Lm vs MCP Aichat
A side-by-side look at the Lm and MCP Aichat MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Lm for conversational ai work as a local process; choose MCP Aichat 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 Lm when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
HOUTINI_LM_ENDPOINT_URL, HOUTINI_LM_API_KEY, HOUTINI_LM_MODEL, HOUTINI_LM_SERIALISE, LM_STUDIO_URL.
Choose MCP Aichat 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
aichat_create_conversation, aichat_create_conversation_v2, aichat_list_models.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 49/100 · Fair | 51/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | HOUTINI_LM_ENDPOINT_URLHOUTINI_LM_API_KEYHOUTINI_LM_MODELHOUTINI_LM_SERIALISELM_STUDIO_URL | ACEDATACLOUD_API_TOKEN |
| Verified owner | Community listing | Community listing |
| GitHub stars | 120 | 0 |
| Directory activity | 0 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | Not listed | 4 |
| Full listing | View Lm | View MCP Aichat |
Tools & capabilities
Lm tools
No tool names declared yet. Check the project README from the listing page.
MCP Aichat tools 4
aichat_create_conversationCreate an AI conversation with any supported modelaichat_create_conversation_v2Create/manage conversations via `/aichat2/conversationsaichat_list_modelsList all available AI modelsaichat_get_usage_guideGet 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.
Lm
{
"mcpServers": {
"lm-2": {
"command": "npx",
"args": [
"-y",
"@houtini/lm"
],
"env": {
"HOUTINI_LM_ENDPOINT_URL": "YOUR_HOUTINI_LM_ENDPOINT_URL_HERE",
"HOUTINI_LM_API_KEY": "YOUR_HOUTINI_LM_API_KEY_HERE",
"HOUTINI_LM_MODEL": "YOUR_HOUTINI_LM_MODEL_HERE",
"HOUTINI_LM_SERIALISE": "YOUR_HOUTINI_LM_SERIALISE_HERE",
"LM_STUDIO_URL": "YOUR_LM_STUDIO_URL_HERE"
}
}
}
}
MCP Aichat
{
"mcpServers": {
"mcp-aichat": {
"command": "uvx",
"args": [
"mcp-aichat"
],
"env": {
"ACEDATACLOUD_API_TOKEN": "YOUR_ACEDATACLOUD_API_TOKEN_HERE"
}
}
}
}
Frequently asked questions
Lm is categorized under Conversational AI and uses a local stdio subprocess. MCP Aichat belongs to Conversational AI and uses a local stdio subprocess. Pick Lm for conversational ai work and MCP Aichat 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.
Lm has no declared pricing — check the project's own site. MCP Aichat 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 lm-2 and mcp-aichat as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Lm has a quality signal of 49/100 (Fair) with 0 views, 0 installs and 120 GitHub stars. MCP Aichat has 51/100 (Good) with 0 views, 0 installs and 0 GitHub stars.