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Vetted Servers(9120)

0
0
High Cost
Consist-Systems icon

doxi-mcp-server

by Consist-Systems

Sec8

Manages Doxi Sign document signing flows, templates, and documents for AI clients (ChatGPT, Claude, etc.) via a JSON-RPC 2.0 API.

Setup Requirements

  • ⚠️Requires OpenAI API Key (Paid) for AI-powered features (GPTDataExtraction).
  • ⚠️Requires Doxi Sign API access and credentials to manage document flows.
  • ⚠️Designed for Docker deployment, though local 'dotnet run' is also supported.
Verified SafeView Analysis
The server uses standard .NET Core practices for configuration and deployment (e.g., Docker, user secrets for sensitive data). No 'eval' or direct arbitrary code execution patterns were identified. Authentication relies on basic auth, requiring credentials to be passed with method calls, which should be protected via HTTPS in production. API keys (e.g., for GPT) are expected to be configured externally, preventing hardcoding. A potential risk lies in the 'AddTexts' method where AI interaction based on a 'prompt' could be susceptible to prompt injection against the upstream AI model, though this doesn't directly compromise the server's code execution.
Updated: 2025-12-16GitHub→
0
0
Medium Cost
davetbo-amzn icon
Sec1

A backend server component responsible for managing and providing memory storage and retrieval functionalities for an AI agent system.

Setup Requirements

  • ⚠️Requires a persistent data store (e.g., database, vector store) for agent memory.
  • ⚠️Potential dependency on external AI services for advanced memory processing or embedding generation.
Review RequiredView Analysis
Security audit cannot be performed as source code was not provided. Consequently, all security aspects including potential vulnerabilities, hardcoded secrets, 'eval' usage, obfuscation, network risks, or malicious patterns are unknown. The score reflects this complete lack of visibility.
Updated: 2025-12-02GitHub→
0
0
High Cost
jsamuel1 icon
Sec5

Analyzes OpenTelemetry traces for performance, error diagnosis, and offers LLM-assisted deep analysis.

Setup Requirements

  • ⚠️Requires AWS credentials configured (via environment variables, ~/.aws/credentials, or IAM roles) for X-Ray and CloudWatch functionality.
  • ⚠️Requires Python 3.11 or newer.
  • ⚠️Relies on 'uv' or 'uvx' for installation and execution.
  • ⚠️Sensitive trace data may be sent to an LLM provider during 'deep_analyze' calls.
Review RequiredView Analysis
The `load_trace` tool's `path` parameter directly reads content from the specified file path using `Path(path).read_bytes()`. If an attacker can control this parameter, it could lead to arbitrary local file disclosure. Additionally, the `search_genai_traces` tool's `filter_query` for CloudWatch Logs Insights could allow for query injection, though the impact is likely limited to manipulating log queries rather than code execution. The `deep_analyze` tool sends the full trace data to an LLM provider, which poses a data privacy/confidentiality risk if sensitive information is present in the traces.
Updated: 2026-01-19GitHub→
0
0
Low Cost
NailaOfficial icon

mcp-server

by NailaOfficial

Sec10

This repository provides documentation for installing the Google Gemini Command Line Interface (CLI) to interact with Google's Gemini AI services.

Setup Requirements

  • ⚠️Requires Python 3.8 or higher
  • ⚠️Requires `pip` for package installation
Verified SafeView Analysis
The provided source code consists solely of Markdown documentation files, which do not contain executable code. Therefore, it does not pose direct security risks such as 'eval' usage, obfuscation, network vulnerabilities, or hardcoded secrets. Any potential security concerns would originate from the `google-gemini-cli` package itself, which is installed from PyPI as per the instructions and is external to the provided source.
Updated: 2025-12-05GitHub→
0
0
Low Cost
lehoangnb icon

xiaozhi-mcp

by lehoangnb

Sec2

Provides real-time Vietnamese financial data, news, radio, and music streaming via MCP servers for AI integration.

