Skip to main content
Glama
Sign In
offline-mcp

offline-mcp

by gabrielmahia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

check_ollama_status

Verify whether Ollama is active on your local machine and retrieve a list of available models for offline AI inference.

Instructions

Check if Ollama is running locally and list available models.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "check_ollama_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description describes the tool's behavior as a read-only check and listing operation. No annotations are provided, so the description carries the burden; it is adequate but does not disclose edge cases (e.g., behavior if Ollama is not installed) or output details beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that captures the essential purpose without superfluous information. Every word adds value; no restructuring needed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (no parameters) and the presence of an output schema, the description is largely complete. It covers the tool's main function. A minor gap: it could mention whether errors are thrown if Ollama is not installed, but this is not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and the schema coverage is 100% (vacuously). The description adds no parameter details because none exist, which is acceptable. Baseline score for zero-parameter tools is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Check') and the resource ('if Ollama is running locally and list available models'). It distinguishes this tool from siblings like run_local_inference or list_recommended_models by focusing on status checking rather than computation or recommendations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for verifying Ollama availability and listing models, but does not explicitly state when to use this over alternatives (e.g., before running inference) or when not to use it. Sibling tools provide context but no direct guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools