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Vetted Servers(8554)

38
1
Medium Cost
apiwat-chantawibul icon

small-mcp-server-demos

by apiwat-chantawibul

Sec9

Provides a secure mathematical expression evaluator and an interface to an external search API, designed for AI agent integration.

Setup Requirements

  • ⚠️Requires SEARCH_API_KEY for searchapi.io (a paid service with free credits).
  • ⚠️Docker is required for easy setup and running both services.
  • ⚠️Python 3.13 or newer is required.
Verified SafeView Analysis
The calculator module uses AST parsing and whitelisted operators to safely evaluate mathematical expressions, avoiding direct use of `eval()`. The search-api module securely handles API keys via environment variables and makes standard HTTP requests to an external search service. Reliance on the external `searchapi.io` for query sanitization is implied.
Updated: 2025-12-10GitHub→
38
4
Low Cost
sassoftware icon

sas-mcp-server

by sassoftware

Sec9

Acts as a Model Context Protocol (MCP) server for executing SAS code on SAS Viya environments.

Setup Requirements

  • ⚠️Requires Python 3.12+ and uv 0.8+.
  • ⚠️Requires administrative access to a SAS Viya environment for initial setup, including disabling Content Security Policy (CSP) on SAS Logon Manager for local development and registering an OAuth client with a specific redirect URI.
  • ⚠️The `redirect_uri` for OAuth client registration in Viya is hardcoded to `http://localhost:8134/auth/callback` (or the configured HOST_PORT), which must match the server's `HOST_PORT` if changed from default.
Verified SafeView Analysis
The server itself implements strong OAuth2 with PKCE authentication and securely handles environment variables. It uses `httpx` with SSL verification and cleans up compute sessions. A critical setup requirement, however, is disabling Viya's form-action CSP for local development, which is explicitly noted as not following security best practices and requiring TLS for production. This external configuration is a deployment-level risk/caveat, not a flaw in the server's code itself.
Updated: 2025-12-03GitHub→
38
11
Medium Cost
uarlouski icon

ssh-mcp-server

by uarlouski

Sec9

Provides secure SSH capabilities (command execution, SFTP, port forwarding, templates) for AI assistants to manage remote servers.

Setup Requirements

  • ⚠️Requires Node.js version 18.0.0 or higher.
  • ⚠️A configuration file (`ssh-mcp-config.json`) is mandatory and must specify valid SSH server details, including paths to existing SSH private keys.
  • ⚠️Server startup will fail if there are naming conflicts between manually defined SSH servers and those imported from the `~/.ssh/config` file (if `sshConfigImport` is enabled).
Verified SafeView Analysis
The server implements robust security controls including strict command allowlisting (parsing complex shell chains, substitutions, and backticks to extract all invoked commands), server allowlisting, mandatory SSH key authentication, and comprehensive audit logging. Port forwarding is locally bound by default to prevent external access. Configuration validation ensures essential security parameters are correctly set, and checks for existence of private key files. No 'eval' or obvious malicious patterns were found in the provided source code.
Updated: 2026-01-07GitHub→
38
1
High Cost
noetic-sys icon

index

by noetic-sys

Sec8

Provides local semantic search for project dependencies, integrating as an MCP server for AI tools like Claude Code to prevent hallucinations.

Setup Requirements

  • ⚠️Requires OpenAI API Key (Paid) for embeddings generation, configurable via `idx config set-key`.
  • ⚠️Requires an active internet connection for downloading package source code and communicating with the OpenAI API.
  • ⚠️Consumes local disk space for storing the index database, vector store, and code blobs in a `.index/` directory.
Verified SafeView Analysis
The application downloads and parses source code from public package registries, which inherently carries a risk if malicious code were to exploit parsing vulnerabilities. However, the application uses robust parsing libraries (Tree-sitter) and does not show obvious signs of 'eval', obfuscation, hardcoded secrets, or malicious patterns in its own code. OpenAI API keys are stored locally in a configuration file (`~/.config/idx/config.toml`), which is readable on the local filesystem, but not directly exposed to network risks by the application itself.
Updated: 2026-01-18GitHub→
37
5
Medium Cost
McFuzzySquirrel icon

local-workbook-mcp

by McFuzzySquirrel

Sec9

Enable conversational AI interaction with local Excel workbooks using natural language queries, without transmitting data to external services.

