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Vetted Servers(9120)

0
0
Medium Cost
gesteves icon

domestique

by gesteves

Sec9

Integrates fitness data from Intervals.icu, Whoop, and TrainerRoad into a unified MCP server for comprehensive analysis and workout management via AI agents.

Setup Requirements

  • ⚠️Node.js 20+ is a prerequisite.
  • ⚠️Intervals.icu API Key and Athlete ID are mandatory for core functionality.
  • ⚠️Whoop integration requires a multi-step OAuth authorization process involving manual browser interaction and a Redis database for token storage and refresh management. Redis is a hard dependency for Whoop functionality.
  • ⚠️TrainerRoad integration requires a private iCal calendar feed URL.
Verified SafeView Analysis
The project demonstrates strong security practices including the use of environment variables for all sensitive credentials, secure constant-time comparison for the MCP authentication token, and all external API communications use HTTPS. The Whoop OAuth token refresh mechanism is well-implemented with Redis-based distributed locking to prevent race conditions and token invalidation. Error handling is structured to avoid exposing raw details to LLMs. No 'eval' or obfuscation found.
Updated: 2026-01-17GitHub→
0
0
Medium Cost
sundar-nallalagappan icon

FastMCP_Samples

by sundar-nallalagappan

Sec8

Orchestrating multi-protocol communication protocol (MCP) tool servers with various LLM agent frameworks for diverse task execution.

Setup Requirements

  • ⚠️Requires the 'weatherserver.py' script to be run separately in the background for HTTP connections.
  • ⚠️Requires API keys (e.g., GROQ_API_KEY, OPENAI_API_KEY) for LLM providers, which typically involve paid services.
  • ⚠️Python 3.11 or higher is required as specified in 'pyproject.toml'.
Verified SafeView Analysis
The code uses environment variables for API keys (e.g., GROQ_API_KEY, OPENAI_API_KEY), which is good practice. No 'eval' or obvious malicious patterns were found. The 'weatherserver.py' runs an HTTP server on localhost, which is typical for local development/testing and not inherently insecure when confined to localhost. 'mathserver.py' uses stdio transport, which is generally secure. Observability fixes in ADK clients indicate attention to robustness.
Updated: 2026-01-19GitHub→
0
0
Medium Cost
ricCap icon
Sec9

This server integrates Sketch Engine's corpus linguistics and text analysis tools with Claude AI, allowing users to perform complex linguistic research and language analysis directly through conversational prompts.

Setup Requirements

  • ⚠️Requires a free Sketch Engine account to obtain an API key.
  • ⚠️Requires manual download and execution of a standalone executable specific to the user's OS.
  • ⚠️Requires manual editing of a specific Claude Desktop JSON configuration file to register the server and API key.
  • ⚠️Requires Node.js >= 21.0.0 if building/running from source.
Verified SafeView Analysis
No critical vulnerabilities found. The Sketch Engine API key is correctly handled via environment variables and is not hardcoded. Input validation is performed using Zod schemas for all tool calls, mitigating potential injection risks. Local file system writes are constrained to a '.cache' directory within the application's current working directory, which is appropriate for a local agent.
Updated: 2025-12-14GitHub→
0
0
Medium Cost
guvensoft icon

mcp-rag-server

by guvensoft

Sec3

Provides a local Model Context Protocol (MCP) server that analyzes codebases and offers context-aware search and development tools to AI agents.

Setup Requirements

  • ⚠️Python 3.9+ is required, and for the full-featured semantic engine (Sentence-Transformers + ChromaDB), additional Python packages (`sentence-transformers`, `chromadb`, `fastapi`, `uvicorn`) must be manually installed via pip. Without these, a less capable Node.js fallback engine is used.
  • ⚠️Redis (e.g., Memurai on Windows) is implicitly recommended for the BullMQ job queue and QueryCache, adding an external dependency for enhanced indexing and performance.
  • ⚠️The initial indexing process can be time-consuming for large codebases, though incremental indexing and a watcher are in place to manage updates.
Review RequiredView Analysis
The server includes tools like `run_tests` and `run_task` that execute arbitrary shell commands or npm scripts based on agent input. While the project documentation mentions sandbox modes and approval policies for clients, the server itself allows direct execution of these commands. This represents a significant security risk if the server is exposed to untrusted inputs or not operated within a robustly sandboxed environment. File access is somewhat restricted by a policy layer (blocking sensitive file types and large files, limiting to specified roots), and network services bind to localhost by default, mitigating external attack surface.
Updated: 2025-11-25GitHub→
0
0
Medium Cost

cisco-mcp-server

by ine-content

Sec4

A server for managing and interacting with Cisco IOS XE network devices, potentially leveraging an LLM (Claude) for intelligent automation, configuration, or query processing.

