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Vetted Servers(9120)

0
0
Medium Cost
otsuboyumeto icon

line-mcp-server

by otsuboyumeto

Sec8

An MCP (Multi-Agent Communication Protocol) server for sending messages to LINE groups and personal chats, primarily for integration with an agent system like Manus.

Setup Requirements

  • ⚠️Requires a LINE Messaging API Channel setup, including obtaining a Channel Access Token.
  • ⚠️Requires a LINE Group ID or a LINE User ID for messaging targets. These IDs need to be manually retrieved.
  • ⚠️The server must be publicly accessible (e.g., deployed on Render or exposed via ngrok) for LINE webhooks to function and for an MCP client to connect.
Verified SafeView Analysis
The server properly uses environment variables for sensitive information (LINE_CHANNEL_ACCESS_TOKEN, LINE_GROUP_ID, PERSONAL_USER_ID), which is a good practice. There are no clear 'eval' calls, code injection points, or hardcoded secrets found. Input validation is present for required message parameters. The logging of webhook bodies should be handled with care in production to avoid exposing sensitive user data, though it's standard for debugging.
Updated: 2025-12-01GitHub→
0
0
Low Cost

adhd-tasks-mcp

by transform-ia

Sec9

Provides a GraphQL interface for managing ADHD tasks, bridging LLM agents to a Hasura/PostgreSQL backend.

Setup Requirements

  • ⚠️Requires a running Hasura GraphQL endpoint with the 'adhd-tasks' schema.
  • ⚠️Requires a Kubernetes environment for deployment via the provided Helm chart, or manual setup of the Go application with environment variables.
  • ⚠️The HASURA_ADMIN_SECRET must be securely managed, typically via Kubernetes secrets.
Verified SafeView Analysis
The code appears to be well-structured and follows standard Go practices. No 'eval' or similar dangerous patterns were found. The primary security consideration is the secure management of the HASURA_ADMIN_SECRET, which is handled via Kubernetes secrets, a good practice. The use of standard libraries and the absence of obfuscation suggest a safe codebase.
Updated: 2025-11-22GitHub→
0
0
Medium Cost
schwarztim icon

web-search-mcp

by schwarztim

Sec4

Provides web search and page content fetching capabilities using DuckDuckGo for LLM proxies via the Model Context Protocol.

Setup Requirements

  • ⚠️Requires Node.js (v18+)
  • ⚠️Requires careful network isolation (e.g., containerized environment with egress filtering) due to SSRF vulnerability in `fetch_page` tool, which can access arbitrary URLs.
Review RequiredView Analysis
The `fetch_page` tool allows fetching arbitrary user-provided URLs. This introduces a significant Server-Side Request Forgery (SSRF) risk, enabling potential access to internal network resources, port scanning, or DoS attacks if the server is not deployed in a strictly isolated environment with egress filtering. Although there is a 10-second timeout on requests, it does not mitigate the core SSRF vulnerability. The HTML scraping for search results is robust enough but relies on regex, which can be brittle to changes in DuckDuckGo's HTML structure. No 'eval' or hardcoded secrets were found.
Updated: 2026-01-16GitHub→
0
0
Low Cost
nicholaswilde icon

rescue-groups-mcp

by nicholaswilde

Sec9

Provides a Model Context Protocol (MCP) server for AI assistants (like Claude) to search for adoptable pets, list breeds, and find rescue organizations using the RescueGroups.org API.

Setup Requirements

  • ⚠️Requires a RescueGroups API Key (requires registration and approval from RescueGroups.org).
  • ⚠️Requires Rust toolchain if not running via Docker.
  • ⚠️API key must be provided via environment variable (RESCUE_GROUPS_API_KEY) or a configuration file (config.toml, config.yaml, or config.json).
Verified SafeView Analysis
API keys are handled via environment variables or configuration files, not hardcoded. Optional bearer token authentication is available for HTTP mode. Uses structured logging for errors and warnings, preventing sensitive information leakage to clients. Relies on `reqwest` for secure HTTP communication. Overall robust error handling and input validation patterns.
Updated: 2026-01-18GitHub→
0
0
Medium Cost
davidpham87 icon

mcp-bb-clj

by davidpham87

Sec7

Provides an MCP (Model Context Protocol) server and client, implemented in Babashka Clojure, offering a suite of code-centric tools for formatting, linting, and schema validation.

Setup Requirements

  • ⚠️Babashka must be installed on the system.
  • ⚠️The 'clj-kondo' binary must be installed and available in the system PATH for the corresponding tool to function.
Verified SafeView Analysis
The 'clj-kondo' tool relies on an external binary that must be installed and available in the system PATH, introducing a dependency on the security and trustworthiness of that binary. The server exposes functionality via HTTP, requiring standard network security practices for deployment. No 'eval' or obvious malicious patterns were identified in the provided code snippet.
Updated: 2025-11-25GitHub→
0
0
Low Cost
madhaviai icon
Sec10

A Model Context Protocol (MCP) server that provides tools to retrieve professional profile information, skills, education, and contact details for an AI assistant or developer environment.

Setup Requirements

  • ⚠️Requires the 'mcp-server' Python package to be installed (e.g., `pip install mcp-server`).
  • ⚠️The 'run_command' provided in the `mcp.json` configuration uses an absolute path (`/Users/madhavik/...`) which will need to be adjusted by other users to match their local file system.
  • ⚠️Requires Python 3.x, likely 3.8+ due to asyncio usage.
Verified SafeView Analysis
The server operates by providing static, read-only professional profile data. It does not perform any file I/O, network requests, or execute arbitrary code based on user input. No 'eval', 'exec', 'os.system', or similar dangerous functions are used. All data is hardcoded within the script. Communication is via standard I/O, making it inherently secure for its intended use case.
Updated: 2025-12-12GitHub→
0
0
Medium Cost
ramailo1 icon
Sec4

DriftGuard is an MCP server that acts as a 'Traffic Controller' for AI agents, enforcing a strict state machine to prevent 'code drift' during development tasks.

Setup Requirements

  • ⚠️Requires Node.js version 18 or higher to run.
  • ⚠️Must be run within a Git repository to fully utilize L3 Git Notes (audit trails, risk analysis, integrity checks).
  • ⚠️The system grants the AI agent the ability to define and execute arbitrary shell commands (e.g., for `dg_verify`), necessitating trust in the agent or a sandboxed execution environment.
Verified SafeView Analysis
The server's core functionality involves executing arbitrary commands (`testCommand`) provided by the AI agent via the `dg_verify` tool. While this is an intended feature to allow agents to run tests, it means the server can be an arbitrary code execution vector if a malicious or compromised agent interacts with it, or if untrusted commands are passed to the agent. No explicit hardcoded secrets or direct `eval` calls were found. File system operations are scoped to `.driftguard/` for internal state and project files as claimed by tasks.
Updated: 2026-01-18GitHub→
0
0
Medium Cost
algtools icon

mcp-server

by algtools

Sec8

Deploys an authentication-less Model Context Protocol (MCP) server on Cloudflare Workers, providing AI-powered tools for mathematical calculations and RAG-based search.

Setup Requirements

  • ⚠️Requires a Cloudflare Workers account and project setup.
  • ⚠️Requires Workers AI (AI binding) to be configured in your Cloudflare Worker project.
  • ⚠️Requires a Cloudflare Durable Object Namespace binding (MCP_OBJECT) for state management.
  • ⚠️The 'searchCursorRules' tool requires an existing Cloudflare AI AutoRAG vector store, defaulting to 'algtools-cursor-rules-rag', or an ID provided via AUTORAG_ID environment variable.
Verified SafeView Analysis
The server is explicitly designed to be 'auth-less', meaning any client can access its tools. While input validation (zod) is used for tool arguments and external calls are directed to Cloudflare AI services, the inherent nature of 'auth-less' means it should not be used for sensitive operations or exposed without proper network controls if authentication is desired. There are no obvious code-level vulnerabilities like arbitrary code execution (eval) or hardcoded secrets beyond its intended functionality.
Updated: 2025-12-03GitHub→
0
0
Low Cost
scanzy icon
Sec1

This project provides an interface for AI to programmatically edit 2D CAD files in Draftsight on Windows using Win32 COM APIs.

