llm-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": true
} |
| resources | {
"subscribe": false,
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_modelsA | The registered local models, which one is the default, and whether each is running (with its start command if not). |
| askA | Send one prompt to a local model and return its answer text. system: optional system prompt. model: a name from list_models; omit for the default. think: let the model reason before answering. Better on hard problems, but often several times slower; the reasoning itself is not returned. max_tokens: upper bound on generated tokens, reasoning included. finish_reason "length" means the answer was cut off. |
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 2 tools
The two tools occupy entirely disjoint roles: list_models is a read-only discovery/inventory call, while ask performs inference. An agent can unambiguously pick between them, and all the optional knobs (system, model, think, max_tokens) are correctly folded into ask as parameters rather than separate tools.
list_models follows a clean verb_noun pattern, but ask is a bare verb with no noun, so the set is not fully uniform (ask_model or prompt_model would have matched). With only two tools this is a minor, still-readable deviation.
Two tools is thin for a server whose apparent scope is managing and using local models. The surface is defensible because most capability is expressed as ask parameters, but it sits at the borderline of feeling under-specified.
Discover models and send a single prompt covers the core inference loop, but there is no model lifecycle control (starting/stopping a model, despite list_models reporting start commands and running state), no multi-turn chat, no streaming, and no embeddings. These are notable gaps an agent will hit quickly.