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Vetted Servers(9120)

0
0
Medium Cost

mcp-sefaria-server

by CharmingSteve

Sec8

Provides an MCP interface for Large Language Models to retrieve, search, and reference Jewish texts and learning schedules from the Sefaria library.

Setup Requirements

  • ⚠️Requires Python 3.10 or higher.
  • ⚠️The 'uv' CLI tool is required to run the server as per the provided instructions.
  • ⚠️Requires active internet connection to access the Sefaria API.
Verified SafeView Analysis
The server makes outbound API calls to the Sefaria API (sefaria.org) to retrieve text and data. This is an expected and core function. No 'eval' or obvious obfuscation detected. Assumes the Sefaria API itself is secure and trustworthy.
Updated: 2025-11-17GitHub→
0
0
Low Cost
amritpattnaik icon

mcpstreamabledeploy

by amritpattnaik

Sec10

Exposes a simple Python function as an HTTP-streamable API endpoint using the MCP framework.

Setup Requirements

  • ⚠️Python 3.11+ required
  • ⚠️Requires the 'mcp' Python package (mcp[cli]>=1.22.0)
Verified SafeView Analysis
The provided source code is minimal and does not contain any obvious security vulnerabilities such as 'eval', hardcoded secrets, or malicious patterns. The 'greeting' tool is safe.
Updated: 2025-12-01GitHub→
0
0
High Cost
srimanikantaBattu icon

livekit-backend

by srimanikantaBattu

Sec8

A starter project for building voice AI assistants using LiveKit Agents for Node.js and LiveKit Cloud.

Setup Requirements

  • ⚠️LiveKit Cloud Account Required: Requires a LiveKit Cloud account and API keys (LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET) to function.
  • ⚠️Initial Model Download: An explicit 'pnpm run download-files' command is needed before the first run to download local models like Silero VAD.
  • ⚠️Specific Node.js and Package Manager Versions: Requires Node.js version '>=22.0.0' and uses 'pnpm' as the package manager.
  • ⚠️Usage-based AI Models: Involves costs for external AI services (OpenAI, AssemblyAI, Cartesia) through LiveKit Cloud, charged per usage.
Verified SafeView Analysis
The server acts as a voice AI agent, relying on the LiveKit Agents SDK and LiveKit Cloud for core functionality, including STT, LLM, and TTS inference. Secrets (LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET) are correctly managed via environment variables loaded from .env.local. No direct eval or unsafe file system operations with arbitrary user input were found in the provided src/agent.ts. The primary security considerations involve the trustworthiness of the LiveKit platform and its integrations with third-party AI models (OpenAI, AssemblyAI, Cartesia).
Updated: 2025-11-27GitHub→
0
0
High Cost
Sec9

Provides a secure interface for AI assistants to access Roman Catholic Liturgical Calendar data, enabling reasoning over liturgical dates, seasons, feasts, and saints.

Setup Requirements

  • ⚠️Requires Docker Desktop with the MCP Toolkit enabled.
  • ⚠️Manual editing of Docker MCP catalog and registry files is required, and these changes may be overwritten on Docker Desktop restarts.
  • ⚠️Preferred Python version is 3.12 (implied by local testing instructions and .python_version file).
Verified SafeView Analysis
The server is read-only, handles no sensitive information, and is designed to run as a non-root user in a container. It interacts with a publicly accessible, documented API over HTTPS. No signs of code obfuscation or direct 'eval' risks are apparent.
Updated: 2025-11-17GitHub→
0
0
Low Cost
muhammadhayat123 icon

MCP_client_server

by muhammadhayat123

Sec9

This project demonstrates client-server delegation of LLM tasks using the MCP framework, where the server requests an LLM generation from the client.

Setup Requirements

  • ⚠️Python 3.12+ required
  • ⚠️MCP library must be installed (pip install mcp)
  • ⚠️Uvicorn server must be installed to run the ASGI application (pip install uvicorn)
Verified SafeView Analysis
The provided code snippet does not contain 'eval', 'exec', or hardcoded secrets. Input 'topic' is embedded as text content and not directly executed. The server runs on localhost, and the security relies heavily on the underlying 'mcp' library and proper configuration in a production environment. For a demo, it appears safe.
Updated: 2025-11-19GitHub→
0
0
Low Cost
Sec10

This server provides tools to search and retrieve Swedish library statistics from the National Library of Sweden's open data API.

Setup Requirements

  • ⚠️Requires Node.js environment.
Verified SafeView Analysis
The code does not use 'eval' or similar dangerous functions. It relies on the well-established '@modelcontextprotocol/sdk' and standard Node.js/fetch APIs. No hardcoded secrets are present. Network requests are made to a public API endpoint. The code appears safe to run.
Updated: 2025-11-21GitHub→
0
0
High Cost
maqsam22 icon

claude-code

by maqsam22

Sec9

A comprehensive developer tool that integrates AI agents for structured feature development, git workflow automation, code quality review, and Agent SDK application development and verification.

Setup Requirements

  • ⚠️Requires an Anthropic API Key (Paid) for AI model access.
  • ⚠️Requires GitHub CLI (`gh`) installed and authenticated, along with a `GITHUB_TOKEN` environment variable for full Git workflow automation and issue management scripts.
  • ⚠️The project's scripts are written in TypeScript and use Bun (or Node.js with ts-node) as an execution environment.
Verified SafeView Analysis
The project includes explicit security features like a `security_reminder_hook.py` that warns about common vulnerabilities (e.g., `eval`, `child_process.exec`, XSS patterns) when code is modified. Several AI agents (`code-reviewer`, `silent-failure-hunter`, `agent-sdk-verifier-py/ts`) are also tasked with identifying security issues and bad practices like hardcoded API keys. While the nature of a coding agent means it could be instructed to execute arbitrary commands, the project demonstrates a strong focus on internal security and user guidance to prevent vulnerabilities. The GitHub interaction scripts use environment variables for sensitive tokens.
Updated: 2026-01-19GitHub→
0
0
Low Cost
Sec8

Demonstrates one-to-many JSON object relationships using Gilhari ORM with a RESTful microservice.

