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Edit

  • Env var "GITHUB_TOKEN" for server "github" looks like a secret but is empty.

Output

Generated config

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-github"
      ],
      "env": {
        "GITHUB_TOKEN": ""
      }
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/home/user/projects"
      ]
    }
  }
}

Health checks

  • [FAIL][github] 1 required environment variable missing: GITHUB_TOKENSet the GITHUB_TOKEN environment variable in the next step
  • [WARN][github] Requires Node.js 18+ and npmDownload from nodejs.org or install via your package manager
  • [FAIL][filesystem] 1 required environment variable missing: GITHUB_TOKENSet the GITHUB_TOKEN environment variable in the next step
  • [WARN][filesystem] Requires Node.js 18+ and npmDownload from nodejs.org or install via your package manager

Install steps

Config file: ~/Library/Application Support/Claude/claude_desktop_config.json

  1. 1. Copy the config— Copy the JSON below for Claude Desktop.
  2. 2. Locate the config file— Open ~/Library/Application Support/Claude/claude_desktop_config.json in any text editor. If the file doesn't exist, create it.
  3. 3. Paste the config— Place the JSON inside the `mcpServers` object. Merge with any existing servers if present.
  4. 4. Restart your AI app— Close and reopen your AI application. The MCP server will start automatically.

The Model Context Protocol (MCP) is an open standard for connecting AI applications with external tools and data sources. This editor lets you work directly with multi-server MCP configs — validate them, convert between clients, and export a clean .env.example — entirely in your browser.

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open standard that defines how AI applications communicate with external tools and services. Think of it as a "USB-C port for AI" — instead of every AI app building custom integrations for every tool, MCP provides a single, standardized protocol.

This editor is the power-user companion to the guided MCP Config Generator. Where the generator walks you through a wizard, the editor lets you work directly with the raw mcpServers JSON, convert the same config between client formats, and validate it before dropping it into your AI app.

Edit, Validate, Convert

JSON-First Editing

The editor is built around the MCP config JSON. You can:

  • Paste an existing claude_desktop_config.json, opencode.json, .cursor/mcp.json, or VS Code settings.json
  • Switch to the form view to add, remove, and edit servers without hand-writing JSON
  • Watch validation errors and warnings update as you type

Multi-Server, Multi-Client

A single config can contain many servers. The editor keeps them all in one place and converts the whole document for whichever client you target:

Client Config File Root Key
Claude Desktop claude_desktop_config.json mcpServers
OpenCode opencode.json mcpServers
Cursor .cursor/mcp.json mcpServers
VS Code settings.json mcp.servers
Other mcp-config.json mcpServers

Switching the target client rewrites the output (including the VS Code mcp.servers key) without touching your source config.

Validation & Health Checks

The editor reuses the same validation engine as the generator:

  • JSON syntax checking
  • Per-server checks (command for stdio, url for sse/http)
  • Empty-secret warnings for env vars that look sensitive
  • Template-matched health checks (missing tokens, Node/uv requirements, OS notes)

Environment Variables & .env.example

Secrets never belong in committed config. The editor collects every env key across all servers and generates a .env.example you can copy or download, so you can keep real values out of version control.

Shareable Configs

Copy a share link to send a config to a teammate. The entire config is compressed into the URL hash with lz-string — nothing is uploaded to any server.

Security Best Practices

  1. Use dedicated tokens: Create API tokens with minimal required scopes instead of using personal access tokens
  2. Read-only when possible: For databases, create a read-only user if you only need query access
  3. Restrict filesystem access: Only grant access to directories the AI actually needs to read
  4. Review permissions: Check the health checks before adding any MCP server
  5. Revoke unused access: Remove MCP server configs you no longer use
  6. Keep tokens secret: MCP configs contain tokens — use .env.example and don't commit real values

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