Setup Requirements

  • ⚠️Requires an Anthropic Claude API Key (a paid service) to be configured, likely within `claude_desktop_config.json` or via environment variables.
  • ⚠️Requires access to one or more live Cisco IOS XE network devices for the server to manage or interact with.
  • ⚠️Relies on environment variables defined in a `.env` file for configuration and credentials, which must be properly set up prior to execution.
Review RequiredView Analysis
Without access to the actual code, a comprehensive security audit is impossible. However, as a 'server' component (`ios_xe_mcp_server.py`) designed to interact with network devices and external LLMs (indicated by `claude_desktop_config.json` and `.env`), it inherently carries risks. These include potential for unauthenticated access, command injection if interactions with IOS XE devices are not meticulously validated, and exposure of sensitive API keys if `.env` or `claude_desktop_config.json` are not securely managed or if the server logs/exposes their values. Running this server likely involves opening network ports and processing external input, necessitating robust validation and authentication to ensure safety.
Updated: 2025-11-17GitHub→
0
0
Low Cost
conda-forge icon
Sec9

Provides a Managed Compute Platform (MCP) server for integrating with the Langsmith SDK.

Setup Requirements

  • ⚠️Requires Python 3.x (specific minor version derived from 'python_min' in recipe)
  • ⚠️Intended for installation via Conda or Mamba
Verified SafeView Analysis
The provided source code primarily consists of build recipes and CI/CD configurations. No 'eval' statements, obfuscation, or hardcoded secrets were found within these configuration and build scripts. The `build-locally.py` script uses `subprocess.check_call` to execute other build scripts, which is standard for such tools. However, the core Python server application logic (`langsmith_mcp_server.server:main`) is not available for a full security audit.
Updated: 2025-11-22GitHub→
0
0
Medium Cost
plantoncloud-inc icon

mcp-server-planton

by plantoncloud-inc

Sec8

The Planton Cloud MCP Server enables AI agents to query, create, update, and delete cloud resources using user-scoped permissions via the Model Context Protocol (MCP).

Setup Requirements

  • ⚠️Requires a Planton Cloud API Key, obtained from the Planton Cloud Console, which is a critical setup step for all operations.
  • ⚠️Specific Go version requirements (Go 1.24.7 in `go.mod`, Go 1.25 for Docker builds/CI/CD) and nuances with `go mod tidy` can be a friction point for local development environments.
  • ⚠️Requires a compatible MCP client (e.g., Cursor, Claude Desktop, LangGraph) for interaction; it doesn't offer a direct web UI or basic CLI for general users to interact with its tools.
Verified SafeView Analysis
The server employs a 'passthrough' security model for HTTP transport: user API keys from the Authorization header are extracted and forwarded to Planton Cloud APIs for authentication and fine-grained authorization (FGA), rather than being validated locally. This offloads robust validation to the backend. The `PLANTON_API_KEY` is handled securely via environment variables or request headers and is not hardcoded. A global state workaround is used for API key context propagation in tool handlers due to `mcp-go` library limitations, which could introduce theoretical race conditions in high-concurrency (non-SSE typical) scenarios. The project has a defined security reporting process.
Updated: 2025-12-14GitHub→
0
0
Medium Cost
heejung0 icon
Sec4

This server exposes the Dissect digital forensics library as a Model Context Protocol (MCP) server, enabling MCP clients to extract and analyze various artifacts from disk images (E01, RAW, VMDK, etc.).

