offline-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": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_ollama_statusA | Check if Ollama is running locally and list available models. |
| run_local_inferenceC | Run a prompt through a local Ollama model. |
| list_recommended_modelsB | List recommended open-weight models for East Africa AI use cases. |
| degraded_mode_guideA | Guide for operating AI systems when cloud connectivity fails. |
| open_weights_directoryC | Directory of open-weight AI models suitable for East Africa civic use cases. |
| local_deployment_guideC | Guide to deploying local AI inference on modest hardware in Kenya/East Africa. |
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 6 tools
Each tool has a clearly distinct purpose: status checking, running inference, listing recommended models, providing a directory, and offering guides. No overlap in functionality.
All names use snake_case and follow a verb_noun or adjective_noun pattern, with minor variation between imperative verbs and descriptive nouns. Consistent enough for clear identification.
Six tools is well-scoped for an offline AI inference and guidance server, covering core operations and supplementary resources without bloat.
The tool set covers the full expected workflow: status check, inference, model recommendations, directory, and guides for deployment and degraded mode. No obvious gaps.