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Vetted Servers(9120)

0
0
Low Cost
Song-JunHyeong icon

Appwrite-MCP

by Song-JunHyeong

Sec7

Enable AI agents to fully automate and configure Appwrite backend operations, including databases, users, storage, functions, and messaging.

Setup Requirements

  • ⚠️Requires Appwrite Project ID and API Key with full administrative access.
  • ⚠️Node.js version >=18.0.0 is required.
  • ⚠️Appwrite endpoint (cloud or self-hosted) must be accessible.
Verified SafeView Analysis
The server acts as a wrapper around the Appwrite SDK, exposing comprehensive administrative capabilities. It processes API calls using credentials provided via environment variables or CLI arguments. There are no obvious code injection vulnerabilities (e.g., direct use of `eval` or unsanitized `child_process.exec`) within the MCP server's logic itself. However, due to the nature of 'complete automation' and exposing tools like `graphql_query`, `graphql_mutation`, and `create_deployment` (which accepts base64-encoded function code), a compromised AI agent or malicious input to the agent could lead to extensive data manipulation, resource deletion, or deployment of arbitrary code within the Appwrite project. This is an inherent risk of granting broad administrative access, rather than a flaw in the MCP server's implementation.
Updated: 2025-12-24GitHub→
0
0
High Cost
CR-AudioViz-AI icon

crav-mcp-filesystem

by CR-AudioViz-AI

Sec7

Model Context Protocol (MCP) server for file system operations, enabling AI to autonomously manage and validate files with security sandboxing.

Setup Requirements

  • ⚠️Requires `MCP_API_KEY` environment variable for all API routes except health checks.
  • ⚠️The `STORAGE_PATH` (default: `/tmp/javari-builds`) requires write permissions and should be a persistent volume for production data.
  • ⚠️File creation/update operations are limited by `MAX_FILE_SIZE` (default: 10MB), configurable via environment variable.
Verified SafeView Analysis
The server includes robust path sanitization to prevent directory traversal, file type whitelisting, and size limits. API key authentication and rate limiting are implemented. However, the `execAsync` command for TypeScript validation runs `npx tsc --noEmit` with a user-controlled `cwd` (workspace directory). While `tsc --noEmit` is generally safe, any shell injection vulnerability in `workspaceId` (though mitigated by `path.join`) or unexpected behavior of `tsc` in a malicious context could be a theoretical concern. Additionally, `SUPABASE_ANON_KEY` is hardcoded in some files, though it's typically for client-side use and not a service-level secret.
Updated: 2026-01-09GitHub→
0
0
Medium Cost
vcampitelli icon

workshop-mcp-laravel

by vcampitelli

Sec7

This server provides an e-commerce database layer for simulating various scenarios and experimenting with Eloquent/SQL queries, exposed via public API routes and an MCP (Microservice Communication Protocol) for AI interaction related to orders.

Setup Requirements

  • ⚠️Requires Docker and Docker Compose to run the environment.
  • ⚠️Requires PHP and Composer installed locally if not using Docker.
  • ⚠️Database seeding (`php artisan migrate --seed`) is necessary to populate the database with dummy data for testing the API and MCP functionalities.
  • ⚠️To fully utilize the AI interaction features (MCP), a local LLM server (like Ollama) configured to run on `http://localhost:11434/v1` is required, as suggested by `fastagent.config.yaml`.
Verified SafeView Analysis
The application uses standard Laravel security practices, including form validation (e.g., `OrderValidation`) and Eloquent ORM, which mitigates common web vulnerabilities like SQL injection and mass assignment. The `docker-compose.yaml` file includes a hardcoded weak default password (`password`) for the MariaDB root user, which is a critical security risk if used in a production environment. There are no obvious `eval` or similar dangerous functions used without justification in the provided code. The `.env.example` is assumed to contain placeholders for sensitive credentials that need to be configured.
Updated: 2025-12-14GitHub→
0
0
Medium Cost
haksndot icon

haksnbot-memory

by haksndot

Sec8

Provides persistent memory storage and retrieval for AI agents using SQLite with full-text search and a tagging system.

Setup Requirements

  • ⚠️Requires Node.js 18+ to run.
  • ⚠️Requires specific configuration in Claude Code's settings.json to register as an MCP server, including the full path to its `index.js` file.
Verified SafeView Analysis
The server uses `better-sqlite3` with prepared statements, mitigating SQL injection risks. It communicates via standard I/O (StdioServerTransport), reducing direct network attack surface for the server itself. Database path is configurable via an environment variable, preventing hardcoded paths. No `eval` or obvious malicious patterns found. The primary security considerations would be the integrity of the underlying MCP SDK and Node.js environment, and careful management of the `MEMORY_DB_PATH` if not defaulted.
Updated: 2026-01-18GitHub→
0
0
Low Cost
kirbah icon
Sec9

This is a starter template for developing Model Context Protocol (MCP) servers that enable AI models to interact with custom tools, resources, and prompts via a class-based, type-safe architecture.

