.
    1
    90
    Apache 2.0
  • A
    license
    A
    quality
    D
    maintenance
    A Model Context Protocol server that provides development standards and best practices context to AI coding assistants, ensuring generated code adheres to team/project conventions.
    3
    10 npm
    1
    MIT
  • -
    license
    Not graded
    quality
    Not graded
    maintenance
    A universal MCP server that acts as a code quality gate for AI assistants, providing pre-generation guidance, post-generation review, and root cause analysis to improve code quality.
    -