Setup Requirements

  • ⚠️Requires installation of Dissect binaries (`target-query`, `rdump`, `target-fs`, `acquire`) and their correct paths specified via environment variables (`DISSECT_TARGET_QUERY`, etc.).
  • ⚠️Processing large disk images requires significant local storage and computational resources.
  • ⚠️Output directories (`DISSECT_ACQUIRE_DIR`, `DISSECT_EXTRACT_DIR`) must have appropriate write permissions and sufficient space.
Review RequiredView Analysis
The server executes external binaries (`target-query`, `rdump`, `target-fs`, `acquire`) via `subprocess.run` with user-controlled arguments like `image_path`, `plugin`, `search`, `fs_path`, `username`, `profile`, and `output_type`. While `subprocess.run` with a list of arguments (`List[str]`) typically prevents direct shell injection, it does not mitigate application-level injection if the `dissect` binaries themselves interpret specially crafted arguments as commands or options. A significant risk is the ability to specify an `output_dir` in `extract_file_or_directory` and `acquire_minimal_artifacts`. Without robust path sanitization (e.g., preventing `../../../` traversal), a malicious client could potentially instruct the server to write or extract files to arbitrary locations on the host system, leading to privilege escalation, data destruction, or denial of service. The security posture is highly dependent on the robustness of the underlying `dissect` tools against malicious input passed as arguments.
Updated: 2025-12-13GitHub→
0
0
High Cost
irab icon

orgbrain

by irab

Sec6

Provides a Model Context Protocol (MCP) server for knowledge extraction and cross-repository analysis within multi-repository organizations.

Setup Requirements

  • ⚠️Requires Node.js 18+ and pnpm.
  • ⚠️Requires GitHub CLI (`gh`) to be installed and authenticated for `connect_org` functionality (`gh auth login`).
  • ⚠️Relies on `git` and `gh` executables being in the system's PATH.
  • ⚠️Tree-sitter parsers for Rust, Go, Python require native modules, which are expected to be available.
Verified SafeView Analysis
The system involves cloning repositories and executing `git` commands, which inherently carries risks if interacting with untrusted code. The `GitManager` explicitly sets `GIT_SSH_COMMAND: "ssh -o BatchMode=yes -o StrictHostKeyChecking=no"`. Disabling `StrictHostKeyChecking` bypasses host key verification for SSH connections, making the cloning process vulnerable to Man-in-the-Middle (MITM) attacks if the git remote server is compromised or impersonated. While this prevents interactive prompts and might be acceptable in tightly controlled environments (e.g., trusted CI/CD against a trusted GitHub instance), it is a significant security consideration for a general-purpose tool.
Updated: 2026-01-08GitHub→
0
0
Medium Cost

mcp-gdocs

by marcosremar

Sec7

A document processing and conversion tool that facilitates operations between Markdown, PDF, and Google Docs, likely integrating LLMs for content correction and formatting.

Setup Requirements

  • ⚠️Requires Google API Key (may incur costs for high usage)
  • ⚠️Requires LLM API Key (e.g., Claude, typically a paid service)
  • ⚠️Potentially integrates with or requires the 'Cursor' development environment/tool
Verified SafeView Analysis
The system interacts with external APIs (Google Docs, potentially LLMs) and utilizes shell scripts, which inherently carry network and execution risks. API keys for external services must be handled securely. Without source code inspection, specific vulnerabilities cannot be identified, but no obvious 'eval' or obfuscation is apparent.
Updated: 2025-11-18GitHub→
0
0
Low Cost
shobhit743 icon
Sec9

Manages personal financial expenses by allowing users to add, list, and summarize expenditures with categorization via an API.

Setup Requirements

  • ⚠️Requires Python 3.13 or newer.
Verified SafeView Analysis
The server uses parameterized SQL queries via aiosqlite, which effectively mitigates SQL injection vulnerabilities. No 'eval', 'exec', or other obvious dangerous patterns were found. A broad 'try...except' block in 'add_expense' could mask specific errors but does not pose a direct security risk.
Updated: 2025-12-13GitHub→
0
0
Medium Cost
d01ki icon

Pentest-mcp

by d01ki

Sec8

Unified penetration testing toolkit for authorized systems, integrated with Claude Desktop.

Setup Requirements

  • ⚠️Requires OpenAI API Key (Paid) for LLM agent functionality.
  • ⚠️Docker is required for the recommended easy setup and server isolation.
  • ⚠️Nmap must be installed on the host system (or within the Docker container for the unified server).
Verified SafeView Analysis
The server implements robust command sanitization (using `shlex.quote` and dangerous character checks), human-in-the-loop approval for destructive operations, and masking of sensitive data in logs. It explicitly warns users against unauthorized use and emphasizes Docker isolation for security. The core functionality involves inherently risky penetration testing actions (e.g., SSH brute force, SQL injection tests), so proper authorization and strict ethical considerations are paramount for the user to avoid misuse. The Playwright integration also introduces potential browser-based risks if not used carefully.
Updated: 2026-01-19GitHub→
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