MCP Glm vs MCP Rubber Duck
A side-by-side look at the MCP Glm 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 MCP Glm for conversational ai work as a local process; 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 MCP Glm when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
ACEDATACLOUD_API_TOKEN. - You want tools like
glm_chat_completions, glm_list_models, glm_get_usage_guide.
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 | 51/100 · Good | 68/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | ACEDATACLOUD_API_TOKEN | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 178 |
| Directory activity | 0 views 0 installs 0 upvotes | 4 views 0 installs 0 upvotes |
| Tools | 3 | 15 |
| Full listing | View MCP Glm | View MCP Rubber Duck |
Tools & capabilities
MCP Glm tools 3
glm_chat_completionsRun a GLM chat completion callglm_list_modelsList available GLM modelsglm_get_usage_guideGet the API usage guide
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.
MCP Glm
{
"mcpServers": {
"mcp-glm": {
"command": "uvx",
"args": [
"mcp-glm"
],
"env": {
"ACEDATACLOUD_API_TOKEN": "YOUR_ACEDATACLOUD_API_TOKEN_HERE"
}
}
}
}
MCP Rubber Duck
{
"mcpServers": {
"nesquikm-mcp-rubber-duck": {
"command": "npx",
"args": [
"-y",
"mcp-rubber-duck"
]
}
}
}
Frequently asked questions
MCP Glm is categorized under Conversational AI and uses a local stdio subprocess. MCP Rubber Duck belongs to Conversational AI and uses a local stdio subprocess. Pick MCP Glm 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.
MCP Glm 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 mcp-glm and nesquikm-mcp-rubber-duck as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, MCP Glm has a quality signal of 51/100 (Good) with 0 views, 0 installs and 0 GitHub stars. MCP Rubber Duck has 68/100 (Great) with 4 views, 0 installs and 178 GitHub stars.