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NightTrek

Ollama MCP Server

by NightTrek
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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    B3/5.0

    Scored across 10 tools

    Disambiguation4/5

    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.

    Naming Consistency3/5

    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.

    Tool Count5/5

    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.

    Completeness5/5

    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.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources