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Vetted Servers(9120)

32
2
Medium Cost
MiloCreditPlatform icon

loanpro-mcp-server

by MiloCreditPlatform

Sec9

Exposes read-only LoanPro financial data via Model Context Protocol (MCP) using HTTP, SSE, and stdio transports for integration with AI agents or other clients.

Setup Requirements

  • ⚠️Requires LoanPro API credentials (LOANPRO_API_URL, LOANPRO_API_KEY, LOANPRO_TENANT_ID) which are likely paid/private.
  • ⚠️Requires a Go 1.21+ development environment to run from source.
  • ⚠️Primarily designed for interaction with a Model Context Protocol (MCP) client, not a standalone user interface.
Verified SafeView Analysis
The server correctly uses environment variables for API credentials, avoiding hardcoded secrets. It employs structured logging (`slog`). The code is well-structured Go with no obvious malicious patterns, 'eval' usage, or obfuscation. CORS is broadly enabled ('Access-Control-Allow-Origin: *'), which is common for APIs but could be restricted further for specific deployment scenarios if stricter access control is desired.
Updated: 2025-12-12GitHub→
32
2
Medium Cost

mcp-servers

by mericozkayagan

Sec8

This repository provides Model Context Protocol (MCP) servers for Cursor AI to integrate with external tools like PostgreSQL, Obsidian, n8n, and Google Gemini for various AI-driven operations.

Setup Requirements

  • ⚠️Requires multiple external services (PostgreSQL database, n8n instance, Obsidian with Local REST API plugin) to be pre-configured and running.
  • ⚠️Requires obtaining and securely configuring several API keys (Google Gemini, n8n, Obsidian) and PostgreSQL database connection strings.
  • ⚠️Requires both Node.js 18+ and Python 3.11+ environments, with separate installation and build steps for each MCP server type.
Verified SafeView Analysis
The servers are designed to run locally and do not expose services to the network by default, which is a strong security measure. However, they require sensitive API keys (Gemini, n8n, Obsidian) and database credentials (PostgreSQL) which must be securely managed via environment variables and never committed to version control. The Obsidian MCP server has read/write access to local notes, and the Gemini server implements path validation to prevent directory traversal attacks.
Updated: 2025-11-17GitHub→
32
2
Medium Cost
sbl-sdsc icon

mcp-proto-okn

by sbl-sdsc

Sec7

Provides an AI assistant interface to query scientific knowledge graphs via SPARQL endpoints, with specialized support for Proto-OKN and FRINK platforms.

Setup Requirements

  • ⚠️Requires a compatible client application like Claude Desktop (Pro/Max subscription) or VS Code Insiders (GitHub Copilot subscription).
  • ⚠️Requires Python 3.10+ to be installed.
  • ⚠️Requires 'uvx' (or 'pip') to be installed and accessible in your PATH for execution.
  • ⚠️Requires network access to the specified SPARQL endpoint and raw.githubusercontent.com for metadata and documentation.
Verified SafeView Analysis
The server acts as a proxy, directly forwarding user-provided SPARQL queries to target endpoints. While the server itself does not execute arbitrary code, it exposes the underlying SPARQL endpoint to potential SPARQL injection attacks if the AI or user crafts malicious queries. No direct 'eval' or obvious hardcoded secrets found. Relies on fixed GitHub URLs for metadata retrieval, mitigating Server-Side Request Forgery (SSRF) risk for those specific fetches. The 'clean_mermaid_diagram' tool uses regex for string manipulation, which is generally safe but could pose a theoretical ReDoS (Regular Expression Denial of Service) risk with extremely complex or malicious patterns on untrusted input.
Updated: 2026-01-18GitHub→
32
2
Low Cost
sandraschi icon
Sec4

Manages database connections, executes queries and data operations, inspects schema, and performs advanced browser bookmark management across multiple browsers and database types, including AI-powered features for tagging and analysis.

Setup Requirements

  • ⚠️Requires Python 3.8+.
  • ⚠️Requires `uv` (Python package manager) to run the server via the provided command.
  • ⚠️Windows-specific operations require a Windows operating system and may need administrator privileges for registry access.
  • ⚠️Firefox browser must be completely closed for write operations on its database to avoid locking issues, or `force_access=True` must be used (labeled 'dangerous!' in docs).
  • ⚠️Database servers (e.g., PostgreSQL, MongoDB, ChromaDB) must be running and accessible for respective operations.
  • ⚠️External API keys (e.g., OpenAI) are implicitly required for AI-powered features if they are to perform actual API calls.
Review RequiredView Analysis
The server involves extensive direct file system access to sensitive user data (browser profiles, Plex DBs) and direct database operations. This poses risks of path traversal or data corruption if input paths and queries are not rigorously sanitized by the caller. The `ENABLE_PASSWORD_STORAGE` environment variable, when enabled, allows storing database credentials on disk, which is explicitly labeled a 'DEV MODE' feature and is a significant security risk if used in production or without proper local encryption. Operations like 'bruteforce' database access for Firefox (explicitly labeled 'dangerous!') further highlight potential stability or integrity risks. While parameterized queries are recommended in some tool docstrings, the responsibility lies with the caller to prevent SQL injection. External API calls for AI features require secure management of API keys and careful consideration of data privacy.
Updated: 2026-01-16GitHub→
32
2
High Cost
cozycrafters icon

cozyreq

by cozycrafters

Sec6

An AI agent to interact with and explore API endpoints by consuming OpenAPI specifications and user prompts.