Setup Requirements

  • ⚠️Requires Node.js 20.0.0 or higher.
  • ⚠️Requires an MCP-compatible client (e.g., Claude Desktop) for full interactive functionality beyond standalone execution.
Verified SafeView Analysis
The server implements robust input validation using Zod schemas for all tools and prompts, which is a critical security measure against malformed inputs. Centralized error handling and logging (`LoggerService`) are in place. Communication uses `StdioServerTransport`, which operates over standard I/O streams and does not open network ports directly in its default configuration. The logger sanitizes data, preventing accidental exposure in logs. Hardcoded secrets are avoided by using `dotenv/config`. No explicit `eval` or direct arbitrary command execution of user-provided strings is evident in the provided code snippets.
Updated: 2026-01-16GitHub→
0
0
Medium Cost

threat-intel-agent

by pedrofausto

Sec6

This is a frontend application (likely a dashboard or agent UI) designed to interact with and display threat intelligence data.

Setup Requirements

  • ⚠️Requires Node.js and npm/yarn for dependency management and running the frontend development server/build.
  • ⚠️Likely requires a compatible backend API for threat intelligence data; the agent will likely be non-functional without a configured data source.
  • ⚠️Configuration for API endpoints, authentication (e.g., API keys), and specific intelligence sources will be necessary.
Review RequiredView Analysis
Cannot verify code for 'eval' or obfuscation without file content. As a frontend agent, it will inherently make network requests to backend services for threat intelligence. This interaction could pose data privacy or integrity risks depending on the implementation, target endpoints, and data sensitivity. The lack of a README makes it difficult to ascertain the project's security posture, data handling, and specific network interactions.
Updated: 2025-11-18GitHub→
0
0
Low Cost
Maxbleu icon

MCP-F1analisys

by Maxbleu

Sec9

An MCP server for LLM interfaces like Claude to interact with F1Analisys for Formula 1 telemetry data visualization and analysis.

Setup Requirements

  • ⚠️Requires Claude to be installed on your system.
  • ⚠️Requires `PUBLIC_KEY`, `ALGORITHM`, `JWT_TOKEN`, and `F1ANALISYS_PRIVATE_NETWORK_BASE_PATH` environment variables to be set for proper operation.
  • ⚠️Requires Python 3.12 or newer.
Verified SafeView Analysis
The server uses JWT for authentication, retrieving `PUBLIC_KEY` and `ALGORITHM` from environment variables. It makes outbound HTTP requests to an external F1 analysis API, using a `JWT_TOKEN` and `F1ANALISYS_PRIVATE_NETWORK_BASE_PATH` also from environment variables. URL path construction within `path_utils.py` appears robust against path injection, converting parameters to strings or specific path segments. No 'eval' or obvious malicious patterns were found. The primary external risk relies on the security and trustworthiness of the `F1ANALISYS_PRIVATE_NETWORK_BASE_PATH` backend.
Updated: 2025-12-03GitHub→
0
0
Low Cost
wusimpl icon

VisioInWord-MCP

by wusimpl

Sec6

An MCP server enabling large language models to insert Visio UML class diagrams and plain text directly into Microsoft Word documents.

Setup Requirements

  • ⚠️Requires Windows OS, Microsoft Word, and Microsoft Visio to be installed and runnable.
  • ⚠️Word must be open with an active document and cursor placed at the insertion point before use.
  • ⚠️Python 3.10+ is required.
Verified SafeView Analysis
The server uses `pywin32` for COM automation of Microsoft Word and Visio. While this is the core functionality, direct manipulation of desktop applications based on LLM input introduces potential risks if input sanitization is not robustly handled within the Python script. Without the actual `word_visio_mcp.py` source code, a thorough audit for 'eval', 'exec', or uncontrolled system interactions cannot be performed. It runs locally via stdio, limiting network-based attack vectors, but local compromise via crafted LLM input is a theoretical concern.
Updated: 2025-11-28GitHub→
0
0
Low Cost
Indranil2020 icon

crystal_mcp_server

by Indranil2020

Sec8

Provides a backend API for crystal structure generation, analysis, and optimization, integrating Machine Learning Force Fields (MLFFs) and enabling natural language interaction through Large Language Models (LLMs).

