"Security testing, penetration testing, and code auditing services" matching MCP servers:

A Model Context Protocol server for LLM fuzzing and testing, providing secure access to multiple AI providers through a standardized interface.

Open-source AI code review MCP server for local git diff auditing with deterministic security rules and AI-powered analysis using any OpenAI-compatible model.

Aggregates and summarizes security research from IEEE, NIST, CISA, and blogs with a live-streaming web UI.

An MCP server that lets Claude Code delegate coding tasks such as code generation, review, refactoring, and test writing to a local Ollama instance running a Qwen3 model.

An MCP server that recommends UI components and utility methods from private code repositories using AI to enhance code reuse and development efficiency.

Scans prompts for PII and masks or redacts sensitive data locally before sending to an LLM, supporting multiple anonymization modes.

Connects Kimi Code with Claude Code, enabling Claude to delegate bulk codebase reading to Kimi (256K context) for cost savings, while Claude focuses on reasoning and code edits.

Provides AI-powered assistance for Microsoft Business Central (BC) development, enabling users to get expert help with debugging, coding, upgrades, testing, and more through specialized AI agents.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

Provides coding agents with typed classification, yes/no checks, scoring, ranking, and question-answering tools that return calibrated probabilities for fast, reliable decisions.

Enables Claude Code to route complex software analysis tasks to Gemini for long-context execution tracing, cross-system impact analysis, performance bottleneck detection, and hypothesis testing, including multi-turn AI-to-AI conversations.

Enables Claude Code to escalate complex code-analysis tasks to Google's Gemini AI, supporting multi-turn conversations, execution-flow tracing, cross-system impact analysis, performance bottleneck detection, and hypothesis testing.

Enables comprehensive code reviews using multiple AI models (O3, Gemini, Claude Opus) and consolidates results with GPT-4, focusing on bugs, security, performance, and best practices.

Enables Claude Code to escalate complex code analysis to Google's Gemini AI for multi-model workflows, including AI-to-AI conversational debugging, execution tracing, cross-system impact analysis, performance bottleneck detection, and hypothesis testing over large codebases and traces.

Enables Claude Code to escalate complex code analysis tasks to Google's Gemini AI for large-context execution tracing, cross-system impact analysis, performance bottleneck detection, hypothesis testing, and AI-to-AI conversational debugging.

Enables Claude Code to escalate complex analysis tasks to Google's Gemini AI for execution-flow tracing, cross-system impact analysis, performance bottleneck detection, and hypothesis testing. Facilitates AI-to-AI dialogue for iterative debugging across large codebases and logs.

An MCP server for automatic code review using OpenRouter, enabling performance, clean code, SOLID, async, error handling, and security analysis with direct file modification.

Enables Claude Code to escalate complex code analysis to Gemini for execution-flow tracing, cross-system impact analysis, performance bottleneck detection, hypothesis testing, and AI-to-AI conversational debugging over large codebases, logs, and traces.

Enables Claude Code to escalate complex code analysis to Google's Gemini AI, supporting large-context debugging, execution-flow tracing, cross-system impact analysis, performance bottleneck detection, and hypothesis testing through multi-turn AI-to-AI conversations.

Enables Claude Code to escalate complex code analysis tasks to Google's Gemini AI for execution-flow tracing, cross-system impact analysis, performance bottleneck detection, hypothesis testing, and AI-to-AI conversational debugging over large codebases, logs, and traces.