Setup Requirements

  • ⚠️Requires Python 3.14+
  • ⚠️Requires 'mise' for dependency management and environment setup.
  • ⚠️Relies on a Pydantic AI agent (pydantic-ai) which will incur token costs with the underlying AI model (e.g., Mistral).
Verified SafeView Analysis
The application fetches OpenAPI specifications from user-provided URLs. While `httpx` and `openapi-pydantic` are used for fetching and parsing, a malicious URL or a malformed specification could potentially lead to SSRF or resource exhaustion. The core functionality relies on a `pydantic-ai` agent to interact with APIs based on the prompt and spec, the security of which depends on the `pydantic-ai` library's internal safeguards against arbitrary code execution or unintended actions by the AI model. No explicit 'eval' or hardcoded secrets were found.
Updated: 2026-01-17GitHub→
32
2
Medium Cost
JupiterOne icon
Sec9

Provides a Model Context Protocol (MCP) server that enables AI assistants and other MCP clients to interact with JupiterOne's security data, rules, dashboards, and integrations.

Setup Requirements

  • ⚠️Requires an active JupiterOne Account, API Key, and Account ID.
  • ⚠️Requires Node.js version 18 or greater to run.
  • ⚠️For new rules and dashboard widgets, J1QL queries must be syntactically correct and adhere to JupiterOne's specific query language rules; the server includes validation and suggestions for common errors.
Verified SafeView Analysis
The server uses `dotenv` to load environment variables for sensitive credentials (API key, account ID, OAuth token), preventing hardcoding. Input validation is performed using `zod` schemas for tool parameters, reducing risks of malformed input. Interactions with the JupiterOne API are via `graphql-request` and `fetch` for deferred queries, which are standard and generally secure, assuming the JupiterOne API itself is trusted. Query validation is implemented for J1QL, which adds a layer of defense against invalid or potentially harmful queries, although it focuses on syntax rather than direct injection into the underlying database (J1QL is not SQL). There are no obvious uses of `eval`, direct command injection points, or unnecessary network access. The `package.json` does not contain unusual or unsafe scripts. The primary security considerations revolve around proper configuration of JupiterOne API credentials.
Updated: 2025-12-05GitHub→
32
2
Medium-High Cost
carmentacollective icon

carmenta

by carmentacollective

Sec6

A heart-centered AI platform that unifies external services and internal AI agents into a single conversational interface, facilitating intelligent routing, personal knowledge management, and automated task execution.

Setup Requirements

  • ⚠️Requires PostgreSQL database with Drizzle ORM configured via DATABASE_URL.
  • ⚠️Full functionality, especially scheduled agents, requires a running Temporal server accessible via TEMPORAL_ADDRESS.
  • ⚠️Requires API keys for at least one LLM provider (OpenRouter or Vercel AI Gateway) and Clerk for user authentication.
  • ⚠️Code Mode (`/api/code`) executes real bash commands and requires specific project path configurations or workspace mode setup.
Review RequiredView Analysis
The system employs robust AES-256-GCM encryption for API keys and OAuth tokens stored in the database, and uses SVIX for webhook signature verification. However, it exposes high-privilege features: 1. **Code Mode**: Allows execution of arbitrary bash commands (`lib/code/bash-executor.ts`) within user-selected project paths. While `DANGEROUS_DIRS` are defined and environment variables are sanitized, this feature inherently carries a high risk if not rigorously sandboxed and permissioned for specific, trusted users/environments. 2. **MCP Servers**: AI agents can configure and interact with custom Microservice Control Plane (MCP) servers, allowing dynamic integration of external APIs via `createMcpServer` and `raw_api` calls. This significantly expands the attack surface, as a malicious or compromised MCP server could execute arbitrary operations. 3. **Raw API Access**: Many service adapters (e.g., LinkedIn, CoinMarketCap, Gmail, Spotify) expose a `raw_api` operation, enabling AI agents to make direct, arbitrary calls to external service APIs. This bypasses higher-level abstractions and requires careful control of the agent's intent and context. Rigorous access control, environment isolation (e.g., running Code Mode in ephemeral, isolated containers), and comprehensive monitoring are critical.
Updated: 2026-01-19GitHub→
32
2
Medium Cost
elifarley icon

hug-scm

by elifarley

Sec9

Provides AI assistants with access to Hug SCM commands for investigating and understanding Git and Mercurial repositories.

