Aibolit MCP Server
The Aibolit MCP Server analyzes a single Java file to identify its most critical design issue for AI agents to fix.
Prioritizes significant problems: Focuses on design issues that would significantly improve code quality, maintainability, and stability when addressed.
Ignores cosmetic issues: Deliberately skips minor or easy-to-fix problems to concentrate on impactful refactoring opportunities.
AI integration: Integrates with AI agents like Claude Code via the MCP protocol to guide efficient code refactoring efforts.
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., "@Aibolit MCP Serverfind the most critical design issue in this Java class and suggest a fix"
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.
MCP Server for Aibolit, Java Code Analyzer
If you use AI agents, such as Claude Code, Cursor, or Windsurf, for code refactoring, you may enjoy using this MCP server. Your AI agent, when you ask it to "make code better," may wonder what exactly needs to be improved. Sadly, it may often overlook important problems. AI agents, by design, pay more attention to cosmetic issues, which are "low-hanging fruits" for them. This MCP server will give your agent a hint: what is the most critical design issue in the code. Then, the agent will refactor it and fix the issue.
First, install Node, Npm, Python, Pip, and aibolit:
aibolit --versionThen, add this MCP server to Claude Code
(or simply edit ~/claude.json, but it's not recommended):
claude mcp add aibolit npx aibolit-mcp-server@0.0.6Then, restart Claude Code and ask it something along these lines: "Find the most critical design issue in my code base and fix it."
How to Contribute
To test this project, simply run the following commands (you'll need Node 18+, Npm, and GNU make installed):
npm install
makeIf everything builds correctly after your changes, submit a pull request.
Available Tools
1 toolfind_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.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
TDQS
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.
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.
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.
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.
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.
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. Dates show when Glama detected each change.
1 tool update
v1.0.0- Changed
find_the_most_critical_design_issue1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
1 tool update
- First observed
find_the_most_critical_design_issue
TDQS
With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined.
Single tool, so no inconsistency. The name follows a verb_noun pattern (find_issue).
One tool for a specific purpose (finding design issues in one file) is borderline but acceptable for a narrow scope.
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
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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