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Vetted Servers(8554)

Medium Cost

mcp-gcalendar

by moodjx

Sec9

Provides Google Calendar integration for AI assistants to manage events, check availability, and search calendars.

Setup Requirements

⚠️Python 3.13+ required.
⚠️Requires `uv` package manager for installation and running.
⚠️Requires a Google Cloud Project with Calendar API enabled and OAuth2 credentials configured.

Verified SafeView Analysis

The server implements strong security practices by using per-request OAuth2 tokens, meaning user credentials are not stored server-side. It explicitly disables automatic token refresh from the Google API client library to ensure the client is responsible for valid access tokens. Server-side OAuth client credentials are loaded securely from environment variables. Input validation is performed using Pydantic schemas and custom validation functions (e.g., for timezones and emails). There is no use of `eval` or obvious obfuscation. Comprehensive error handling is in place, providing informative but non-revealing messages for authentication failures.

Updated: 2025-12-10GitHub→

Medium Cost

mcp-toolsets

by Maximophone

Sec3

A modular Model Context Protocol (MCP) server that provides AI tools for filesystem operations, email, messaging, and productivity platforms (Notion, LinkedIn).

Setup Requirements

⚠️Requires manual configuration of the '.env' file for API keys/tokens for each enabled toolset (Gmail, Discord, Notion, LinkedIn), as the default 'MCP_API_KEY' is not secure for production.
⚠️Gmail integration setup involves creating a Google Cloud project, enabling the Gmail API, creating OAuth 2.0 Desktop app credentials, and downloading 'credentials.json' for the initial browser-based OAuth flow.
⚠️Discord integration requires creating a Discord bot, enabling 'Message Content Intent' and 'Server Members Intent' in the Discord Developer Portal, copying the bot token, and inviting the bot to servers with specific permissions.
⚠️LinkedIn integration uses an unofficial API which is prone to account restrictions. Authentication can be challenging, recommending browser cookie extraction (Brave, Chrome, Firefox, Edge) or manual 'li_at' and 'JSESSIONID' configuration over username/password due to 2FA/CAPTCHA risks.
⚠️Python dependencies must be installed via `pip install -r requirements.txt`.

Review RequiredView Analysis

The 'system' toolset includes highly dangerous capabilities like 'run_command' (executes arbitrary shell commands), 'execute_python' (executes arbitrary Python code), 'save_file' (writes to arbitrary paths within BASE_DIR), and 'persistent_shell' (maintains an interactive shell session). While these tools are explicitly marked 'safe=False' and require API key authentication, a compromised API key or misuse by an AI agent could lead to severe system compromise, data loss, or unauthorized access. File operations are sandboxed to 'MCP_BASE_DIR', which mitigates some direct filesystem risks but does not eliminate the danger of code execution. The use of pickle for Gmail tokens is generally safe as it's for self-generated, controlled data.

Updated: 2025-12-15GitHub→

Medium Cost

jobsearch-mcp-server

by hanjianchun

Sec1

This server provides AI-powered job search assistance by fetching job listings and matching them against a user's resume.

Setup Requirements

⚠️Requires manual removal and secure configuration of hardcoded API key and session cookies (CRITICAL security fix).
⚠️Requires ChromeDriver executable to be manually downloaded and its path configured (currently hardcoded for Windows).
⚠️Requires a valid DeepSeek/OpenAI API key for LLM services.

Review RequiredView Analysis

CRITICAL: The `llm.py` file contains a hardcoded OpenAI/DeepSeek API key. The `listjob.py` file contains a hardcoded user session cookie string for `zhipin.com` and a hardcoded proxy server address. These hardcoded credentials represent severe security vulnerabilities, exposing sensitive access tokens and potential user session data. The `chromedriver_path` is also hardcoded to a Windows-specific location, which is a misconfiguration risk.

Updated: 2025-12-11GitHub→

Medium Cost

github-trendings-mcp

by mainhanu

Sec8

An MCP server enabling AI assistants to fetch, track, and analyze trending GitHub repositories with language filtering and structured insights for technology trend analysis.

Setup Requirements

⚠️Requires global NPM installation (`npm install -g github-trending-mcp`).
⚠️Designed to integrate with AI assistants supporting Model Context Protocol (MCP), such as VS Code Copilot.
⚠️If behind a network proxy, requires `HTTPS_PROXY`/`HTTP_PROXY` environment variables or `--proxy` argument to be configured.

