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Vetted Servers(9120)

0
0
Medium Cost
eh24905-wiz icon

jira-mcp

by eh24905-wiz

Sec9

Enables AI assistants to interact with Jira for issue management, progress tracking, and team activity monitoring through a standardized Model Context Protocol interface.

Setup Requirements

  • ⚠️Requires a Jira Cloud Account with API access enabled.
  • ⚠️Requires a Jira API Token to be generated and configured.
  • ⚠️Requires setting critical environment variables (JIRA_BASE_URL, JIRA_USER_EMAIL, JIRA_API_TOKEN) for authentication and access.
Verified SafeView Analysis
The server securely handles sensitive Jira API credentials by requiring them to be set as environment variables, preventing hardcoding. It uses Zod for input schema validation and implements specific type handling and conversion (e.g., for Jira's Atlassian Document Format) when updating fields, which helps prevent injection attacks. There are no indications of 'eval' or direct arbitrary command execution. While JQL injection is a theoretical risk for Jira's API itself (e.g., crafting queries to retrieve excessive data), the server mitigates this with `maxResults` limits and doesn't translate it into server-side code execution.
Updated: 2025-12-15GitHub→
0
0
High Cost
b9b4ymiN icon

myMCPserver

by b9b4ymiN

Sec3

Provides professional-grade financial analysis tools for stock valuation, investment decision-making, and portfolio management, integrating real-time data from Thai stock markets.

Setup Requirements

  • ⚠️Requires Node.js 18+ runtime environment.
  • ⚠️Relies on an external SET Watch API (`SET_WATCH_API_HOST`) for Thai stock data, which may require specific authentication or have usage limits (default host is provided, but reliability/access unclear).
  • ⚠️The exposed file system tools (`delete_file`, `write_file`) necessitate robust sandboxing or highly constrained AI agent permissions to prevent accidental or malicious system-level damage.
Review RequiredView Analysis
The server exposes powerful file system manipulation tools (read, write, delete, list, search files) through `src/tools/fileSystemTools.ts`. Specifically, `delete_file` with recursive capability poses a critical risk for data loss or system compromise if the server is exposed without stringent sandboxing or if an unconstrained AI agent misuses these tools. `path.resolve` allows operations relative to the server's working directory. While API keys are handled via environment variables, the direct file system access makes it highly vulnerable if not deployed in a strictly isolated and controlled environment.
Updated: 2026-01-17GitHub→
0
0
Medium Cost
quintindk icon
Sec7

Provides an MCP server and CLI tool to download images and draw.io diagrams from Confluence pages for AI assistants or standalone use.

Setup Requirements

  • ⚠️Requires a Confluence instance URL and a Personal Access Token with read permissions.
  • ⚠️Python 3 and `pip` dependencies are required for local installation.
  • ⚠️Docker is recommended for easy deployment, requiring volume mounts for downloaded files to persist on the host.
Verified SafeView Analysis
The tool can download files to arbitrary absolute paths specified by the user or an AI agent, potentially allowing writes to sensitive system locations if the process has broad filesystem permissions. While the `resolve_output_path` function attempts to contain relative paths to a mounted `/output` directory within Docker, it explicitly returns absolute paths unmodified. `CONFLUENCE_PERSONAL_TOKEN` is a critical secret, correctly handled via environment variables but its security depends on the runtime environment. No 'eval' or obfuscation detected.
Updated: 2025-11-27GitHub→
0
0
Low Cost
Sec5

A server-side application for tracking and managing personal expenses remotely, likely supporting multiple clients or a centralized access point.

Setup Requirements

  • ⚠️Specific Python version may be required (indicated by .python-version file).
  • ⚠️Requires `uv` or equivalent for dependency management (indicated by uv.lock and pyproject.toml).
Review RequiredView Analysis
Cannot analyze code for 'eval', obfuscation, or specific network risks as only file names were provided. Server applications inherently carry network exposure risks, and without reviewing the implementation in `main.py`, security cannot be guaranteed.
Updated: 2025-11-17GitHub→
0
0
Medium Cost

Bridge-MCP

by ali-moghadam

Sec5

This project provides a bridge or interface for integrating the Claude AI model into a desktop application or a Master Control Program (MCP) like system.

Setup Requirements

  • ⚠️Requires Python 3.x and installation of dependencies listed in `requirements.txt`.
  • ⚠️Requires an API key or credentials for the Claude AI service, configured via `.env` or `claude_desktop_config.json`.
  • ⚠️The `setup.sh` script may have system-level dependencies or require specific permissions to execute successfully.
Review RequiredView Analysis
Without access to the source code, it is impossible to verify the presence of 'eval' statements, code obfuscation, or specific network vulnerabilities. However, being an 'MCP Server' implies network communication, which always carries inherent risks. The `setup.sh` script could execute arbitrary commands, and `requirements.txt` might introduce transitive dependencies with vulnerabilities. The `claude_desktop_config.json` could expose sensitive configuration if not handled securely.
Updated: 2025-11-18GitHub→
0
0
Medium Cost
RycnCDL icon
Sec8

Provides a Model Context Protocol (MCP) server for Microsoft Sentinel to enable natural language SOC operations and multi-tenant security management through Python and PowerShell tools.

Setup Requirements

  • ⚠️Requires Python 3.10+ and PowerShell 7+ installed locally.
  • ⚠️Requires an Azure Service Principal with specific RBAC roles (e.g., Microsoft Sentinel Reader, Log Analytics Reader) configured in environment variables.
  • ⚠️Requires the SentinelManager.ps1 PowerShell script to be manually downloaded and its path configured via an environment variable (`SENTINEL_MANAGER_SCRIPT`).
  • ⚠️Claude Desktop integration requires manual editing of a JSON configuration file with custom paths and environment variables, which can be error-prone (e.g., Windows path escaping).
Verified SafeView Analysis
The server uses a PowerShell Bridge to execute local and remote scripts, which is a powerful capability. While the implementation includes whitelisting of PowerShell functions and parameter sanitization to mitigate command injection risks, the underlying `subprocess.run` and `pypsrp` for remote execution carry inherent risks if the environment is not properly secured. Remote PowerShell execution with `pypsrp` by default disables certificate validation (`cert_validation=False`), which is noted as a development/test setting and should be set to `True` in production. Sensitive credentials are managed via environment variables, which is a good practice, and explicit warnings against hardcoding them are present.
Updated: 2025-11-29GitHub→
0
0
Medium Cost
DCode-v05 icon

MCP-Server-Client

by DCode-v05

Sec5

An advanced AI assistant enabling natural language interaction (reading, analyzing, modifying) with local Excel spreadsheets via Google Gemini and the Model Context Protocol (MCP).

Setup Requirements

  • ⚠️Requires Google Gemini API Key (paid, usage-based).
  • ⚠️Requires Node.js and npm for frontend setup.
  • ⚠️Excel files must be manually placed in the `excel_data/` directory for the LLM to access them.
Review RequiredView Analysis
The FastAPI backend configures CORS with `allow_origins=['*']`, which is a critical security vulnerability for any deployment exposed to the internet, allowing arbitrary domains to make cross-origin requests. Although the LLM's file operations are sandboxed to the `excel_data/` directory (preventing directory traversal), this broad CORS policy could enable an unauthenticated attacker to make requests that control the LLM's tool calls, potentially leading to unauthorized modification or deletion of Excel data within the allowed directory. No explicit `eval()` or code obfuscation found.
Updated: 2026-01-03GitHub→
0
0
Medium Cost
littlebearapps icon

mcp-audit

by littlebearapps

Sec9

Tracking and analysis of MCP tool usage and token costs across multiple AI coding platforms (Claude Code, Codex CLI, Gemini CLI), with a programmatic MCP server for real-time metrics, optimization recommendations, and configuration analysis.

Setup Requirements

  • ⚠️Requires `pip install token-audit[server]` for MCP server functionality.
  • ⚠️Gemma tokenizer download requires `pip install huggingface_hub`, and may involve HuggingFace token/license acceptance or GitHub API rate limits.
  • ⚠️Python < 3.11 may require `pip install toml` for TOML configuration file support.
Verified SafeView Analysis
The project demonstrates strong security awareness. It includes explicit path validation (`validate_config_path`) to restrict file access to known, safe directories, preventing path traversal. Credential detection (`credential_detector.py`) is implemented, and sensitive values are redacted in output. External calls to `git` are for metadata collection, captured, and not executed, limiting risk. Tarball extraction for the Gemma tokenizer includes `_validate_tarball_member` to prevent path traversal attacks. The MCP server interfaces sanitize inputs and outputs. The main risks are contained by robust internal controls.
Updated: 2025-12-30GitHub→
0
0
Low Cost
pranavkrishna-sn icon

TESTING-FS

by pranavkrishna-sn

Sec8

A boilerplate or framework for developing modular web applications with distinct backend API and frontend UI components.

Setup Requirements

  • ⚠️Requires a Python environment with FastAPI, SQLAlchemy, and Pydantic for the backend.
  • ⚠️Requires a Node.js/npm/yarn environment for the React frontend.
  • ⚠️Requires a database (e.g., SQLite, PostgreSQL) for SQLAlchemy models to persist data.
Verified SafeView Analysis
The provided code snippets are mostly boilerplate and do not contain obvious 'eval' statements, hardcoded secrets, or malicious patterns. The frontend uses a placeholder `baseURL: 'https://api.example.com'` which would need secure configuration in a production environment. SQL models use SQLAlchemy, which generally prevents SQL injection when used correctly.
Updated: 2026-01-13GitHub→
0
0
Low Cost
pulkitdabur icon

MCP-Server

by pulkitdabur

Sec6

A server for exposing local tool APIs via the Model Context Protocol (MCP) to be consumed by AI/ML clients or agents.

Setup Requirements

  • ⚠️Requires Python 3.12+
  • ⚠️For the 'MCP-demo', the 'add_tool.py' (Tool API) must be run in a separate process before starting 'mcp_server.py'.
  • ⚠️The server binds to '0.0.0.0', requiring network awareness.
Verified SafeView Analysis
The 'Templatized-MCP-Server' dynamically loads Python functions from modules defined in a YAML configuration file. While the provided YAML refers to internal project functions, this dynamic loading pattern (`import_module`, `getattr`) can introduce a significant security risk if the configuration file or module paths could be influenced by untrusted input, potentially leading to arbitrary code execution. The 'MCP-demo' implementation is less prone to this specific risk as the tool is explicitly defined. Both servers bind to '0.0.0.0', making them accessible from all network interfaces, which should be secured in a production environment.
Updated: 2025-12-06GitHub→
0
0
Medium Cost
aranej icon
Sec8

An MCP server enabling AI language models to interact with YouTube content, providing tools for video information retrieval, transcript management, channel data, playlist management, and basic content manipulation.

Setup Requirements

  • ⚠️Requires `YOUTUBE_API_KEY` environment variable for most functionality.
  • ⚠️Requires `YOUTUBE_OAUTH_CLIENT_ID` and `YOUTUBE_OAUTH_CLIENT_SECRET` environment variables for write operations (e.g., creating/modifying playlists).
  • ⚠️Full YouTube API setup (Google Cloud project, YouTube Data API v3 enabled, API key, and OAuth credentials) is necessary.
Verified SafeView Analysis
The server uses environment variables for YouTube API keys and OAuth client secrets, and stores OAuth tokens in a local file (`youtube-mcp-token.json` in user's home directory), which are standard practices. A local HTTP server is started on `localhost:8888` for the OAuth callback flow; this is generally safe for local use. No code obfuscation, 'eval' usage, or obvious malicious patterns were found. Security relies on proper management of API keys/secrets and host system file permissions for stored tokens.
Updated: 2025-12-25GitHub→
0
0
High Cost
Vibe-Code-Agent icon

github-review

by Vibe-Code-Agent

Sec7

An MCP server for comprehensive GitHub Pull Request review, code analysis, and security issue detection.

Setup Requirements

  • ⚠️Requires Node.js 18.0.0 or higher.
  • ⚠️A GitHub Personal Access Token (GITHUB_TOKEN) with repository access is required, but this token is unlikely to work for GitHub Copilot API calls, causing AI analysis features to fail as implemented.
  • ⚠️The AI analysis relies on GitHub Copilot API, which may incur costs and requires proper authentication not directly provided by a standard GITHUB_TOKEN.
Verified SafeView Analysis
The server correctly uses environment variables for the GITHUB_TOKEN, which is good practice. No 'eval' or direct code execution vulnerabilities from user input were found. However, there is a significant functional flaw in `src/services/CodeAnalyzer.ts`: it attempts to authenticate with `https://api.githubcopilot.com` using the `GITHUB_TOKEN` which is intended for the standard GitHub API. GitHub Personal Access Tokens (PATs) are generally not valid for GitHub Copilot's API, meaning the core AI-powered analysis features will likely fail unless a different, compatible authentication method is manually configured or the code is modified. This is not a direct security vulnerability of the server's code, but a critical functional bug regarding authentication for a key feature. Additionally, if the AI generates malicious or incorrect recommendations, applying them to the codebase could introduce risks, though the server itself only generates comments and does not automatically apply fixes.
Updated: 2025-12-08GitHub→
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