Setup Requirements

  • ⚠️Node.js (18+) runtime required for the core MCP server.
  • ⚠️Python 3.x with extensive scientific libraries (pymatgen, pyxtal, ase, spglib, numpy, matplotlib) required for core functionality and MLFFs.
  • ⚠️Ollama server must be running separately (e.g., `ollama serve`) for LLM chat features.
  • ⚠️Specific MLFF Python packages (e.g., 'chgnet', 'm3gnet', 'mace-torch') need to be `pip install`-ed individually if those tools are used.
Verified SafeView Analysis
The architecture utilizes a complex multi-language subprocess model (Node.js invoking Python scripts via a Python FastAPI bridge). Extensive input validation (Zod in TypeScript, Pydantic in Python) is implemented to mitigate common injection risks by strictly controlling tool arguments. Data passed to Python subprocesses uses temporary files, which is a robust practice against command injection. CORS is restricted to localhost, appropriate for a local desktop application. While reliance on external CDNs for frontend libraries (MolStar, Kekule.js) is present and common, it introduces a dependency on their security. Overall, the codebase demonstrates good defensive programming practices, but the inherent complexity of orchestrating multiple processes and languages means minor, though mitigated, risks exist.
Updated: 2026-01-16GitHub→
0
0
Low Cost
railsstudent icon

fake-store-mcp-server

by railsstudent

Sec6

This server simulates a fake store within a Minecraft server, allowing external applications to browse items, purchase them via bot commands, and check the bot's status.

Setup Requirements

  • ⚠️Requires a running Minecraft server for the bot to connect to.
  • ⚠️Requires Node.js runtime and npm/yarn for dependency management.
  • ⚠️Requires configuration of `MC_HOST`, `MC_PORT`, `MC_USERNAME`, and critically, `MC_PASSWORD` as environment variables (e.g., in a `.env` file).
Review RequiredView Analysis
The `POST /buy` endpoint directly uses user-provided `itemId` and `quantity` to construct a Minecraft chat command (`bot.chat`). This pattern could be vulnerable to command injection if the target Minecraft server or its plugins interpret special characters or commands within chat messages. The use of `cors()` without specific origin restrictions also broadens the potential attack surface. Sensitive information like `MC_PASSWORD` is correctly handled via environment variables.
Updated: 2025-12-01GitHub→
0
0
Medium Cost
zero-to-prod icon

datadog-mcp

by zero-to-prod

Sec9

Provides a Multi-Cloud Platform (MCP) server for searching and analyzing Datadog logs, enabling AI agents to interact with Datadog's Logs API v2 with simplified natural language queries and advanced data transformation capabilities.

Setup Requirements

  • ⚠️Requires PHP 8.4+ and PHP extensions 'json' and 'curl'.
  • ⚠️Requires `jq` binary to be installed in the execution environment for filtering capabilities (e.g., `apk add --no-cache jq` in Docker).
  • ⚠️Requires Datadog API Key (DD_API_KEY) and Datadog Application Key (DD_APPLICATION_KEY) for authentication.
Verified SafeView Analysis
The server uses environment variables for sensitive API keys (DD_API_KEY, DD_APPLICATION_KEY), which is good practice. The `applyJqFilter` method, which executes the `jq` binary, correctly sanitizes user-provided filters using `escapeshellarg()` to prevent command injection. Error handling for external API calls and `jq` execution is robust. The code does not appear to contain hardcoded secrets, obfuscation, or obvious malicious patterns. Regex patterns used in query normalization are standard, reducing the risk of ReDoS.
Updated: 2026-01-19GitHub→
0
0
Medium Cost
FreeOnlineUser icon

shell-mcp

by FreeOnlineUser

Sec8

Provides secure local and remote shell access for Claude Desktop, enabling AI to interact with system environments.

Setup Requirements

  • ⚠️Requires Windows 10/11 and .NET 8.0 SDK (for building, pre-built binaries available).
  • ⚠️Requires manual configuration of Claude Desktop's `claude_desktop_config.json`.
  • ⚠️Specifically designed for integration with Claude Desktop, not a standalone general-purpose shell.
Verified SafeView Analysis
The project demonstrates a strong focus on security for its intended purpose of providing AI with shell access. It implements explicit security modes ('safe' with allowlists, 'dangerous' with per-command approval), a hardcoded blocklist for critical commands (e.g., `format`, `rm -rf /`), real-time command visibility, and a user-controlled 'Lift Pen' feature for instant pausing. SSH passwords are held in memory only, and sudo support is opt-in and disabled by default. While giving an AI shell access inherently carries risk, the extensive safeguards and user control mechanisms significantly mitigate these risks, making it safe to run under responsible user oversight.
Updated: 2025-12-22GitHub→
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