Setup mcp-rubber-duck

You are setting up mcp-rubber-duck as an MCP server in an AI coding tool.

Reference

• .claude/skills/setup-mcp-server/references/tool-configs.md — per-tool JSON/YAML config templates
• .claude/skills/setup-mcp-server/references/provider-env-vars.md — env var reference
• .claude/skills/setup-mcp-server/references/troubleshooting.md — common issues

Supported tools and config paths

ChatGPT supports MCP via UI only (Developer Mode > Add MCP Server), not a config file.

1. Determine target tool

If $ARGUMENTS specifies a tool name, use it. Otherwise ask the user:

Supported tools: claude-desktop, claude-code, cursor, windsurf, vscode, continue

2. Detect platform

Run uname -s to detect macOS / Linux / Windows (Git Bash). This affects config file paths.

3. Detect install method

Check in order:

1. which mcp-rubber-duck — if found, use mcp-rubber-duck as the command
2. If we're inside the mcp-rubber-duck repo (check for package.json with "name": "mcp-rubber-duck"), offer from-source via node dist/index.js
3. Default to npx -y mcp-rubber-duck

4. Ask about providers

Ask the user which providers to include. Offer these choices:

• OpenAI (OPENAI_API_KEY) — most popular
• Google Gemini (GEMINI_API_KEY)
• Groq (GROQ_API_KEY) — fast inference
• Ollama (local, no key needed)
• CLI agents (Claude CLI, Codex, Gemini CLI, Aider)

Allow multiple selections.

5. Ask about API keys

For each selected HTTP provider that needs a key, ask: provide a real key now or use a placeholder to fill in later?

• If real key: validate it looks reasonable (starts with expected prefix like sk- for OpenAI, gsk_ for Groq)
• If placeholder: use descriptive placeholders like your-openai-api-key-here

6. Load reference templates

Read the config template from .claude/skills/setup-mcp-server/references/tool-configs.md to get the exact JSON/YAML structure for the target tool.

7. Write the configuration

For Claude Code (claude-code)

Use the claude mcp add CLI command — this is the idiomatic way:

claude mcp add --scope user rubber-duck -- mcp-rubber-duck

Then set env vars by editing ~/.claude.json to add the env block, or suggest the user set them in their shell profile.

If the user prefers project-scope, use --scope project (writes to .mcp.json).

For all other tools

1. Determine the config file path (platform-aware) from the reference templates
2. Read the existing config file if it exists
3. If rubber-duck entry already exists, warn the user and ask before overwriting
4. If other MCP servers exist, merge — never overwrite the entire file
5. If the file doesn't exist, create it (and parent directories with mkdir -p)
6. If existing JSON is invalid, warn and offer to backup the broken file before writing
7. Write the final config

Important details

• Always include "MCP_SERVER": "true" in the env block — this is required
• VS Code uses "servers" key, not "mcpServers" — get this right
• Continue uses YAML with mcpServers as an array of objects
• Windsurf & VS Code support ${env:VAR_NAME} syntax for env var interpolation

8. Print verification steps

After writing the config, tell the user:

1. Restart the tool (fully quit and relaunch for desktop apps)
2. Test by using the list_ducks tool with check_health: true
3. Troubleshoot — point them to .claude/skills/setup-mcp-server/references/troubleshooting.md if anything goes wrong

Important rules

• Never overwrite existing MCP server entries for other servers
• Always create parent directories before writing config files
• Use the exact JSON structure from the reference templates
• For env var interpolation tools (VS Code, Windsurf), prefer ${env:VAR} syntax over hardcoded keys
• Include DEFAULT_PROVIDER set to the first selected HTTP provider