Setup Requirements

  • ⚠️Requires a local LLM server (e.g., LM Studio, Ollama) to be running and configured, typically on 'http://localhost:1234' or 'http://localhost:11434'.
  • ⚠️Requires .NET SDK 9.0+ installed for building and running from source.
  • ⚠️Requires Excel workbook files in .xlsx format (not .xls or other legacy formats) to analyze.
Verified SafeView Analysis
The project is explicitly designed with 'Privacy-First Local Operation' as a core principle, ensuring Excel data never leaves the local machine. It leverages Semantic Kernel's plugin architecture, where predefined tools wrap MCP server functions, limiting arbitrary code execution by the LLM. Error messages are sanitized (e.g., 'Sheet not found' instead of revealing full path/name) to prevent sensitive data exposure, with full details logged locally for troubleshooting. Input validation is present (e.g., JSON schema for pivot analysis). There are no apparent hardcoded critical secrets in the provided code snippets (API keys are noted as 'not-used' or 'not-needed-for-local' for local LLMs). The main risks would be potential vulnerabilities in underlying libraries like ClosedXML or the local LLM itself, or subtle command injection vectors if user inputs are mishandled before reaching the MCP server, though the design aims to mitigate these through controlled tool calls and sanitization.
Updated: 2025-11-28GitHub→
37
9
Low Cost
jztan icon
Sec9

Integrates Redmine project management data with AI assistants via a Model Context Protocol (MCP) server.

Setup Requirements

  • ⚠️Requires Python 3.10+ (for local installation) or Docker for deployment.
  • ⚠️Requires access to an existing Redmine instance.
  • ⚠️Authentication (Redmine API Key or Username/Password) is mandatory and must be configured via a `.env` file.
Verified SafeView Analysis
The server demonstrates strong security practices including explicit handling of SSL/TLS configurations (self-signed, mutual TLS), UUID-based secure file storage, path traversal prevention for file serving, and time-limited access URLs for attachments. A critical path traversal vulnerability was previously addressed and removed. It uses environment variables for sensitive credentials (API key, username/password) with clear documentation to avoid hardcoding or committing them. Default binding to `0.0.0.0` is common in containerized environments but requires awareness for external exposure. Explicit warnings are provided when SSL verification is disabled.
Updated: 2026-01-18GitHub→
37
9
Medium Cost
tosin2013 icon

documcp

by tosin2013

Sec8

DocuMCP is an intelligent Model Context Protocol (MCP) server designed for automating documentation workflows, including analysis, generation, and deployment for GitHub Pages.

Setup Requirements

  • ⚠️Requires LLM API Key (e.g., OpenAI, DeepSeek, Anthropic) or local LLM setup (e.g., Ollama).
  • ⚠️Git must be installed and repository must be initialized for many features.
  • ⚠️Specific Static Site Generator (SSG) CLIs and their language runtimes (Node.js, Python, Ruby, Go) are required for SSG-related tools.
  • ⚠️Requires read/write file system permissions for specified project and documentation paths.
Verified SafeView Analysis
Interacts heavily with the filesystem and executes child processes for Static Site Generator (SSG) builds and Git operations. Uses external LLM APIs and relies on environment variables for API keys. Incorporates `permission-checker.ts` and explicit security policy. Critical to run in a sandboxed/isolated environment as an MCP agent to mitigate risks of arbitrary code execution.
Updated: 2026-01-17GitHub→
37
7
Medium Cost
Technickel-Dev icon

baseline-mcp

by Technickel-Dev

Sec9

Provides an MCP server to query and analyze baseline web features, browser compatibility, and web standards data for developers and AI assistants.

Setup Requirements

  • ⚠️Requires Node.js and npm for local development and execution.
  • ⚠️Requires a TypeScript compilation step (`npm run build`) before running locally.
Verified SafeView Analysis
The server processes JSON-RPC requests and uses internal, trusted data sources from the `web-features` npm package and local JSON files. It relies on the `@modelcontextprotocol/sdk` for transport and request handling. No direct `eval` of user input, unvalidated external network calls, or hardcoded sensitive secrets are evident in the server's code. Tools designed for file analysis, such as `list_features_in_file` and `get_min_browser_support_for_file`, expect `fileContent` (the content of the file) as input, not `filePath`, meaning the server does not directly access the filesystem based on user-provided paths. The prompts for LLMs (`find-features-in-file`, `min-browser-support-report`) suggest operations involving file paths, but the actual server tools that implement these operations require the file content to be provided by the client, preventing server-side arbitrary file access.
Updated: 2026-01-18GitHub→
37
7
Medium Cost
lynnlangit icon
Sec7

Deep learning-based cell segmentation and classification in microscopy images for quantitative phenotyping and visualization.