Setup Requirements

  • ⚠️Python 3.10+ required
  • ⚠️Hug SCM CLI must be installed and in system PATH
  • ⚠️Requires manual configuration of Claude Desktop's `claude_desktop_config.json`
Verified SafeView Analysis
The server explicitly states that it only exposes read-only operations to AI assistants, preventing any destructive actions on the repository. It uses `subprocess.run` with list arguments, which mitigates shell injection risks. Path validation is implemented to prevent directory traversal attacks and ensure commands operate on valid, existing directories. No hardcoded secrets or direct network listening are evident in the provided source code.
Updated: 2026-01-19GitHub→
32
2
High Cost
tonyredondo icon

debugger-mcp-server

by tonyredondo

Sec8

A cross-platform server and CLI for remote crash dump analysis, interactive debugging, and AI-assisted diagnostics using WinDbg or LLDB.

Setup Requirements

  • ⚠️Requires .NET 10 SDK.
  • ⚠️Requires a platform debugger: WinDbg (Windows) or LLDB (Linux/macOS).
  • ⚠️AI analysis features require an API key for a supported LLM provider (OpenRouter, OpenAI, Anthropic), which are typically paid services.
Verified SafeView Analysis
The server provides optional API key authentication, CORS configuration, and rate limiting. File uploads are rigorously validated (magic bytes, size limits, ZIP extraction rules, path sanitization). Session isolation ensures users only access their own data. The 'exec' command allows raw debugger command execution, which is powerful and requires high privilege on the host, but is an intended function of a debugging server. Sensitive debugging outputs and LLM prompts/responses can be traced to disk if enabled, which requires careful handling.
Updated: 2026-01-13GitHub→
32
2
Low Cost

cozyreq

by cozyguerrilla

Sec7

Facilitates interaction between AI agents and a custom API.

Setup Requirements

  • ⚠️Requires installation and management of multiple language runtimes: Rust, Python, and Node.js.
  • ⚠️Requires 'mise' for project environment management, which might be a new dependency for some users.
Verified SafeView Analysis
The server enables AI agent interaction via an API. Inherent risks include insecure API endpoints, lack of input validation, and potential for injection attacks by malicious agents or users if not properly secured. The 'inspector' feature, if exposed without authentication, could also pose a risk. No explicit 'eval' or obfuscation is evident from the file list, but multi-language projects can be harder to audit comprehensively.
Updated: 2025-11-17GitHub→
32
2
Low Cost
talkops-ai icon

talkops-mcp

by talkops-ai

Sec9

A Model Context Protocol (MCP) server for AI assistants to manage Kubernetes applications via ArgoCD using GitOps principles, enabling secure, production-grade operations.

Setup Requirements

  • ⚠️Requires a running ArgoCD Server (v2.x) instance.
  • ⚠️The `ARGOCD_AUTH_TOKEN` environment variable is required for authentication with ArgoCD.
  • ⚠️The server defaults to read-only mode (`MCP_ALLOW_WRITE=false`); explicit setting to `true` is required to enable write operations.
  • ⚠️Git credentials (e.g., `GIT_PASSWORD` for HTTPS or `SSH_PRIVATE_KEY_PATH` for SSH) are required for repository onboarding functionality.
Verified SafeView Analysis
The ArgoCD MCP server implements robust security. It defaults to a read-only mode (`MCP_ALLOW_WRITE=false`), preventing all mutating operations unless explicitly enabled. Sensitive credentials (ArgoCD API tokens, Git passwords/SSH keys) are read from environment variables or secure file paths, ensuring they are never exposed to LLM models. It supports TLS verification for secure connections to ArgoCD. The codebase appears to follow good practices for input validation and avoids dangerous patterns like `eval` for command execution, leveraging explicit whitelisting and validation mechanisms. Network security is good by design (HTTP/SSE endpoint with configurable host/port).
Updated: 2026-01-19GitHub→
32
1
Low Cost
jaysongiroux icon

whoisthere

by jaysongiroux

Sec8

An MCP server enabling LLMs to check domain name availability and discover available Top-Level Domains (TLDs).

Setup Requirements

  • ⚠️Requires Go 1.23+ to build/run locally without Docker.
  • ⚠️Relies on external WHOIS services which may be slow or rate-limited.
Verified SafeView Analysis
The server primarily relies on external WHOIS services (likexian/whois) which inherently involve network requests. Input validation (`CleanAndValidateDomain`) is present before making WHOIS queries. The `http.ListenAndServe` call in `main.go` lacks explicit timeouts, which is flagged by `gosec` (G114) for potential resource exhaustion in production environments, though for an internal MCP server, this might be acceptable. No clear hardcoded secrets, 'eval' usage, or malicious patterns were identified.
Updated: 2025-12-01GitHub→
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