".ENV" matching MCP servers:

An MCP server that enables AI agents to securely add secrets to .env files by prompting the user to input the value through a local masked dialog, ensuring the agent never sees the secret.

Scans projects for hardcoded secrets, unprotected .env files, and console.log leaks to prevent credential exposure.

Enables an MCP-capable desktop client to inspect canvas state and action schemas, execute design edits and submissions, and read rewards from a deterministic design-canvas environment over stdio.

Compares a project's .env file against its .env.example template to surface missing, extra, and empty keys, flag empty required variables, and produce paste-safe key inventories for tickets or sharing. Runs entirely on local files with every value redacted, so secrets are never echoed or uploaded.

A local MCP server that provides a safe, structured way to manage Python environments inside a project workspace, including virtual environment creation, restricted pip operations, and dependency freezing.

Automates setup of local development environments for Python, Node.js, Flutter, Android, and more on macOS and Linux. Can be used as a standalone CLI or as an MCP server for AI assistant integration.

Secure environment variable and configuration management MCP server with Claude Code integration for storing, managing, and retrieving environment variables.

Provides a restricted Docker-based sandbox for LLM agents, enabling file operations, command execution, and local Git within an isolated runtime.

A secure wrapper that launches other MCP servers, replacing environment secrets with placeholders and eliciting them via an encrypted browser form, never exposing secrets to the model or client.

Read-only MCP server that scans Docker Compose, Cloudflare Tunnel, and .env files for security risks in self-hosted homelabs.

Provides environment and configuration tools for AI agents, enabling parsing and generation of .env files, port checking, system information inspection, and dependency verification.

An MCP (Model Context Protocol) server that inspects your local development environment and reports missing dependencies, version mismatches, and misconfigured environment variables to any MCP-compatible AI assistant. Works with Claude Desktop, Cursor, Gemini CLI, and any other client that supports stdio-transport MCP servers.

A unified developer toolkit for AI-assisted workflows. Task timing, doc drift detection, env validation, secret scanning, port conflict resolution, AI context generation, and license auditing — one MCP server, one install.

Enables LLMs to retrieve verified macronutrient data (calories, protein, carbs, fat) for real ingredients and quantities from USDA FoodData Central, with structured caveats for uncertain matches.

Enables programmatic access to EndNote .enl libraries, allowing users to list, search, and extract full text from bibliographic references and their attached PDFs through MCP tools.

A dynamic MCP proxy that allows switching environment variables on-the-fly, enabling context switching for multiple AWS accounts or Kubernetes clusters without restarting the client.

Provides tools for creating per-project sprite consistency rules, verifying sprite frames with numeric checks, and deterministically repainting sprite regions to preserve shading and silhouettes.

An MCP server that enables LLMs to search, fetch, and act on Google Workspace (Drive, Gmail, Docs, Sheets, etc.) with rich, one-call results and file deposits to disk, reducing context usage.

Local ledger for development ports: leases a block of ports per project slot (one per working copy), renders env files, and resolves service names to URLs. No daemon, no listener.

Automates ENVI Classic remote sensing experiments on Windows by executing steps, capturing authentic screenshots, and generating formatted Word/PDF lab reports.