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Vetted Servers(9120)

0
0
Medium Cost
Benjamincf0 icon

mcp-servers

by Benjamincf0

Sec8

Provides a framework and examples for building AI-accessible tools as Micro-Controller-Protocol (MCP) servers, enabling large language models to interact with external services like weather APIs, robotics, and job search platforms.

Setup Requirements

  • ⚠️Requires Python 3.14+ (this is likely a typo as 3.14 is not yet released; the Dockerfile uses 3.10, but pyproject.toml files explicitly state `>=3.14`).
  • ⚠️Requires `uv` for dependency management and execution.
  • ⚠️Specific external API keys (e.g., SerpAPI key for job search) must be set via environment variables (.env file).
  • ⚠️Extensive manual configuration required for integration with Claude/Docker MCP gateway (editing multiple YAML/JSON config files with absolute paths).
Verified SafeView Analysis
The project avoids direct use of 'eval' or other highly dangerous functions. API keys are handled via environment variables (.env file), which is a good practice. Network requests use the httpx library. The MCP server infrastructure, including Docker gateway setup, introduces complexity that might have its own security considerations, but the provided server code itself appears reasonably secure given its scope. The use of SerpAPI implies reliance on an external service.
Updated: 2025-11-23GitHub→
0
0
Medium Cost
pardeepdogra icon

mcp-server

by pardeepdogra

Sec1

It is unclear what the specific use case is without the provided source code, but the name 'mcp-server' suggests it is a server application.

Setup Requirements

  • ⚠️Source code not provided for analysis. Cannot verify requirements, dependencies, or potential malicious behavior.
  • ⚠️Running unknown server software without source code analysis is highly risky and not recommended.
Review RequiredView Analysis
Cannot perform a security audit as no source code was provided for analysis. Without visibility into the code, it is impossible to assess risks like 'eval' usage, obfuscation, network vulnerabilities, hardcoded secrets, or malicious patterns. Therefore, a score of 1 is assigned, indicating maximum unknown risk. It is strongly advised not to run this without thorough manual inspection of the full source code.
Updated: 2025-11-23GitHub→
0
0
High Cost

wordpress-mcp-server

by KuljeetSinghXDA

Sec8

Provides AI-powered content creation, optimization, and management for WordPress sites using specialized AI agents and tools.

Setup Requirements

  • ⚠️Requires Node.js 18+ and npm
  • ⚠️Requires Docker and Docker Compose (Recommended)
  • ⚠️Requires a WordPress site with REST API enabled and an Application Password configured
  • ⚠️Requires at least one paid AI Provider API Key (OpenAI, Anthropic, Google, or Groq)
Verified SafeView Analysis
The system handles sensitive AI API keys and WordPress application passwords via environment variables, which must be secured. It makes outbound network calls to external AI providers and Google Search, which is inherent to its function. Containerization with Docker helps isolate the environment.
Updated: 2025-11-19GitHub→
0
0
Medium Cost
Rizwan723 icon

MCP-Security-Proxy

by Rizwan723

Sec9

A security proxy for Model Context Protocol (MCP) to test and defend against vulnerabilities in LLM tool interactions within a cloud-native Dockerized environment.

Setup Requirements

  • ⚠️Docker is required for deployment and orchestration.
  • ⚠️A local GPU (NVIDIA driver) is required for optimal performance of the `custom-llm` service.
  • ⚠️OpenAI or Google API keys (`CLOUD_OPENAI_API_KEY`, `CLOUD_GOOGLE_API_KEY`) are needed if using cloud LLM services.
  • ⚠️Local LLM models (e.g., GGUF files) must be available in the `./models` directory for the `custom-llm` service.
Verified SafeView Analysis
The MCP Bridge (security proxy) is exceptionally well-designed for its purpose, incorporating multiple layers of defense: a rule-based detector for known attacks, a statistical anomaly detector, a semantic (DistilBERT) prototypical learning detector, and an optional MAML-based few-shot adaptation detector, all within a weighted ensemble that defaults to 'ATTACK' in ambiguous cases. Network isolation is enforced through internal Docker networks for tools, preventing direct external access. Input validation is performed using Pydantic, and ReDoS protection is present in regex-based rules. The primary security risk lies in the intentionally vulnerable 'MCP Tools' services (e.g., filesystem, fetch, sqlite) when their 'SAFE_MODE' flags are set to 'false' (which is the default in the provided `docker-compose.yml` for testing purposes). This design is a feature for vulnerability testing, not a flaw in the proxy's defensive capabilities. A minor theoretical risk is the `torch.load(..., weights_only=False)` call for model loading, which could allow arbitrary code execution if the model files themselves were compromised by an attacker, though in a research context generating its own models, this is standard. The LLM proxy endpoint ( `/v1/chat/completions`) performs basic forwarding without explicit security checks in the provided code, but it is configured to use internal LLM services by default.
Updated: 2026-01-19GitHub→
0
0
High Cost
dshnayder icon

kubeapi-mcp

by dshnayder

Sec8

Enables AI agents to interact with Kubernetes and Google Kubernetes Engine (GKE) clusters using natural language prompts.

Setup Requirements

  • ⚠️Requires kubectl and gcloud CLI to be installed and configured with appropriate credentials and default project/location.
  • ⚠️The `install.sh` script may require `sudo` privileges to move the binary to `/usr/local/bin`.
  • ⚠️If using `--server-mode http`, the server listens on all network interfaces (0.0.0.0) and requires a firewall or other security measures to restrict access.
  • ⚠️Installing for Claude Code CLI (`install claude-code`) requires the `claude` command-line tool to be installed and in the system's PATH.
Verified SafeView Analysis
The server's HTTP mode binds to all network interfaces (0.0.0.0) by default, which is explicitly documented as requiring external firewalling for security. No 'eval', obfuscation, hardcoded secrets, or direct malicious patterns were identified. The tool relies on standard Kubernetes and gcloud authentication mechanisms (kubeconfig, Application Default Credentials). The ability to apply/delete Kubernetes resources is inherent to its administrative purpose and requires careful AI agent interaction, not a security flaw in the server itself.
Updated: 2025-11-26GitHub→
0
0
High Cost
shanewwarren icon

mcp-google-slides

by shanewwarren

Sec9

Enables LLMs to create and manage Google Slides presentations via browser-based OAuth 2.1 authentication.

Setup Requirements

  • ⚠️Requires Bun runtime (not Node.js)
  • ⚠️Requires manual setup of Google Cloud OAuth 2.0 credentials (Desktop app type, Slides API, Drive API, OAuth consent screen configuration, test users)
  • ⚠️Requires environment variables MCP_GSLIDES_CLIENT_ID and MCP_GSLIDES_CLIENT_SECRET to be set with your Google OAuth credentials.
Verified SafeView Analysis
The server employs browser-based OAuth 2.1 with PKCE for authentication, which is a secure method for desktop applications. Tokens are stored locally with appropriate file permissions (0600). Environment variables (MCP_GSLIDES_CLIENT_ID, MCP_GSLIDES_CLIENT_SECRET) are used for OAuth credentials, which is best practice. However, the `src/auth/config.ts` file contains placeholder client ID/secret values directly in the code, which could be a theoretical risk if they were ever active and not overridden by environment variables. The application relies on Google's API to fetch images from provided URLs, shifting the security responsibility for URL validation and content fetching to Google's infrastructure. No direct use of `eval()` or arbitrary command execution from user input was found.
Updated: 2026-01-19GitHub→
0
0
Medium Cost
rgeertsema icon
Sec1

Provides an MCP (Managed Code Provider) engine for managing book-related resources and tools, facilitating AI-driven content generation and editing.

Setup Requirements

  • ⚠️Requires `BOOK2_ROOT` environment variable or `--book2-root` CLI argument to define the book's working directory.
  • ⚠️Requires Python 3.8 or higher.
Review RequiredView Analysis
CRITICAL: The `resources/read` method in `bookwriting_server/server_stdio.py` (the primary server implementation invoked by `engine.json`) is vulnerable to path traversal. It constructs a `Path` object directly from the user-provided `file://` URI (e.g., `Path(uri.replace("file://", ""))`) without validating that the resulting path is contained within the designated `book2-root`. This allows an attacker to read arbitrary files on the host filesystem that are accessible to the server process, by simply crafting a URI like `file:///etc/passwd`. The `mcp_resources_read` function in `bookwriting_server/__main__.py` (used for selftests) has the same vulnerability. While `server_fastmcp.py` implements necessary path validation (`is_relative_to`), it is not the configured entry point for this engine.
Updated: 2025-12-01GitHub→
0
0
Low Cost
GGoYoungHee icon

ai-mcp-server-test

by GGoYoungHee

Sec9

This boilerplate facilitates the rapid development of Model Context Protocol (MCP) servers in TypeScript, enabling them to expose various AI-powered tools and resources to MCP clients.

Setup Requirements

  • ⚠️Requires a Hugging Face API token (HF_TOKEN) for the image generation tool, which may incur costs from Hugging Face for usage.
  • ⚠️Requires Node.js version 18 or higher for execution (some development dependencies prefer Node.js 20+).
  • ⚠️For local testing with Cursor MCP, an absolute path to the 'build/index.js' file must be manually configured in './.cursor/mcp.json'.
Verified SafeView Analysis
Hugging Face API token is correctly retrieved from configuration or environment variables (HF_TOKEN), reducing the risk of hardcoded secrets. Input schemas for tools and prompts are validated using Zod, which helps prevent malformed inputs and potential injection attacks. No direct usage of 'eval' or other highly dangerous patterns was identified. The overall architecture delegates external AI model interactions to a well-known SDK.
Updated: 2025-11-28GitHub→
0
0
Low Cost
nieuwoudt icon
Sec7

Provides an AI context server for educational data, integrating with the D6 school management platform to answer natural language queries about student information, academic records, and school operations for AI clients like Claude Desktop.

Setup Requirements

  • ⚠️Requires D6 API credentials (username, password, base URL) for full functionality, potentially requiring an authorized D6 account.
  • ⚠️Requires manual configuration within the Claude Desktop application (or other MCP client) via a specific JSON entry.
  • ⚠️Access to all real D6 API data endpoints (e.g., student marks, parent-child relationships) may still be limited or require activation from D6, despite the server's robust mock data fallbacks and optimized tools addressing client-side data truncation issues.
Verified SafeView Analysis
The server includes hardcoded test API credentials (username/password) as fallback values, which poses a security risk if not overridden by environment variables in production. Hardcoded JWT secret fallbacks are also present for development environments. However, the system implements robust security measures including environment variable prioritization, input validation with Zod, CORS configuration, and dedicated authentication and tenant-scoping middleware for Fastify-based deployments. Production deployments on Cloudflare Workers and Vercel Edge Functions prevent mock mode if configured for production environments, adding a layer of safety.
Updated: 2026-01-16GitHub→
0
0
Low Cost
iampunnu icon
Sec9

Provides an MCP server that exposes simple utility functions as tools for multi-agent systems.

Setup Requirements

  • ⚠️Requires Python 3.13 or newer.
  • ⚠️Requires the 'mcp' Python package to be installed.
Verified SafeView Analysis
The provided source code is simple and does not contain obvious critical vulnerabilities like 'eval', direct OS command execution from user input, or hardcoded secrets. The tools' implementations (string formatting and basic arithmetic) are inherently low risk. Network exposure as an HTTP server means general network security practices should be followed, but no application-level exploits are evident in the given code.
Updated: 2025-12-02GitHub→
0
0
Low Cost
bhakti259 icon
Sec9

Manages and tracks personal expenses, offering features like adding, listing, updating, deleting, and summarizing expenses with natural language date parsing and categorization.

Setup Requirements

  • ⚠️Requires Python 3.12 or newer
Verified SafeView Analysis
The server uses `aiosqlite` with parameterized queries, effectively preventing SQL injection vulnerabilities. No 'eval', 'exec', or other notoriously dangerous patterns were found. The database path is set to `/tmp/expense_tracker.db`, which is suitable for ephemeral environments like FastMCP Cloud, reducing risks of persistent data leakage on the host system. No hardcoded secrets or API keys are present in the provided source.
Updated: 2025-11-28GitHub→
0
0
High Cost
AuraFriday icon

system_mcp

by AuraFriday

Sec7

Provides cross-platform desktop automation and management capabilities, optimized for AI access to interact with applications like a human.

Setup Requirements

  • ⚠️Windows is fully implemented, but macOS and Linux support for some advanced UI features (like full UI element scanning and detailed interaction) is explicitly 'in progress' or basic.
  • ⚠️On Windows, UAC Elevation prevents interaction with elevated windows from a non-elevated process.
  • ⚠️On macOS, 'Accessibility Permissions' are required for full automation features, requiring user approval.
  • ⚠️On Linux, specific external tools like `wmctrl`, `scrot`, `ImageMagick`, or `gnome-screenshot` may need to be installed if the preferred `pywinctl` library is not available or insufficient for certain operations.
  • ⚠️Performance considerations exist for UI scanning (100-500ms) and screenshots (50-200ms), which could impact real-time AI interactions.
Verified SafeView Analysis
The server's core functionality involves direct operating system interaction, including command execution via `subprocess.Popen`, file system operations (read/write), and direct UI manipulation. This grants very powerful control over the user's machine. Security relies heavily on the `tool_unlock_token` and the host system's permissions. If the token is compromised, an attacker could gain full control. The code itself does not show immediate signs of malicious intent or 'eval' of untrusted input, and input parameters are validated. However, due to its inherent power, misuse or compromise of the token poses a high risk. It is safe to run only if the environment is secure and the unlock token is protected.
Updated: 2025-12-02GitHub→
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