Setup Requirements

  • ⚠️Requires Draftsight CAD software to be installed on a Windows machine.
  • ⚠️Specific Windows environment and permissions may be needed for Win32 COM API interaction.
Review RequiredView Analysis
The provided source code consists only of the README.md file. A security audit of the actual implementation is not possible without the relevant code files. Interacting with local applications via Win32 COM APIs carries inherent risks if not implemented securely, which cannot be assessed here.
Updated: 2025-11-25GitHub→
0
0
High Cost
uditsanghvi07 icon
Sec9

An agentic expense processing system for zero-shot data extraction from invoice images and autonomous expense classification into structured reports.

Setup Requirements

  • ⚠️Requires an external Multimodal LLM service (e.g., OpenAI, Google Gemini, Anthropic) for zero-shot data extraction and autonomous classification, which typically involves API keys and potential paid usage.
  • ⚠️Requires multiple Python processes to be run concurrently: the MCP server (main.py), a FastAPI backend (backend.py), and a Streamlit frontend (frontend.py).
Verified SafeView Analysis
The server uses SQLite with parameterized queries, mitigating SQL injection risks. No direct 'eval' or other dangerous functions are present. Database file paths are relative and localized. No hardcoded sensitive credentials were found. The use of 'check_same_thread=False' for SQLite in backend.py is common with FastAPI but should be understood for potential concurrency implications, though not a direct security flaw in this context.
Updated: 2025-11-20GitHub→
0
0
Medium Cost
ry-ops icon
Sec9

Manages and monitors Kubernetes (K3s) clusters through a Model Context Protocol (MCP) server, enabling AI-powered cluster management via Claude.

Setup Requirements

  • ⚠️Requires a valid Kubernetes `kubeconfig` file with appropriate RBAC permissions, and its path must be provided via the `KUBECONFIG` environment variable. Updating the `server` field in the `kubeconfig` to the correct K3s API IP/hostname is often necessary.
  • ⚠️Requires network connectivity from the machine running the MCP server to the K3s cluster's API server.
  • ⚠️Requires Python 3.10 or higher and the `uv` package manager for dependency management and execution.
Verified SafeView Analysis
The server leverages standard Kubernetes `kubeconfig` for authentication and authorization. Security critically depends on keeping the `kubeconfig` file secure, setting appropriate file permissions (e.g., 600), and configuring Kubernetes RBAC (Role-Based Access Control) to grant only necessary permissions. The code itself does not introduce hardcoded secrets or obvious vulnerabilities, and documentation explicitly highlights security best practices for `kubeconfig` and RBAC.
Updated: 2025-12-17GitHub→
0
0
High Cost
talianzakowski icon

py2to3mcp

by talianzakowski

Sec6

Facilitates Python 2 to 3 code migration, provides file system manipulation, and offers code search capabilities for AI-driven development.

Setup Requirements

  • ⚠️Requires Python 3.9 or higher.
  • ⚠️Requires specific configuration in `~/.claude.json` with absolute paths for each MCP server.
  • ⚠️The `py2to3-migration` server recommends installing `fissix` (`pip install fissix`) for full functionality and file conversion; otherwise, it falls back to the system's `2to3` command which may not be present or compatible.
Verified SafeView Analysis
The servers expose powerful file system and code execution capabilities. The `filesystem_server` allows reading and writing arbitrary files/directories (within process permissions and defined limits). The `py2to3_server` uses `subprocess.run` to execute external `2to3` or `fissix` commands for code transformation. While these are legitimate tools, the use of `subprocess` and broad file system access introduces a risk vector. A compromised or malicious calling agent could potentially misuse these tools to read/write sensitive files or execute arbitrary code on the host system. Internal `eval` is not present in server logic, only shown in a Python 2 example.
Updated: 2025-11-23GitHub→
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