Agenttune vs MCP Rubber Duck
A side-by-side look at the Agenttune and MCP Rubber Duck MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Agenttune for conversational ai work over a hosted endpoint; choose MCP Rubber Duck for conversational ai as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Agenttune when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
get_free_tools, list_tunings, get_tuning.
Choose MCP Rubber Duck when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You want tools like
ask_duck, chat_with_duck, clear_conversations.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 57/100 · Good | 68/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 180 |
| Directory activity | 0 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | 6 | 15 |
| Full listing | View Agenttune | View MCP Rubber Duck |
Tools & capabilities
Agenttune tools 6
get_free_toolsReturn the free browser tools catalog, destination registry, and local JavaScript API documentation. No personal data is accepted. Download the functions to process instructions locally; this call does not run models or install preferences.list_tuningsCatalog of all 43 AgentTune personality tuning files (slug, code, name, one-line blurb), optionally filtered by system. Use it to resolve a user's personality type to the right slug before calling get_tuning.get_tuningFetch one tuning file as Markdown with YAML front-matter. The front-matter links to the shared platform registry and installation protocol. structuredContent.body is the preference text; metadata stays separate. Confirmation of saved text is distinct from evaluation of behavior. MIT licensed.get_test_specFetch a complete questionnaire specification for mbti (32 items), enneagram (36), disc (16), attachment (36) or big-five (50). All five are available, with exact instrument/version IDs, response anchors and strict scoring instructions. The first four preserve the legacy AgentTune adaptations; instrument-specific source terms apply. Administer only when requested, keep responses local, preserve ties, and let users review any suggested communication preferences before installation.list_resourcesSearch the versioned catalog of setup guides, templates, protocols and research. Returns canonical URLs, Markdown URLs, revisions and evidence status.get_resourceRetrieve one page-specific Markdown resource by the exact ID from list_resources; includes provenance and evidence status.
MCP Rubber Duck tools 15
ask_duckAsk a single question to a specific LLM providerchat_with_duckConversation with context maintained across messagesclear_conversationsClear all conversation historylist_ducksList configured providers and health statuslist_modelsList available models for providerscompare_ducksAsk the same question to multiple providers simultaneouslyduck_councilGet responses from all configured ducksget_usage_statsUsage statistics and estimated costsduck_voteMulti-duck voting with reasoning and confidenceduck_judgeHave one duck evaluate and rank others' responsesduck_iterateIteratively refine a response between two ducksduck_debateStructured multi-round debate between ducks- +3 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Agenttune
{
"mcpServers": {
"agenttune": {
"url": "https://agent-tune.com/mcp"
}
}
}
MCP Rubber Duck
{
"mcpServers": {
"nesquikm-mcp-rubber-duck": {
"command": "npx",
"args": [
"-y",
"mcp-rubber-duck"
]
}
}
}
Frequently asked questions
Agenttune is categorized under Conversational AI and uses a remote HTTP/SSE transport. MCP Rubber Duck belongs to Conversational AI and uses a local stdio subprocess. Pick Agenttune for conversational ai work and MCP Rubber Duck for conversational ai.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Agenttune has no declared pricing — check the project's own site. MCP Rubber Duck is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add agenttune and nesquikm-mcp-rubber-duck as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Agenttune has a quality signal of 57/100 (Good) with 0 views, 0 installs and 0 GitHub stars. MCP Rubber Duck has 68/100 (Great) with 5 views, 0 installs and 180 GitHub stars.