Carbon Arc vs MCP Glm
A side-by-side look at the Carbon Arc and MCP Glm MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Carbon Arc for conversational ai work over a hosted endpoint; choose MCP Glm 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 Carbon Arc when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
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.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 40/100 · Fair | 51/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | ACEDATACLOUD_API_TOKEN |
| Verified owner | Community listing | Community listing |
| GitHub stars | 33 | 0 |
| Directory activity | 0 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | Not listed | 3 |
| Full listing | View Carbon Arc | View MCP Glm |
Tools & capabilities
Carbon Arc tools
No tool names declared yet. Check the project README from the listing page.
MCP Glm tools 3
glm_chat_completionsRun a GLM chat completion callglm_list_modelsList available GLM modelsglm_get_usage_guideGet the API usage guide
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.
Carbon Arc
{
"mcpServers": {
"carbon-arc": {
"url": "https://mcp.carbonarc.ai"
}
}
}
MCP Glm
{
"mcpServers": {
"mcp-glm": {
"command": "uvx",
"args": [
"mcp-glm"
],
"env": {
"ACEDATACLOUD_API_TOKEN": "YOUR_ACEDATACLOUD_API_TOKEN_HERE"
}
}
}
}
Frequently asked questions
Carbon Arc is categorized under Conversational AI and uses a remote HTTP/SSE transport. MCP Glm belongs to Conversational AI and uses a local stdio subprocess. Pick Carbon Arc for conversational ai work and MCP Glm 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.
Carbon Arc has no declared pricing — check the project's own site. MCP Glm has no declared pricing — check the project's own site. 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 carbon-arc and mcp-glm as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Carbon Arc has a quality signal of 40/100 (Fair) with 0 views, 0 installs and 33 GitHub stars. MCP Glm has 51/100 (Good) with 0 views, 0 installs and 0 GitHub stars.