Setup Requirements

  • ⚠️Requires `MCP_ENDPOINT` environment variable to be set for the WebSocket connection.
  • ⚠️The MP3 proxy service (`mp3-proxy`) is a Node.js application that relies on `ffmpeg` for real-time transcoding of live streams. `ffmpeg` must be available in the execution environment (e.g., within the Docker image).
  • ⚠️The `SECRET_KEY` environment variable for the `mp3-proxy` service is set to a placeholder and should be replaced with a strong, unique secret for production deployment.
Review RequiredView Analysis
The `calculator.py` script uses `eval(python_expression)` directly on user-provided input. This is a critical remote code execution vulnerability, allowing arbitrary Python code to be executed on the server. Additionally, placeholder `SECRET_KEY=your-esp32-secret-key-2024` in `docker-compose.yaml` implies a secret that should be configured securely and not committed as a placeholder.
Updated: 2025-11-26GitHub→
0
0
Medium Cost
yaacov icon
Sec7

Provides an AI assistant with the capability to manage Migration Toolkit for Virtualization (MTV) operations in a Kubernetes cluster, including querying resources, monitoring migrations, and controlling the migration lifecycle.

Setup Requirements

  • ⚠️Requires `kubectl-mtv` CLI installed and available in PATH for local execution of commands.
  • ⚠️Requires access to a Kubernetes cluster with Migration Toolkit for Virtualization (MTV) deployed.
  • ⚠️Requires appropriate RBAC permissions on the Kubernetes cluster for `kubectl-mtv` and `kubectl` operations.
  • ⚠️If using SSE mode without `--tls-cert` and `--tls-key`, communication will be over unencrypted HTTP, which is insecure for sensitive data like Kubernetes tokens.
Verified SafeView Analysis
The server executes `kubectl-mtv` and `kubectl` commands. While arguments are passed safely as `[]string` (mitigating shell injection), the underlying commands grant powerful control over a Kubernetes cluster, including creation, modification, and deletion of resources. If the server or the AI assistant interacting with it is compromised, it could lead to significant cluster alterations or data loss. SSE mode defaults to HTTP, which is insecure for sensitive communication (e.g., passing Kubernetes tokens) if not explicitly configured with TLS (--tls-cert, --tls-key). Token authentication via the Authorization header is supported in SSE mode; while tokens are sanitized for display/logging, they are passed directly to `kubectl`/`kubectl-mtv` for authentication.
Updated: 2025-11-28GitHub→
0
0
Medium Cost
zenvite2 icon
Sec3

Integrates a self-hosted Bitbucket Server with the MetaMCP protocol, providing programmatic access to Bitbucket functionalities for potential AI agent interaction.

Setup Requirements

  • ⚠️Requires access to a self-hosted Bitbucket Server instance (not Bitbucket Cloud).
  • ⚠️Requires Python 3.10+ and the UV package manager for dependency management.
  • ⚠️Requires explicit configuration of Bitbucket Server credentials (URL, username/password or token) in a `.env` file.
Review RequiredView Analysis
CRITICAL: The `BitbucketClient` explicitly disables SSL verification (`verify_ssl: False`) by default, making connections vulnerable to man-in-the-middle attacks. This is a severe security risk. Additionally, hardcoded Bitbucket credentials (`viettq32`, `Tqv@@@4302`) are present in test files (`test_connection.py`, `comprehensive_test.py`, `test_mcp_client.py`), posing a risk of credential exposure.
Updated: 2025-11-24GitHub→
0
0
Low Cost
netbuffer icon
Sec9

This server provides real-time host system information (CPU, memory, disk, OS) via a Model Context Protocol (MCP) interface, designed for integration with Spring AI applications.

Setup Requirements

  • ⚠️Requires Java 17 or higher
  • ⚠️Requires Maven 3.6.3 or higher for building
  • ⚠️Requires MCP client configuration for integration (e.g., ~/.mcp/config.json)
Verified SafeView Analysis
The server uses the OSHI library to collect read-only system information. It does not involve dynamic code execution, 'eval' functions, or direct network listening (configured as 'web-application-type: none' and uses stdio for MCP). No hardcoded secrets were found. The primary risk would be if the MCP client is compromised and misuses the system information, or if a highly sensitive system runs this in an untrusted environment.
Updated: 2025-12-07GitHub→
0
0
Medium Cost
mstfcck icon
Sec8

The API BDD Test Case Generator is an MCP (Model Context Protocol) server that automatically generates BDD (Behavior-Driven Development) test scenarios in Gherkin format from OpenAPI 3.0/3.1 specifications, enabling deep API analysis, comprehensive test generation (happy path, validation, security, edge cases), and export to multiple formats.

Setup Requirements

  • ⚠️Requires Node.js 18.0.0 or higher.
  • ⚠️Requires TypeScript for building (if not using npx directly or a pre-built image).
  • ⚠️Requires Docker for containerized deployment.
Verified SafeView Analysis
The server uses Node's native 'fs/promises' for file operations, which are appropriately used for reading input specifications and writing generated output files. When deployed via Docker Compose, these file system operations are confined to designated read-only (input specs) and read-write (output features) directories, limiting potential for unintended file access. The 'RefResolver' in its current implementation specifically handles local references ('#/') and would throw an error for external file or HTTP references, mitigating a potential arbitrary file loading risk. No 'eval' or direct command execution vulnerabilities are present in the provided source code. No hardcoded credentials were identified.
Updated: 2025-12-07GitHub→
0
0
High Cost
consigcody94 icon

sentinel-ai

by consigcody94

Sec8

Provides real-time, pattern-based security scanning for code, specifically targeting vulnerabilities in AI-generated code, integrated via the Model Context Protocol.

Setup Requirements

  • ⚠️Requires Node.js version 18.0.0 or higher.
  • ⚠️Requires TypeScript 5.7.2 or higher for building from source.
  • ⚠️Requires local setup and configuration within an MCP client (e.g., Claude Desktop config file).
  • ⚠️The tool needs read access to the target code directories and files being scanned.
Verified SafeView Analysis
The server's internal code appears secure and primarily focuses on detecting vulnerabilities in scanned code rather than introducing them. It uses `path.resolve()` for user-provided file paths, which helps mitigate simple path traversal but does not inherently sandbox file access; a malicious AI client could potentially request scans of sensitive system files if the server process has read permissions. No internal use of `eval` or `child_process.exec` with user input was observed. Communication occurs via standard I/O, limiting direct network attack vectors.
Updated: 2025-11-22GitHub→
0
0
Medium Cost
MADARAKZ icon

MCP-Server

by MADARAKZ

Sec3

Manages Kubernetes OPA Gatekeeper policies using natural language commands, powered by Google Gemini AI.

Setup Requirements

  • ⚠️Requires `GEMINI_API_KEY` environment variable for AI features (Google Gemini API costs may apply).
  • ⚠️Requires a Kubernetes cluster with OPA Gatekeeper installed.
  • ⚠️Requires `kubectl` configured and connected to the Kubernetes cluster.
  • ⚠️Requires Go >= 1.23 to build and run the client/server binaries.
  • ⚠️The client connects to the Gemini API with TLS certificate verification disabled, posing a significant security risk for API key transmission and data integrity.
Review RequiredView Analysis
CRITICAL VULNERABILITY: The `mcp-client/ai.go` explicitly disables SSL/TLS certificate verification by setting `TLSClientConfig: &tls.Config{InsecureSkipVerify: true}` when connecting to the Google Gemini API. This makes the connection highly susceptible to Man-in-the-Middle (MITM) attacks, allowing an attacker to intercept, read, and modify communication (including the GEMINI_API_KEY) without detection. This is a severe security flaw. Additionally, the `mcp-server/k8s.go` executes `kubectl` commands based on input from the client (and implicitly, the AI). While `exec.CommandContext` mitigates simple shell injection, the server acts as a trusted executor for arbitrary YAML content (from files or AI-generated manifests). There is no inherent validation of the YAML content itself by the server before applying it, making the system vulnerable to malicious policy definitions or resource deployments if the AI hallucinates harmful content or the client is compromised.
Updated: 2026-01-18GitHub→
0
0
Medium Cost
ltphat2204 icon

teable-mcp-server

by ltphat2204

Sec9

Connects Teable, an open-source no-code database, to LLMs, enabling AI agents to query records and explore schema structures using natural language.

Setup Requirements

  • ⚠️Requires a Teable API Key with all read permissions enabled for the scopes (spaces, bases, tables, records, views, fields).
  • ⚠️The project must be built (`npm install && npm run build`) before running if using the local source code.
  • ⚠️Configuration relies on `TEABLE_API_KEY` and `TEABLE_BASE_URL` environment variables, which must be set appropriately by the calling client or manually for local development.
Verified SafeView Analysis
The server uses environment variables for `TEABLE_API_KEY` and `TEABLE_BASE_URL`, validating the API key's presence. It communicates with the Teable API via `axios` using standard HTTP requests and authorization headers. No usage of `eval` or other dangerous dynamic code execution patterns were found. Error handling in tool calls directly returns API error messages, which could potentially expose more detail than strictly necessary from the upstream Teable API, but is a common practice for direct API proxies.
Updated: 2025-12-14GitHub→
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