Verified SafeView Analysis

The server uses web scraping (`cheerio`, `undici`) to fetch data from GitHub Trending, which relies on GitHub's page structure remaining consistent. It reads proxy settings from standard environment variables (`HTTPS_PROXY`, `HTTP_PROXY`) or a command line argument (`--proxy`), which is common practice. File system operations are confined to a user-specific cache directory (`~/.github-trending-mcp/cache.json`) and do not pose a system-wide risk. No 'eval', hardcoded secrets, or obvious malicious patterns were found. The tool's reliance on scraping could make it brittle if GitHub's page structure changes.

Updated: 2026-01-19GitHub→

Low Cost

MyTaskly-mcp

by zaka265-star

Sec8

Provides a Model Context Protocol (MCP) server for task and note management, optimized for mobile UIs and voice interaction.

Setup Requirements

⚠️Requires a separate MyTaskly FastAPI backend server to be running and accessible.
⚠️The `JWT_SECRET_KEY` environment variable on the MCP server must EXACTLY match the `SECRET_KEY` used by the FastAPI backend server.
⚠️Requires Python 3.10 or higher.

Verified SafeView Analysis

The server implements a robust dual-JWT authentication mechanism, validating incoming MCP tokens against a specified audience and then generating new JWTs for the FastAPI backend using a shared secret key. This shared `JWT_SECRET_KEY` is a critical security dependency, as explicitly highlighted in the documentation; it must be securely managed and consistent across both services. The `X-API-Key` header provides an additional layer of authentication for the MCP server's communication with the FastAPI backend. Debug logging of JWT token details in `src/auth.py` is present, which should be disabled or carefully managed in production to prevent exposure of sensitive information. An older/deprecated file, `src/client.py`, contains a hardcoded `secret_key` for JWT generation; while the primary entry point (`main.py`) appears to use the refactored, secure client structure, the presence of this file is a potential vulnerability if it were to be inadvertently used.

Updated: 2026-01-19GitHub→

Low Cost

notes

by cardmagic

Sec4

Search, browse, create, and delete Apple Notes with fuzzy matching, designed for integration with Claude Code.

Setup Requirements

⚠️Requires macOS to function (reads Apple Notes database).
⚠️Requires manual 'Full Disk Access' for the terminal/IDE in System Settings to read Apple Notes data.
⚠️Optional: Install 'poppler' (`brew install poppler`) for PDF text extraction features.

Review RequiredView Analysis

The server uses `execSync` for AppleScript automation (create/delete notes) and PDF text extraction (`pdftotext`). While the `applescript.ts` file includes an `escapeAppleScript` function, direct execution of external commands with user-controlled input (even if sanitized) via `execSync` is inherently risky and could lead to arbitrary command injection if the escaping mechanism is flawed or bypassed. This is a critical security concern.

Updated: 2026-01-18GitHub→

High Cost

Quizard-MCP-Server

by Quizzardd

Sec8

This server functions as a Classroom Quiz Generator, leveraging AI to create, revise, and publish quizzes for educators by extracting content from learning materials and integrating with a backend classroom service.

Setup Requirements

⚠️Requires Google Cloud Project setup and service account credentials with Storage Object Viewer and OIDC token generation permissions.
⚠️A separate backend classroom service must be running and accessible at the specified BACKEND_BASE_URL.
⚠️Python dependencies (requests, google-cloud-storage, PyPDF2, python-dotenv, fastmcp, google-auth-oauthlib, google-auth) must be installed.

Verified SafeView Analysis

The server uses Google Cloud Storage for content retrieval and Google OIDC tokens for backend authentication, which are generally secure practices. It loads environment variables for sensitive paths. The `read_content_file_from_URL` function handles various URL schemes, including direct HTTP requests. While the intended use appears to be limited to Google Cloud Storage URLs by the tool descriptions, if the `file_url` input to this function were to be manipulated by an external, untrusted source, it could potentially lead to SSRF vulnerabilities, although the agent instructions for its use typically prevent this. No 'eval' or explicit shell execution is observed.

Updated: 2025-11-23GitHub→

Medium Cost

codemoder

by diodeinc

Sec5

Acts as an MCP proxy to enable AI models to write and execute JavaScript code, allowing them to chain multiple tool calls in a single execution.

Setup Requirements

⚠️Requires a downstream MCP server to proxy.
⚠️Requires Rust toolchain to build.
⚠️Interaction requires understanding of the MCP protocol for client integration.

Verified SafeView Analysis

The core functionality of codemoder is to execute arbitrary JavaScript code provided as input to its `execute_tools` tool. This is achieved using the `rquickjs` (QuickJS) runtime and its `eval` equivalent. While `rquickjs` generally provides a sandboxed environment, the JavaScript code has direct access to call any of the downstream MCP server's tools via the `tools` object. This design inherently poses a significant security risk: if a malicious actor or a compromised AI model provides malicious JavaScript code, it could execute arbitrary commands or logic through the downstream tools on the host system where codemoder is running. It is critical to only feed code from trusted sources.

Updated: 2026-01-18GitHub→

Medium Cost

COMP4952_MCPWebAppServer

by BetterLordWilliam

Sec9

A multi-component .NET web application integrating an AI model (gpt-4o-mini) for content processing, featuring distinct API and MVC client components, likely for a 'beverage' system.

Setup Requirements

⚠️Requires Azure AI access/authentication (e.g., API Key, Azure AD) for the specified AI endpoint (https://models.inference.ai.azure.com).
⚠️This is a multi-project .NET solution, requiring individual compilation/running of components or a solution-level build/run command.
⚠️The MCPWebControllerApi uses a local SQLite database ('beverages.sqlite') which may require initial setup or migration.

Verified SafeView Analysis

No critical security risks like 'eval', obfuscation, or hardcoded sensitive secrets were found. The 'AllowedHosts: *' setting is common in development but should be restricted for production deployments.

Updated: 2025-11-28GitHub→

High Cost

remembrances

by josegarridodigio

Sec4

Provides long-term memory, knowledge base, and code indexing capabilities to AI agents using multiple memory layers and embedding models.

Setup Requirements

⚠️Requires Go 1.20+ installed.
⚠️Requires SurrealDB, either local or remote. An auto-start command feature is available but requires the 'surreal' executable or a running instance.
⚠️Local embedding models (GGUF, Ollama) require significant local resources (CPU/GPU, RAM) and specific setup (e.cpp compilation, GPU drivers, Docker for Ollama). GGUF models need to be manually downloaded.
⚠️Using the `start-remembrances.sh` wrapper script requires Docker to manage Ollama.

Review RequiredView Analysis

The `surrealdb-start-cmd` configuration option allows execution of arbitrary shell commands (`/bin/sh -c "<cmd>"`) if the server fails to connect to SurrealDB. This presents a critical security risk if the configuration can be manipulated by an untrusted entity, as it effectively provides an 'eval'-like capability. Additionally, default SurrealDB credentials of 'root:root' are provided in the sample configuration, which are insecure if not changed. Network-exposed HTTP and SSE APIs lack explicit mention of authentication/authorization mechanisms.

Updated: 2026-01-14GitHub→

Low Cost

planka-mcp

by AcceleratedIndustries

Sec9

Integrates AI clients with Planka kanban boards for task management and automation via a Model Context Protocol (MCP) server.

Setup Requirements

⚠️Requires a running Planka instance to connect to.
⚠️Requires either `PLANKA_TOKEN` or `PLANKA_EMAIL` and `PLANKA_PASSWORD` environment variables for authentication.
⚠️Requires Rust toolchain (`cargo`) to build and run from source.

Verified SafeView Analysis

The server uses environment variables for sensitive data (Planka URL, authentication token/credentials), preventing hardcoding. It explicitly excludes destructive operations (`delete_card`, `delete_list`) from programmatic tool calling, requiring explicit user initiation for safety. Inputs are deserialized and validated for structure/presence of required arguments. The server is designed for local-only execution over stdin/stdout, reducing network exposure.

Updated: 2025-12-03GitHub→

Low Cost

mcp-porkbun-domain-availability

by drdanielbender

Sec9

Provides tools to check domain availability and pricing information via the Porkbun API v3.

Setup Requirements

⚠️Requires Node.js v18 or later.
⚠️Requires Porkbun API credentials (API Key and Secret API Key) which must be generated from the Porkbun account.
⚠️Porkbun API credentials must be configured as environment variables (PORKBUN_API_KEY, PORKBUN_SECRET_API_KEY) or in a .env file.

Verified SafeView Analysis

The server correctly uses environment variables for API keys and secrets, preventing hardcoding. It uses Zod for input schema validation, which helps prevent malformed inputs. Domain names are directly interpolated into the API endpoint URL; while standard for domain lookups, it's a general point to consider for URL injection, though domain name formats are restrictive. No 'eval' or other obvious malicious patterns were found. Network requests are made to a specific, hardcoded Porkbun API endpoint. The implementation includes built-in rate limiting and comprehensive error handling.

Updated: 2025-11-27GitHub→

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