"stack and dns mcp" matching MCP servers:

Visual tech-stack inventory of any codebase: languages, frameworks, databases, AI SDKs, infra.

Enables developers and AI agents to compress context, plan token budgets, rerank results, patch code, analyze ASTs and dependencies, isolate stack traces, compile polyglot types, solve deterministic math, schedule constraints, judge A/B tests, and diff agent memory checkpoints through 12 specialized tools.

Enables Claude to inspect, analyze, and grade local Git repositories with tools for stats, TODO scanning, git history, large file detection, health scores, and tech stack fingerprinting.

MCP server for the Symbols.app platform, enabling AI coding assistants to search docs, generate components, audit code, manage projects, and deploy via the smbls stack.

Zero-config MCP server that connects local codebases to AI assistants, providing secure project tree, regex search, file reading, and tech stack tools locally.

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Enables mapping JavaScript error stack traces back to original source code, extracting context information to help developers locate and fix issues.

Enables decoding minified production stack traces to original TypeScript source locations using source maps, with tools for retracing, code context retrieval, and sourcemap auditing.

Enables local, deterministic review of GitHub and GitLab pull request diffs for secret leaks, OWASP injection issues, WCAG 2.2 AA/EU EAA accessibility gaps, EU AI Act transparency, and database RLS risks, producing risk scores, merge decisions, committable suggestions, and SARIF exports.

Enables AI agents to perform step-through debugging of Python, JavaScript/Node.js, and Rust programs using the Debug Adapter Protocol, with support for breakpoints, variable inspection, and stack traces.

An MCP server that enables analyzing and querying GitHub repositories through the GitHub Chat API, allowing users to index repositories and ask questions about their code, architecture and tech stack.

Provides AI-callable tools for modernizing legacy applications, including stack detection, API contract extraction, SQL to Flyway conversion, and migration effort estimation.

Reads a directory of projects and reports on their stack, git state, deployment targets, and monetization readiness.

Enables converting Python tracebacks into root-cause analysis and runnable unittest/pytest suites, including edge-case generation and assertion auditing. It provides local MCP tools for decompiling stack traces, generating tests, and reviewing test coverage gaps.

AI-assisted COBOL to modern stack bridge that parses COBOL programs, maps to Python/Java/Go, generates test harnesses, and plans migration for legacy mainframe modernization.

Enables full-stack compiler development across the hw-native-sys workspace by combining repository operations (git health, code search, task running) with a knowledge layer for architecture docs, task routing, and abstraction/pass pipeline information.

Enables AI agents to run deterministic, fully local static security and compliance audits of code, configurations, and dependencies through 32 MCP tools covering 24 vulnerability categories. It also detects the project stack, generates risk reports and checklists, suggests stack-aware remediations, and re-validates fixes to confirm vulnerabilities were eliminated.

Enables AI assistants to debug PHP applications through Xdebug's DBGp protocol, providing full debugging control including breakpoints, variable inspection, stack traces, and advanced features like watch expressions and code coverage.

Sub-1ms cross-stack AST contract layer and compiler verification engine mapping React/Next.js routes to FastAPI and SQLAlchemy models for AI coding agents.

Read-only MCP server for cross-stack impact analysis across Java Spring Boot backend and TypeScript frontend linked through OpenAPI.

Enables LLM clients to interact with the GNU Debugger (GDB) for comprehensive debugging and binary analysis. It provides a wide range of tools for program execution control, memory examination, stack analysis, and disassembly.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

Connects coding and chat agents to a cloud API for searching and managing conversational memories, indexing repositories, finding code by behavior, and ranking likely bug locations from symptoms or stack traces.

Enables AI assistants to perform software-development tasks such as deep research, codebase mapping, context curation, requirements and user-story generation, full-stack starter project scaffolding, and multi-step workflow execution.

Enables AI assistants to research, plan, generate requirements, user stories, task lists, and full-stack starter projects, analyze codebases, curate context, and run multi-step software development workflows through MCP tools.

Enables AI assistants to conduct research, plan projects, generate PRDs, user stories and task lists, scaffold full-stack starter kits, map codebases, curate context, and execute multi-step development workflows.

Deterministic AI code review with audit records, providing stack-specific rulesets and governance for coding agents.

An MCP-powered compliance copilot for SaaS stacks, enabling structured audit workflows including stack detection, module wiremapping, implementation directives, code verification, and security/infrastructure/legal readiness gates.

Analyze GitHub repositories into structured JSON with tech stack detection, dependency analysis, health signals, and security checks. No AI, fully deterministic. Available as CLI and MCP server.

Local-first, read-only MCP server for Claude Code that indexes and searches full-stack projects (FastAPI + React + PostgreSQL) with SQLite-backed FTS, cross-reference graph, and file-watcher auto-reload.

Enables AI agents to access VS Code's LSP-based code navigation and native debugging tools (definitions, references, outline, breakpoints, stepping, stack frames, variables, and evaluate) via the MCP protocol.

Enables AI agents to analyze and diff binaries by driving Ghidra headless or inside its GUI, exposing native reverse-engineering tools such as listing functions and segments, searching strings, decompiling, disassembling, and following cross-references. Ships with a devcontainer and supports local stdio or remote HTTP deployment.

Enables AI assistants and MCP-compatible clients to research, plan, generate requirements, analyze codebases, manage tasks, create user stories, and scaffold full-stack starter projects through specialized development tools.

This server automatically validates consistency between AI-generated frontend and backend code by enforcing OpenAPI 3.0 specifications. It injects smart constraints into AI prompts to ensure that generated data structures and API calls remain synchronized across the entire stack.

MCP server that guides an AI agent through stack trace triage, from raw error to the commit and PR that introduced it.

Enables AI assistants to conduct deep research, map codebases, curate context, generate requirements/user stories/task lists, scaffold full-stack starters, and execute multi-step workflows.

A self-hosted MCP server that indexes your codebase and provides AI assistants with deep context including file tree, full-text search, git history, dependencies, and stack detection, all without sending your code to third parties.

Enables AI assistants to research topics, map and analyze codebases across 35+ languages, curate context, and generate requirements documents, user stories, task lists, and full-stack starter projects. It also supports multi-step workflow execution and AI-native task management through a CLI, interactive REPL, or MCP client integration.

Tracks the complete lineage and DNA of every line of code, enabling impact analysis, bug risk prediction, and code archaeology through deterministic analysis of git history and AST.

A FastMCP-based tool for analyzing logback log files to detect code defects and generate automated fix suggestions. It features intelligent stack trace filtering and token optimization to efficiently identify and resolve application errors.