LM Studio MCP Connector & Daemon
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LMSTUDIO_INSTANCES | No | List of LM Studio instances as comma-separated URLs or a JSON array. Equivalent to the --instances CLI option. | |
| LMSTUDIO_DAEMON_PORT | No | Port for the HTTP daemon and web GUI (default: 9123). Equivalent to the --port/--daemon-port CLI option. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| lmstudio_chatB | Send a prompt or instruction to the local LM Studio model and get the generated completion. If all local instances are offline, returns a directive instructing the agent to fallback to remote LLMs. |
| lmstudio_list_modelsA | List all AI models currently loaded and available across LM Studio instances. |
| lmstudio_embeddingsB | Generate vector embeddings for input text using the local LM Studio embedding model. |
| lmstudio_healthA | Check the health, status, latency, and loaded models of configured LM Studio instances. |
| lmstudio_pool_statusB | Inspect multi-instance pool metrics, load balancing distribution, and failover status. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a distinct capability: chat completion, model listing, embeddings, instance health, and pool-level metrics. The only mild overlap is lmstudio_health vs lmstudio_pool_status, both of which report on instance state, but the descriptions distinguish per-instance diagnostics from aggregate load-balancing/failover metrics.
All five tools use a consistent snake_case pattern with the same lmstudio_ namespace prefix followed by a clear noun/verb topic. No mixed conventions or vague verbs.
Five tools is well-scoped for a local-model connector: one each for inference, embeddings, model discovery, instance health, and pool observability. No tool appears redundant or filler.
Core workflows (chat, embeddings, model discovery, health/pool monitoring) are covered. However, model lifecycle operations like loading/unloading models or selecting a target instance per request are absent, which could limit agent control over the daemon.