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Vetted Servers(9120)

0
0
High Cost
zakariaf icon

SecScanMCP

by zakariaf

Sec8

This is an enhanced security scanner test suite designed to detect a wide range of vulnerabilities in MCP (Model Context Protocol) servers, including prompt injection, tool poisoning, hardcoded secrets, and various code injection types.

Setup Requirements

  • ⚠️Requires Docker to run the scanner and potentially for dynamic analysis of target applications.
  • ⚠️Requires several external security tools (OpenGrep/Semgrep, Syft, Grype, TruffleHog, ClamAV, YARA, CodeQL) to be installed and accessible in the environment or Docker container.
  • ⚠️CodeQL analysis is resource-intensive, requiring significant RAM and CPU during database creation and analysis steps, potentially leading to long scan times for large repositories.
Verified SafeView Analysis
This project is a security scanner, which by its nature interacts with and tests for malicious patterns. The code demonstrates good practices for isolating dangerous operations (e.g., Docker containerization, `subprocess.create_subprocess_exec` over `shell=True`, secret masking in findings). However, any security testing tool carries inherent risks if misconfigured or used on unauthorized systems. The documentation explicitly outlines ethical usage and safe testing practices. The internal design follows clean architecture principles, which generally improves code quality and reduces security vulnerabilities within the scanner itself.
Updated: 2025-12-01GitHub→
0
0
High Cost
haksndot icon

haksnbot-tools

by haksndot

Sec5

An MCP server that enables AI (like Claude) to control a Minecraft bot for various in-game actions.

Setup Requirements

  • ⚠️Requires Node.js 18+.
  • ⚠️For `take_screenshot` functionality, OpenGL support (e.g., Mesa on Linux) and Xvfb are required for a virtual display.
  • ⚠️QuickShop-Hikari integration (economy tools) requires setting the `MC_SERVER_ROOT` environment variable to the Minecraft server's root directory.
  • ⚠️Initial Microsoft authentication for online-mode servers requires manual user interaction (device code flow) via a browser.
Review RequiredView Analysis
The server uses `child_process.exec` in `src/tools/economy.js` to query a QuickShop H2 database. This command executes `java -cp ... org.h2.tools.Shell -sql "${sql}"`, directly passing SQL as a string. While there's a basic `replace` for double quotes, this method is highly susceptible to SQL injection if an AI agent can control parts of the SQL input. Furthermore, `exec` can potentially lead to arbitrary command execution on the host system if the input can escape the SQL context and inject shell commands. This poses a critical vulnerability. Additionally, sensitive database files are temporarily copied to `/tmp` for read operations, presenting a minor information exposure risk if the host's `/tmp` directory is not secured. Other actions like `bot.chat()` or direct packet writes (`update_sign`) are standard for Minecraft bots but could be abused by an unconstrained AI for in-game griefing.
Updated: 2026-01-19GitHub→
0
0
Medium Cost
andrew-stclair icon

kali-mcp-server

by andrew-stclair

Sec9

This server provides a standardized interface for 22 essential penetration testing tools, designed for integration with AI assistants and automation platforms.

Setup Requirements

  • ⚠️Requires Docker for recommended setup.
  • ⚠️The container requires elevated Linux network capabilities (`NET_RAW`, `NET_ADMIN`, `NET_BIND_SERVICE`), which may be restricted in some environments or by security policies.
  • ⚠️Primarily designed to run within a Kali Linux environment (though containerized).
Verified SafeView Analysis
The project demonstrates robust security hardening. Input is heavily sanitized to prevent command injection, and all tool execution is whitelisted against an explicit ALLOWED_TOOLS list. Tools run via `subprocess.run` with a list of arguments, avoiding shell interpretation. The Docker container runs as a non-root user with a read-only filesystem and temporary filesystems mounted, further restricting potential damage. Each tool execution includes a 300-second timeout. The container requires elevated network capabilities (`NET_RAW`, `NET_ADMIN`, `NET_BIND_SERVICE`), which are necessary for many penetration testing tools but inherently increase risk. However, these are mitigated by the strong input validation and execution controls.
Updated: 2025-12-15GitHub→
0
0
Low Cost
Amitabh0954 icon

mcp-_server

by Amitabh0954

Sec10

A basic Node.js HTTP server designed to respond with a simple 'Hello, World!' message on any incoming request.

Setup Requirements

  • ⚠️Requires Node.js installed on the system.
Verified SafeView Analysis
The server is a minimal 'Hello, World!' HTTP server with no dynamic content, input processing, or external dependencies that could introduce vulnerabilities. No 'eval', hardcoded secrets, or malicious patterns were found. It is extremely safe.
Updated: 2025-11-25GitHub→
0
0
Low Cost
nik1097 icon
Sec9

Estimates the token cost of tool responses from an MCP server, helping developers and clients understand LLM inference expenses.

Setup Requirements

  • ⚠️Requires Python 3.13 or newer.
  • ⚠️Requires 'uv' for installation and execution (rather than just 'pip' and 'python').
  • ⚠️Requires an existing MCP server URL and potentially a Bearer token for authentication.
Verified SafeView Analysis
The tool is a client that makes HTTP requests to a user-provided MCP server URL. There are no 'eval' or similar dangerous functions, no obfuscation, and no hardcoded secrets. Authentication tokens are passed via command-line arguments. Any security implications would primarily arise from the server it connects to, or from the user exposing their token, not from vulnerabilities within the tool's code itself. It handles JSON parsing and network interactions robustly.
Updated: 2025-12-11GitHub→
0
0
High Cost
Martin-Hausleitner icon

browser-use-mcp-plus

by Martin-Hausleitner

Sec3

Provides a unified interface for browser automation, UI analysis (screenshots to text via LLM), Chrome DevTools interaction, documentation lookup (Context7), and isolated code execution (Docker VM) for agentic workflows.

Setup Requirements

  • ⚠️Requires Python virtual environment with specific dependencies (mcp, browser_use, playwright, gui_agents, pyautogui).
  • ⚠️Requires Google Chrome/Chromium browser installation with CDP enabled.
  • ⚠️Requires API keys for LLM services (e.g., OpenAI, Context7, Chutes.ai) which are typically paid.
  • ⚠️Requires a running Docker daemon and user access for VM functionality.
Review RequiredView Analysis
The `chrome-devtools.evaluate_script` tool allows arbitrary JavaScript execution in the controlled browser instance, posing a critical XSS/code injection risk if the MCP client input is untrusted. The `docker_vm_run` and `agent_s3_vm_run_task` tools enable arbitrary command execution within a Docker container, and `agent_s3_vm_run_task` explicitly supports `unsafe_exec` for unrestricted code execution, creating potential container escape or resource misuse risks if the LLM or input is compromised. While API keys are externalized, the inherent power of these tools requires a highly trusted execution environment and strictly vetted inputs to prevent malicious use.
Updated: 2026-01-17GitHub→
0
0
Medium Cost
nagalikith icon
Sec8

Automates routine tasks in Canvas LMS using an LLM agent.

Setup Requirements

  • ⚠️Requires CANVAS_API_TOKEN environment variable.
  • ⚠️Requires CANVAS_DOMAIN environment variable.
  • ⚠️Requires FIREWORKS_API_KEY environment variable (for LLM).
  • ⚠️The CanvasClient is configured to use 'http' scheme by default, which might need adjustment to 'https' for production Canvas instances if not handled implicitly by the domain.
Verified SafeView Analysis
The code uses environment variables for sensitive API keys (Canvas, Fireworks), which is good practice. Pydantic models are used for API arguments, providing some level of input validation against incorrect types. There's no explicit use of 'eval' or other highly dangerous functions. The `config.yaml` shows placeholders for API keys, which if used directly without environment variables, could lead to hardcoded secrets. The LLM-driven tool routing means that the security relies heavily on the LLM's adherence to the system prompt and the proper validation/sanitization of arguments before they are passed to the Canvas API client. While `smart_normalize_args` helps, it doesn't prevent semantically invalid inputs if the LLM misinterprets a command.
Updated: 2025-11-19GitHub→
0
0
Low Cost

achilles

by kasssandr

Sec9

Analyzes Calibre e-book library metadata to provide detailed statistics and insights.

