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pentestMCP MCP Server

RamKansal/pentestMCP

Score: 62.5 Rank #5629 MCP Server
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pentestMCP: AI-Powered Penetration Testing via MCP, an MCP designed for penetration testers.

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Overview

RamKansal/pentestMCP is a Python tool. pentestMCP: AI-Powered Penetration Testing via MCP, an MCP designed for penetration testers. Topics: cybersecurity, mcp, mcp-client, mcp-server, python.

Ranked #5629 out of 24846 indexed tools.

Ecosystem

Python No license 3 pypi/wk
cybersecuritymcpmcp-clientmcp-serverpython

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 109

109 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → moderate activity

Issue Health 25% 75%

3/4 issues closed → decent issue management

Contributors 10% 4

4 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads 13% 3/wk

3 weekly installs → early adoption

Forks 22
Description Good
License None

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

Expand your description to 150+ characters for better discoverability

License low impact

Add an MIT or Apache-2.0 license to signal trust and enable adoption

Freshness high impact

Last commit was 47 days ago — a recent commit would boost your freshness score

Install in your agent

claude mcp add pentestmcp -- uvx pentestmcp
codex mcp add pentestmcp -- uvx pentestmcp
{"mcpServers":{"pentestmcp":{"command":"uvx","args":["pentestmcp"]}}}
hermes mcp add pentestmcp --command uvx --args pentestmcp

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AgentRank score for RamKansal/pentestMCP
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Matched Queries

topic:mcptopic:mcp-server

From the README

# pentestMCP: AI-Powered Penetration Testing via MCP

**pentestMCP provides a powerful bridge between Large Language Models (LLMs) and practical penetration testing tools through the Model Context Protocol (MCP). This project functions as an MCP Server, exposing a curated suite of over 20 standard security assessment utilities (Nmap, Nuclei, ZAP, SQLMap, etc.) as callable 'tools'. This allows AI agents within MCP-compatible clients (like Claude Desktop or specific VS Code setups) to leverage these utilities for automated and interactive security analysis tasks.**

The goal is to enable natural language control over complex security workflows, making pentesting capabilities more accessible and integrated into AI-driven environments. This work is inspired by [Laurie Kirk](https://github.com/LaurieWired/)'s [GhidraMCP](https://github.com/LaurieWired/GhidraMCP).

---

## Table of Contents

- [Core Concepts & Architecture](#core-concepts--architecture)
- [Key Features](#key-features)
- [Pre
Read full README on GitHub →

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