"Information about bug classification" matching MCP servers:

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

Receives UI feedback captured by the PickFix browser extension—element picks, inline text rewrites, page comments, and recorded bug steps—and hands it to a local coding agent to fix in the current repository, with screenshots and per-item results reported back to the extension. It also pairs the extension per machine, queues batches so only one session claims each, and keeps everything loopback-only and untrusted.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Enables MCP-capable clients to query the tool registry, check install status, get tool recommendations for CTF or bug-bounty work, and run installed security tools through a governed execution path.

Bug-report triage and fix-dispatch MCP for vibe coders. Ask your AI coding agent for a plain-English diagnosis of any user-reported bug — with evidence, blast radius, and a paste-ready fix prompt — then dispatch an automated GitHub fix PR without a second LLM key. 71 tools, Supabase-backed, MIT SDK. npx @mushi-mushi/mcp

Enables AI assistants to interact with Google's Jules coding agent API for autonomous coding tasks, allowing users to delegate bug fixes, refactoring, testing, and feature development with scheduling capabilities and human-in-the-loop approval workflows.

Integrates Google's Jules coding agent with AI assistants to delegate autonomous coding tasks like bug fixes, refactoring, and tests, with support for scheduling recurring maintenance tasks and human-in-the-loop plan approval.

Connects AI assistants to a local Codex engine for performing deep, project-level code reviews and automated refactoring. It enables context-aware bug fixes and multi-file analysis through a standardized bridge between modern AI clients and local development environments.

Integrates the Qwen3-Coder 30B parameter model with Claude Code through 5 specialized tools for code review, explanation, generation, bug fixing, and optimization. Optimized for 64GB RAM systems with advanced performance settings including flash attention and parallel processing.

Enables orchestration of multiple Jules AI workers for tasks like code generation, bug fixing, and review using the Google Jules API. It features git integration, a shared memory system, and real-time activity monitoring for complex, multi-agent development workflows.

Full-stack AI agent for Livewire v4.2+ / Laravel 13 / PHP 8.4+ with planning, memory, bug fixing, version control, GitHub integration, and design conversion.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

Enables users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Shared debugging memory for AI coding agents. Agents search, report, patch, and verify bug fixes through 5 MCP tools. Verified by proof, not upvotes.

An MCP server exposing hybrid RAG search over SEC 10-K filings and an AI bug-fixing agent as callable tools, enabling MCP clients like Claude Desktop to query financial documents and propose test-verified code fixes through natural language.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

Enables AI assistants to create and manage asynchronous coding tasks through Google's Jules API, including code generation, bug fixing, and test creation.

Local-first MCP server that indexes all projects on your machine and provides coding agents with briefings about each project's stack, services, and run instructions, so they can start sessions with context across all repos. No cloud, no accounts, no telemetry.

MCP server that enables Claude Code to communicate with other Claude Code agents over HTTP, allowing users to ask questions about remote codebases or delegate coding tasks.

MCP bridge for calling local coding-agent CLIs (Codex, Claude) from another agent, enabling bounded tasks like code review, verification, and bug hunting.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Bridges Large Language Models with Language Server Protocol interfaces, allowing LLMs to access LSP's hover information, completions, diagnostics, and code actions for improved code suggestions.

Provides code completion, bug fixing, and test generation for multiple programming languages, integrating with the Mistral Codestral API to enhance code development processes.

An autonomous code-reasoning agent that parses repositories using tree-sitter, indexes symbols into SQLite, and answers questions about the codebase through an agentic loop powered by Claude via OpenRouter.

Enables AI coding agents to access real-time VSCode LSP diagnostics, type information, and code navigation without slow tsc/eslint checks, plus symbol renaming and file operations.

A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.

Runs as an MCP server providing file system operations, shell execution, and system information tools, allowing Claude or any MCP client to edit files and control the machine.

Lets AI coding agents ask a Rails app about itself: 35 read-only tools answer from the schema, models, routes, controllers, views, jobs and conventions, over MCP or the command line, even when the app cannot boot.

Enables coding agents to query a parsed graph of a repository and to recall what earlier sessions learned about it, both stored in FalkorDB behind one traversal. It exposes eight MCP tools over stdio for locating symbols, tracing calls and impact paths, and writing or recalling user- and repo-scoped facts, procedures, and handoffs.