ember
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| EMBER_HOST | No | Model server address (host). | 127.0.0.1 |
| EMBER_PORT | No | Model server address (port). | 8765 |
| EMBER_MODEL | No | Model to use: flash (9B) or full (27B). | flash |
| EMBER_DEVICE | No | Device to run the model on: auto, mps, or cpu. | auto |
| EMBER_AUTOSTART | No | Let the MCP server start the model server on demand. | 1 |
| EMBER_MODEL_DIR | No | Run weights from this directory instead of the pinned cache. | |
| EMBER_STATE_DIR | No | Where the pid file and logs live. | Application Support |
| EMBER_MAX_LENGTH | No | Token cap per request; 0 derives it from the model (the model's maximum is 262144). | 0 |
| EMBER_SERVER_URL | No | Where the MCP server sends requests. | http://127.0.0.1:8765 |
| EMBER_START_TIMEOUT | No | Seconds to wait for the model server to start. | 300 |
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| adviseA | Get ember's read on a situation, as calibrated probabilities. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| ember-guide | Full playbook for the advise tool: schemas, thresholds, and recipes. |
TDQS
Scored across 1 tool
With only a single tool in the set, there is no possibility of tool-to-tool confusion; the agent has exactly one entry point. The internal mode parameter (choice/score/noul) is well documented, so even the intra-tool variation is clear.
One tool named 'advise' is trivially consistent — a clean, lowercase verb with no competing conventions to clash against. There is no naming pattern to violate.
A single 'advise' tool with modes is a defensible design for wrapping one decision model, since the modes are variants of the same operation rather than genuinely distinct resources. It is slightly thin, but not a mismatch with the server's stated scope.
The core advisory surface (categorical choice, ordered score, and binary proposition) is covered, but there is no way to report outcomes back for calibration, request explanations, or batch multiple questions. These are notable gaps for a decision-support domain, though agents can still function.