Setup Requirements

  • ⚠️Requires Python 3.11+.
  • ⚠️For real analysis (i.e., `DEEPCELL_DRY_RUN=false`), it implicitly requires deep learning model weights which may need network access for auto-download or manual pre-installation.
  • ⚠️The `DEEPCELL_OUTPUT_DIR` environment variable must point to a writable directory for output visualizations.
Verified SafeView Analysis
The server processes file paths provided as arguments (`image_path`, `segmentation_mask_path`) to its tool functions. While the Streamlit UI includes file sanitization, the server's internal tool implementations do not explicitly re-sanitize these paths before file operations (e.g., `PIL.Image.open()`, `fig.savefig()`). This could potentially lead to path traversal vulnerabilities if arbitrary, unsanitized input is passed directly by a compromised LLM or client. The server operates within a designated output directory (`DEEPCELL_OUTPUT_DIR`), which is a good practice. No 'eval', code obfuscation, or hardcoded sensitive secrets were detected.
Updated: 2026-01-19GitHub→
37
11
Low Cost
unit-mesh icon

auto-dev-next

by unit-mesh

Sec10

Identifies this repository as a deprecated version of an automated development project, directing users to an updated location.

Verified SafeView Analysis
Only README.md file provided. No executable source code to analyze for security risks such as 'eval', obfuscation, network risks, or hardcoded secrets.
Updated: 2025-12-02GitHub→
37
7
Medium Cost
Unleash icon

unleash-mcp

by Unleash

Sec9

Manages Unleash feature flags for LLM-powered coding assistants, enabling creation, evaluation, and code wrapping following best practices.

Setup Requirements

  • ⚠️Requires Node.js 18 or higher.
  • ⚠️Requires access to an Unleash instance (hosted or self-hosted).
  • ⚠️Requires an Unleash Personal Access Token (PAT) with appropriate permissions.
Verified SafeView Analysis
The source code does not contain 'eval' or other direct arbitrary code execution vulnerabilities. It relies on environment variables for sensitive data (UNLEASH_PAT), which is good practice. Network communication is via Node.js native 'fetch' to a configurable Unleash API endpoint, with error handling. No obvious malicious patterns or obfuscation were found.
Updated: 2026-01-14GitHub→
37
6
Medium Cost
NiclasOlofsson icon

dbt-core-mcp

by NiclasOlofsson

Sec6

A Model Context Protocol (MCP) server that empowers AI assistants (like Copilot) to interact with and manage dbt projects. It enables natural language control over dbt operations, providing project metadata, lineage, impact analysis, SQL query execution, and intelligent build/test workflows, all while respecting the user's local dbt environment.

Setup Requirements

  • ⚠️Requires Python 3.9+ (or 3.10+ as per pyproject.toml).
  • ⚠️Installation requires `uv` (recommended) or `pipx`.
  • ⚠️The user's dbt project must have dbt Core 1.9.0+ and a compatible dbt adapter installed in its own Python environment.
  • ⚠️Experimental features, such as CTE test generation, require setting the `EXPERIMENTAL_FEATURES` environment variable to 'true'.
Verified SafeView Analysis
The server executes user-provided dbt commands via `dbt.invoke()` within a Python subprocess running a dynamically generated script. While it does not introduce new, direct arbitrary code execution vectors outside of dbt's capabilities, it acts as a command executor for AI agents. The security posture heavily depends on the underlying dbt project's configuration and dependencies. Maliciously crafted dbt inputs (e.g., specific model names, SQL, or Jinja macros) could potentially lead to privilege escalation if dbt itself or its adapters are vulnerable or misconfigured (e.g., by allowing shell command execution within dbt macros). Network requests are made to the Databricks API for warehouse pre-warming if configured, using credentials from the user's `profiles.yml`.
Updated: 2026-01-18GitHub→
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