Ollama MCP Wrapper
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_local_modelsA | List all local models currently downloaded in the Ollama instance. |
| run_model_completionC | Run text generation completion on a specific local model. |
| generate_with_toolsB | Execute chat generation with a list of tools exposed to the model. |
| list_running_modelsA | List all models currently loaded and running in memory (RAM/VRAM). |
| stop_modelB | Stop and unload a specific model from memory (RAM/VRAM). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| agent_bootstrap | System prompt blueprint for setting up an autonomous reasoning agent. |
| code_assistant | Configure Ollama model specifically for robust programming assistance. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_active_model_status | Get the status of the currently loaded active model. |
TDQS
Scored across 5 tools
The tools are mostly distinct, but list_local_models and list_running_models are similar in purpose (listing models in different states), and run_model_completion and generate_with_tools both handle generation. Descriptions clarify the differences, but the boundaries could be sharper.
All tool names follow a consistent verb_noun snake_case pattern (list_*, run_*, generate_*, stop_*), making the naming predictable and easy to navigate.
With only five tools, the set is well-scoped and focused on core local model interaction, avoiding unnecessary bloat while covering the essential immediate operations.
The set covers listing, running, and stopping models, but lacks essential model lifecycle management like pulling or deleting models, which are common in Ollama workflows. This leaves notable gaps for a wrapper of this kind.