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Vetted Servers(8554)

0
0
Low Cost
monolithic-lab icon

modal-mcp-server

by monolithic-lab

Sec8

Provides an API for managing the lifecycle (creation, status, stopping, deletion) of Modal applications.

Setup Requirements

  • ⚠️Requires `modal`, `fastmcp`, and `mcp` Python packages.
  • ⚠️Requires Modal authentication (e.g., `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET` environment variables or `modal login`).
  • ⚠️Utilizes Modal's experimental API for app listing, which might require a specific Modal SDK version or be subject to change.
Verified SafeView Analysis
The server exposes an API to manage Modal applications, relying on the security of the Modal SDK and the permissions configured for the underlying Modal account. It generates Python code as templates, but does not execute arbitrary user-provided code directly. Destructive operations (stop, delete) are noted as placeholder implementations within the server, meaning the server itself doesn't directly perform these actions, but rather suggests they are handled by external Modal deployment processes.
Updated: 2025-11-30GitHub→
0
0
Low Cost
TL-Developer icon

mcp-servers

by TL-Developer

Sec8

Defines an MCP server for launching npmlens, a tool likely used for analyzing and managing npm packages.

Setup Requirements

  • ⚠️Requires Node.js and npm/npx to be installed on the system.
Verified SafeView Analysis
The provided source code is a configuration file defining a command to execute an external package via 'npx'. While the configuration itself is benign, running 'npx -y npmlens-mcp@latest' implies trust in the 'npmlens-mcp' package, which could introduce risks if the package itself is compromised or malicious. The '-y' flag bypasses user confirmation.
Updated: 2025-11-19GitHub→
0
0
Low Cost
aelvion icon
Sec8

Orchestrates build, deploy, and infrastructure workflows for applications.

Setup Requirements

  • ⚠️Docker required for local dependency management (Redis, Celery worker, API service).
  • ⚠️Python 3.11 or higher is required.
  • ⚠️Celery and Redis are core dependencies for asynchronous task processing.
Verified SafeView Analysis
The project uses Pydantic for input validation, which helps prevent common injection vulnerabilities. Critical operations like building images, running Terraform, or triggering Jenkins are currently stubbed, which inherently limits immediate execution risks from untrusted input. The `docs/secrets.md` file outlines a strong security posture for credential handling (e.g., IAM Roles Anywhere, OIDC, Kubernetes secrets, avoiding hardcoded secrets). Potential risks would arise if the stubbed tasks were implemented to directly execute shell commands without rigorous input sanitization, or if the `generate-dockerfile` output were directly built by an insecure system, but the current code doesn't exhibit these direct vulnerabilities. Redis is exposed locally via Docker Compose, which is typical for development but would require securing in a production environment.
Updated: 2025-12-01GitHub→
0
0
Medium Cost
icy-r icon
Sec9

An MCP server enabling AI assistants to manage Bitbucket Cloud repositories, pull requests, pipelines, and other related development tasks.

Setup Requirements

  • ⚠️Requires Bitbucket authentication setup (API Token, OAuth, or Basic) which involves setting specific environment variables like BITBUCKET_API_TOKEN and BITBUCKET_USER_EMAIL.
  • ⚠️Node.js version 20.0.0 or higher is required.
  • ⚠️MCP client (e.g., Cursor IDE, Claude Desktop) configuration is necessary to connect to and utilize the server.
Verified SafeView Analysis
The server primarily relies on environment variables for sensitive configuration (API tokens, usernames, passwords, OAuth credentials), which is a good practice. There are no indications of hardcoded secrets or use of 'eval' or other directly exploitable dynamic code execution patterns. Network requests are handled with retry logic and error parsing. Proper configuration of Bitbucket API tokens with least-privilege permissions is crucial for overall security when deploying this server.
Updated: 2025-12-22GitHub→
0
0
Medium Cost
lordmoocow icon

gemini-image-mcp

by lordmoocow

Sec4

This server provides an MCP (Multi-Component Platform) interface for generating images using Google's Gemini 3 Pro Image model.

Setup Requirements

  • ⚠️Requires Google API Key (GOOGLE_API_KEY environment variable) for Google Gemini API, which is a paid service.
  • ⚠️Requires Python 3.10 or newer.
  • ⚠️Designed to run within an MCP framework, communicating via standard I/O.
Review RequiredView Analysis
The `call_tool` function allows users to specify an arbitrary `file_path` for saving generated images. This path is used directly with `pathlib.Path(file_path)` and `path.write_bytes()`. This presents a significant directory traversal vulnerability, as a malicious user could specify paths like `../../../../tmp/malicious.png` to write files to arbitrary locations on the server's filesystem, potentially overwriting critical files or planting malicious executables, especially if the server runs with elevated permissions. The `path.resolve()` call occurs after the file write and only resolves the path for reporting, not for preventing the write itself. No input sanitization for `file_path` is evident.
Updated: 2025-11-25GitHub→
0
0
Medium Cost
manuelalba1021 icon

weather-mcp-a2a

by manuelalba1021

Sec8

An agentic AI system that fetches and reasons over real-time global weather data using the Model Context Protocol (MCP) and large language models.

Setup Requirements

  • ⚠️Requires GROQ_API_KEY for the LLM inference (paid service).
  • ⚠️Requires Python 3.11 or later.
  • ⚠️Requires active internet connection for real-time data fetching.
  • ⚠️Microphone permissions needed for voice input functionality.
Verified SafeView Analysis
The project adheres to good security practices by loading API keys from environment variables (`.env`). External API calls are made using `httpx` and `requests` to well-known weather APIs (`api.weather.gov`, `open-meteo.com`), which mitigates direct network risks. Input sanitization for city names used in API requests relies on URL parameter encoding by the `requests` library, which is generally robust against injection. No use of `eval` or other dangerous code execution patterns was identified. The most significant concern, noted in the project's internal documentation, is the unreliability of certain LLMs (Groq models) in correctly formatting tool calls, which led to a direct API implementation workaround in the main Streamlit app. This workaround itself is implemented securely, but deviates from the intended agentic workflow.
Updated: 2026-01-19GitHub→
0
0
Low Cost
CaptainMaxine icon
Sec6

This server provides a communication backbone for a multi-agent system, facilitating both external client-to-server and internal agent-to-agent communication via WebSockets and MQTT.

Setup Requirements

  • ⚠️Requires Python 3+ environment and `pip install -r requirements.txt`
  • ⚠️Likely requires a running MQTT broker (e.g., Mosquitto) for agent-to-agent communication, as indicated by `asyncio-mqtt`.
Verified SafeView Analysis
No explicit 'eval', 'exec', or hardcoded secrets were found in the truncated source code. The server binds to '127.0.0.1' (localhost) by default, which is safer for local testing. However, as a network server, inherent risks exist without clear authentication/authorization mechanisms and robust input validation, especially within 'message_handling.py'. The use of MQTT for agent-to-agent communication also implies the need for a secured MQTT broker to prevent unauthorized agent impersonation.
Updated: 2025-11-30GitHub→
0
0
Low Cost
Rodert icon

awesome-mcp

by Rodert

Sec8

This project is an automated generator and host for a curated list of Model Context Protocol (MCP) projects, providing an interactive and searchable directory, rather than being an MCP server itself.

Setup Requirements

  • ⚠️Requires Python 3.11+ and Node.js 18+.
  • ⚠️Requires a GitHub Personal Access Token (`GITHUB_TOKEN`) with `public_repo` permissions configured as an environment variable for project scraping.
  • ⚠️Initial run of the translation script (`translate_content.py`) will download large language packages for offline translation, which can take time and consume bandwidth.
Verified SafeView Analysis
The project functions as an automated 'awesome list' generator for MCP projects and a VitePress static site. Its Python scripts interact with the GitHub API, requiring a `GITHUB_TOKEN` which is correctly managed via an environment variable. No hardcoded secrets or 'eval' statements were found. The `translate_content.py` script uses an offline translation library (`argostranslate`), minimizing external API calls for translation. The main security considerations are external to this repository's codebase and relate to how users configure *other* MCP servers (e.g., `GITHUB_PERSONAL_ACCESS_TOKEN` for `modelcontextprotocol/server-github`) as described in the README's quick-start guides.
Updated: 2026-01-19GitHub→
0
0
Low Cost
ArcadeAI icon

arcade-mcp-ts

by ArcadeAI

Sec8

A framework for building servers that provide tools for AI assistants, enabling them to interact with external services and data.

Setup Requirements

  • ⚠️Node.js 20+ runtime environment required for the TypeScript framework, utilizing ESM (ECMAScript Modules) only.
  • ⚠️Requires external 'arcade-mcp' CLI for generating new Python server projects and for authentication setup (e.g., 'arcade login').
  • ⚠️Specific tools built with the framework will require configuration of secrets (e.g., MY_SECRET_KEY) as environment variables or OAuth tokens, as demonstrated in the Python examples.
  • ⚠️This repository (arcade-mcp-ts) is a server framework/library, not a standalone executable server, meaning there isn't a single 'run' command for the framework itself; rather, developers use it to build their own servers. The provided README.md primarily details how to run a *generated Python server* using the 'arcade-mcp' CLI.
Verified SafeView Analysis
The framework emphasizes secure handling of secrets and OAuth tokens, explicitly stating they are injected into the context at runtime and are not visible to LLMs or MCP clients. This design pattern reduces the risk of sensitive data exposure. No explicit malicious patterns like 'eval' or obfuscation were found in the provided truncated source code. A dedicated SECURITY.md also points to a security research program.
Updated: 2025-12-02GitHub→
0
0
Low Cost
tehw0lf icon

flux-mcp

by tehw0lf

Sec8

A server and CLI tool for generating high-quality images using FLUX models, featuring VRAM management and automatic model unloading.

Setup Requirements

  • ⚠️Requires an NVIDIA GPU with 12GB+ VRAM (16GB+ recommended) and CUDA toolkit installed.
  • ⚠️Requires PyTorch with CUDA support, which can be complex to set up correctly.
  • ⚠️Configuration relies on a `.env` file (e.g., `FLUX_OUTPUT_DIR`) which must be manually copied and edited.
Verified SafeView Analysis
The code appears robust in handling user input, with parameters like steps, guidance, and dimensions being type-cast and range-validated before use in the generation pipeline. File paths for output are derived from configuration or validated CLI arguments, reducing path traversal risks. System calls (`subprocess.run` for opening file managers) use controlled arguments (a `Path` object), mitigating command injection. There are no obvious hardcoded secrets or direct `eval()`/`exec()` calls on user-controlled input. The primary potential vectors for vulnerability would lie in the underlying `diffusers` library or malicious model weights from HuggingFace, which are outside the scope of this code's direct security implementation.
Updated: 2025-12-07GitHub→
0
0
Low Cost
SvenTern icon

mcp_servers

by SvenTern

Sec7

Synchronizes MCP server configurations from a YAML file to the Claude CLI and generates a project-level .mcp.json configuration.

Setup Requirements

  • ⚠️Requires 'claude' CLI to be installed and accessible in PATH.
  • ⚠️Requires a `mcp-servers.yaml` file in the current directory or script directory for configuration.
  • ⚠️Authentication tokens for MCP servers will be read from the `mcp-servers.yaml` file, potentially storing sensitive information in plaintext.
Verified SafeView Analysis
The script itself does not contain 'eval' or malicious patterns. It relies on `subprocess.run` to interact with the 'claude' CLI, which is a controlled external dependency. The primary security consideration is that users might store sensitive authentication tokens (bearer, API keys) in plaintext within the `mcp-servers.yaml` file, which is a common but user-managed risk. The script faithfully processes URLs and tokens provided in the configuration, so the security of the configured MCP servers depends on the trustworthiness of those URLs and the handling of the `mcp-servers.yaml` file itself by the user.
Updated: 2026-01-17GitHub→
0
0
Medium Cost
merdekiti icon
Sec9

Provides an MCP-compliant interface for AI assistants to access Mimi Panda's image processing, AI image generation, and creative content services.

Setup Requirements

  • ⚠️Requires Node.js 18+.
  • ⚠️Requires external access to the Mimi Panda API (either self-hosted or cloud instance).
  • ⚠️Users must manually obtain a Mimi Panda API token from the Mimi Panda application (web or desktop) for authenticated requests.
Verified SafeView Analysis
The project demonstrates a strong focus on security, as evidenced by its PRODUCTION_CHECKLIST, which includes explicit mentions of input validation (Zod schemas), error handling, timeout management, and sensitive header masking. Communication primarily occurs via STDIO, a secure model for local client-server interaction that reduces external network attack surfaces compared to a publicly exposed web server. No 'eval' or malicious obfuscation was found in the provided code snippets, and sensitive information like API tokens is managed through environment variables or per-request parameters rather than hardcoding.
Updated: 2025-11-24GitHub→
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