Setup Requirements

  • ⚠️Python 3.6 or higher is required.
Verified SafeView Analysis
The tool explicitly states it only reads data from the local metadata.db file and does not modify the Calibre library. There are no indications of network requests, `eval` usage, or obfuscation, making it very safe to run locally.
Updated: 2025-11-23GitHub→
0
0
Medium Cost
Sec6

A generic Python server implementation likely used for testing remote communication or as a component within a distributed system, given its 'test_remote' prefix.

Setup Requirements

  • ⚠️A specific Python version may be required as indicated by the '.python-version' file, which could necessitate using a version manager like `pyenv` or `asdf`.
  • ⚠️Requires 'uv' for dependency management (indicated by `uv.lock` and `pyproject.toml`), which might necessitate a separate installation if not already present, rather than standard 'pip'.
  • ⚠️As a server component, it likely requires specific network port availability and configuration for operation, which could lead to port conflicts or firewall issues.
Review RequiredView Analysis
The actual code content for 'main.py' is not available for review. Therefore, a comprehensive security audit for 'eval' calls, obfuscation, or specific network vulnerabilities (e.g., exposed APIs, unauthenticated access, sensitive data handling) is not possible. The score reflects a neutral stance, acknowledging the unknown risks. Running unknown server code always carries inherent risks.
Updated: 2025-11-17GitHub→
0
0
Medium Cost
Daksh-io icon
Sec4

Enables LLMs to safely interact with databases and tools via a structured Model Context Protocol (MCP) server.

Setup Requirements

  • ⚠️Requires Groq API Key (paid service)
  • ⚠️Project is currently in progress and not finished, which may impact stability and feature completeness
  • ⚠️Potential for SQL injection due to lack of explicit query validation/sandboxing in current version
Review RequiredView Analysis
The project lists 'Query validation & sandboxing' as a future improvement, which implies it's not fully implemented in the current version. This is a critical risk, as direct execution of LLM-generated SQL queries without robust validation could lead to SQL injection, data manipulation, or denial-of-service vulnerabilities. While the design emphasizes safety, the implementation details for preventing malicious SQL are not provided and acknowledged as a future need.
Updated: 2026-01-17GitHub→
0
0
Low Cost
ofurkanuygur icon

tcmb_mcp

by ofurkanuygur

Sec9

Provides current and historical exchange rates, currency conversion, and rate comparisons from the Turkish Central Bank (TCMB).

Setup Requirements

  • ⚠️Requires Python 3.10 or higher.
  • ⚠️Needs network access to the Turkish Central Bank (TCMB) API (`https://www.tcmb.gov.tr`).
  • ⚠️Caching is SQLite-based and defaults to `~/.cache/tcmb-mcp-pro/tcmb_cache.db`, requiring write access to that location.
Verified SafeView Analysis
The project uses `defusedxml` for secure XML parsing to mitigate XML vulnerabilities and `aiosqlite` with parameterized queries, which prevents common SQL injection risks. Configuration is loaded from environment variables and `.env` files, which is good practice. No obvious hardcoded credentials, `eval` usage, or malicious patterns were found. Network calls use `httpx` with timeouts and retries, enhancing reliability.
Updated: 2025-11-30GitHub→
0
0
High Cost
anshu57 icon
Sec7

An agent-based chatbot integrating local and remote (Multi-Tool Communication Protocol - MCP) tools, supporting conversational AI, RAG, and stock price lookup through a FastAPI backend and a Streamlit frontend.

Setup Requirements

  • ⚠️Requires OpenAI API Key (Paid) for chat and embedding models.
  • ⚠️Requires ALPHAVANTAGE_API_KEY for the Stock Price tool.
  • ⚠️The MCPClient configuration includes a hardcoded local path for an 'arith' server ('/Users/anshugangwar/Desktop/mcp-math-server/main.py'), implying a specific local setup is necessary if MCP tools are enabled.
  • ⚠️Conditional dependency on 'langchain_mcp_adapters' if MCP tools are enabled.
Verified SafeView Analysis
The application avoids direct 'eval' and includes some input validation (e.g., for messages and uploaded files). However, the 'MCPClient' hardcodes a local subprocess command execution (`python3 /Users/anshugangwar/Desktop/mcp-math-server/main.py`) and an external HTTP endpoint, which introduces potential risks if the local script is untrusted, or if external MCP services are compromised. Users should ensure the security of any referenced local scripts and external services.
Updated: 2025-11-25GitHub→
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