Setup Requirements

  • ⚠️Requires Docker installed to build and run the microservice.
  • ⚠️Requires Java Development Kit (JDK 1.8+) for compilation if source code modifications are made.
  • ⚠️Gilhari SDK is needed for development and customization, with JX_HOME environment variable set.
Verified SafeView Analysis
The project is a standard Dockerized RESTful microservice. It opens port 80/8081 for API access, which is inherent to its function. By default, it uses an embedded SQLite database. No explicit code-level security vulnerabilities or malicious practices like obfuscation or 'eval' are evident from the provided information. Running any external Docker image carries inherent risks, but this appears to be a legitimate example from a software vendor.
Updated: 2025-11-18GitHub→
0
0
High Cost
marc-shade icon

synthetic-data-mcp

by marc-shade

Sec9

Generates privacy-compliant synthetic data for training and testing, specifically for regulated industries like healthcare and finance. It supports multiple LLM providers, advanced privacy controls, and various compliance frameworks (HIPAA, GDPR, PCI DSS, SOX).

Setup Requirements

  • ⚠️Requires Python 3.10+.
  • ⚠️For local LLM inference (Ollama), Ollama must be installed and running locally, with the desired models pulled.
  • ⚠️For cloud LLM inference, API keys for providers like OpenAI, Anthropic, Google, or OpenRouter are required.
  • ⚠️The UI requires `npm install` and `npm run dev` in the `ui/` directory if the frontend is to be run locally alongside the backend.
  • ⚠️Requires a backend database for internal operations (e.g., PostgreSQL in production, SQLite for local dev).
Verified SafeView Analysis
The system demonstrates a strong focus on security and privacy through robust architectural components like `PrivacyEngine`, `ComplianceValidator`, `AuthService` (using `CryptContext` for hashing, JWT), `DataEncryptionKeyManager` (supporting multiple KMS), and `AuditTrail`. It explicitly guarantees no retention/output of original PII. Environment variables are correctly used for API keys. The presence of demo credentials in `LoginPage.tsx` is noted for development/testing, but is clearly not for production use. No 'eval' or direct malicious patterns were detected. Deployment manifests for Kubernetes and Helm indicate a production-ready security posture.
Updated: 2025-12-29GitHub→
0
0
Medium Cost
08car1118 icon
Sec8

Provides RAG search capabilities using the Gemini File Search API for AI assistants to manage and query documents.

Setup Requirements

  • ⚠️Requires a Google Gemini API Key, which incurs costs for document indexing ($0.15 / 1M tokens) and for the underlying LLM inference during RAG search (Gemini model costs, not explicitly detailed in README's cost section).
  • ⚠️Requires Python 3.11 or newer.
  • ⚠️File upload (`gemini_upload_file`) expects local file paths accessible by the MCP server, not remote URLs.
Verified SafeView Analysis
The `gemini_upload_file` tool takes a `file_path` parameter, which the server reads from the local filesystem. While the `_validate_file` method performs checks for existence, size, and MIME type, exposing this tool to untrusted users could potentially allow attempts at path traversal or access to unintended local files. However, within the context of an MCP server where the caller is a trusted AI assistant, this is a standard and expected pattern for file processing. No direct `eval` calls or unsanitized shell commands were found. API keys are managed via environment variables.
Updated: 2025-12-02GitHub→
0
0
Low Cost
orpheliedomma-a11y icon

MCP-Server-Project

by orpheliedomma-a11y

Sec9

This server provides a Micro-Capability Platform (MCP) for AI agents to plan travel itineraries, fetch weather forecasts, and recommend hotels for a given destination.

Setup Requirements

  • ⚠️Requires `OPENWEATHER_API_KEY` to be set as an environment variable.
  • ⚠️Designed to run with Claude Desktop or a client that connects via standard I/O (stdio) transport.
Verified SafeView Analysis
Uses environment variables for API keys (OPENWEATHER_API_KEY), reducing the risk of hardcoded secrets. No 'eval' or obvious obfuscation found. External API calls are made to OpenWeatherMap, which is a standard practice and not inherently a risk from the server's code perspective.
Updated: 2025-11-25GitHub→
0
0
Medium Cost
vyogotech icon

frappe_ai

by vyogotech

Sec8

Provides an AI assistant for Frappe/ERPNext users by integrating with an external Model Context Protocol (MCP) server for data-aware queries.

Setup Requirements

  • ⚠️Requires a separate, running MCP Server instance, configured to trust the Frappe instance's OAuth2 provider.
  • ⚠️Manual or script-assisted OAuth2 Client setup is required within Frappe, followed by copying Client ID and Secret into MCP Server Settings.
  • ⚠️The MCP Server's `config.yaml` must be updated to specify Frappe's OAuth2 endpoints and trusted clients.
Verified SafeView Analysis
Uses standard OAuth2 client credentials grant for app-to-app authentication, with the client secret stored as a Frappe 'Password' field (implying encryption). User context is transmitted via Frappe session ('sid' cookie) to the MCP server, requiring the MCP server to be configured to trust and validate Frappe's sessions. Configurable network timeouts are implemented. Token caching is in-memory by default, with a recommendation for Redis in production. No 'eval' or other high-risk dynamic code execution patterns from user input were found. The overall security relies heavily on the correct and secure configuration of both the Frappe instance and the external MCP server.
Updated: 2025-11-27GitHub→
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