Ollama MCP Server
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| serveB | Start Ollama server |
| createC | Create a model from a Modelfile |
| showC | Show information for a model |
| runC | Run a model |
| pullC | Pull a model from a registry |
| pushC | Push a model to a registry |
| listC | List models |
| cpC | Copy a model |
| rmC | Remove a model |
| chat_completionB | OpenAI-compatible chat completion API |
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 10 tools
Most tools have distinct purposes, such as list, pull, push, rm, and show, which clearly target different operations on models. However, 'run' and 'chat_completion' could be confused, as both involve executing models, though 'chat_completion' is more specific to API interactions. Overall, the descriptions help clarify boundaries, but there is some overlap in execution-related tools.
The naming is mixed with some inconsistencies: most tools use simple verb forms like list, pull, push, rm, run, and serve, which are consistent. However, 'chat_completion' uses snake_case and is more descriptive, while 'cp' and 'create' are shorter forms that deviate slightly. This creates a readable but not fully uniform pattern across all tools.
With 10 tools, the count is well-scoped for managing Ollama models, covering essential operations like listing, creating, pulling, pushing, removing, running, and serving. Each tool serves a clear purpose in the model lifecycle, making the set comprehensive without being overwhelming or too sparse for the domain.
The tool set provides complete coverage for Ollama model management, including CRUD operations (create, list, rm), lifecycle actions (pull, push, run, serve), and informational tools (show, chat_completion). There are no obvious gaps; agents can perform all core workflows from model acquisition to execution and maintenance seamlessly.