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Glama
Obrais-cloud

ollama-fleet-mcp

by Obrais-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_modelsA

List installed Ollama models per fleet host.

Args:
    host: optional host name (e.g. "corsair"). If omitted, lists all fleet hosts.
generateA

Send a prompt to a specific model on a specific fleet host and return its response.

Args:
    host: fleet host name (e.g. "mac-studio", "corsair", "alien18").
    model: model name as reported by list_models (e.g. "qwen3:32b").
    prompt: the prompt text to send.
    timeout_sec: max seconds to wait for a response (default 120).
fleet_healthA

Check reachability and model count for every host in the fleet.

compare_modelsA

Send the same prompt to multiple host/model pairs in parallel and return responses side by side.

Args:
    prompt: the prompt text to send to every target.
    targets: list of {"host": ..., "model": ...} dicts, e.g.
        [{"host": "corsair", "model": "qwen3:32b"}, {"host": "mac-studio", "model": "qwen3.6:27b-coding-mxfp8"}]
smart_generateA

Generate a response for model, routed to whichever fleet host already has it loaded in memory (skips cold-load latency). Falls back to any host that has it installed but not loaded, and errors if no host has the model at all.

Args:
    model: model name (e.g. "qwen3:32b").
    prompt: the prompt text to send.
    timeout_sec: max seconds to wait for the generate call (default 120).
pull_modelA

Pull (download/verify) a model onto a specific fleet host.

Args:
    host: fleet host name.
    model: model name to pull (e.g. "qwen3:32b").
    timeout_sec: max seconds to wait — large models can take a while (default 900).

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 6 tools

Disambiguation4/5

Most tools have clearly distinct purposes: list_models for inventory, fleet_health for status, pull_model for downloading, compare_models for side-by-side comparison. However, generate and smart_generate both perform text generation and differ only in routing logic (explicit host vs auto-route), which could cause misselection.

Naming Consistency3/5

Naming conventions are mixed: list_models and pull_model use verb_noun, generate is a bare verb, fleet_health is a noun phrase, smart_generate is adjective_verb. The names are readable but do not follow a single consistent pattern.

Tool Count5/5

Six tools is a well-scoped set for an Ollama fleet management server, covering the essential operations of listing models, generating responses, checking health, comparing models, and pulling models without unnecessary bloat.

Completeness4/5

The tool surface covers the core workflows: inventory (list_models), generation (generate, smart_generate), comparison (compare_models), health monitoring (fleet_health), and model acquisition (pull_model). Missing operations like delete_model or model detail inspection are minor gaps that agents can work around.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources