"Information about bug classification" matching MCP servers:

AI-powered smart contract security analysis for AI agents and developers, enabling scanning of Solidity repos for vulnerabilities.

Bridges MCP clients to Language Server Protocol servers like rust-analyzer, pyright, and gopls, exposing type information, references, diagnostics, and refactoring as 31 MCP tools. This lets AI coding agents reason about code with IDE-level understanding across single- or multi-language workspaces.

Enables agents to search indexed documents and GitHub repository code through stdio-exposed tools, returning relevant evidence for grounded answers and code reviews.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

Enables independent, read-only adversarial review of candidate test cases and bug findings, returning a review delta that authoring agents reconcile before writing final artifacts.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enables bug localization by analyzing a repo and bug report with an ant-swarm LLM, returning a HOT/WARM/COLD heatmap and JSON audit trail via MCP.

MCP server that provides npm package information for AI agents during TypeScript development.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Enables AI agents to search and retrieve details about Odoo code elements (models, fields, views, etc.) with exact file locations using MCP tools.

Provides coding agents with a compact map of a TypeScript/TSX codebase, enabling direct answers about symbol locations, imports, dependencies, and references while reducing token usage.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Enables AI coding agents to evaluate code snippets and learn from execution outcomes through local bio-inspired associative memory, delivering instant zero-token bug and antipattern reflexes without LLM inference.

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Enables AI assistants to dynamically generate technology-specific prompts and modular templates for PRD, codebase analysis, and bug analysis through programmable tools, supporting 31 technology stacks and multiple analysis dimensions.

Routes narrow text-processing tools (classification, extraction, summarization, etc.) to budget models to reduce token consumption on the main model.

An MCP server that gives your IDE or agent access to Google Gemini with autonomous codebase exploration, enabling deep code analysis, architectural reviews, and bug hunting.

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

Exposes technical debt management to AI coding assistants, enabling them to find, prioritize, and resolve TODO/FIXME/BUG comments across codebases via MCP tools.

Connects Claude Desktop and Claude Code to Bitbucket Cloud, enabling searching PRs, commits, diffs, and file content for AI-assisted bug triage and impact analysis.

Enables autonomous web-contract verification through MCP tools for planning, classifying, checking, and recording bug patterns, with access to a shared community corpus that strengthens collective detection.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Exposes the squad-dev workflow as deterministic MCP tools, enabling task classification, risk scoring, specialist reviewer selection, and advisory verdict consolidation for code changes.

Exposes Lachesis's code navigation tools over MCP, letting LLM agents ask precise compiler-level questions about dataflow, taint, callers, and guards in source code.

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.

Deterministic, offline root-cause analysis for coding agents: find the commit that introduced a bug, and verify a fix is complete before committing.

An MCP server for gitingest. It allows MCP clients like Claude Desktop, Cursor, Cline etc to quickly extract information about Github repositories including repository summaries, project directory structure, file contents, etc

A high-performance MCP server for intelligent documentation search, proactive bug detection, and semantic analysis of codebases.