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agy-ollama-mcp

agy-ollama-mcp

by leifdavisson
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

local_prewarm_model

Pre-load a local Ollama model into memory to eliminate cold-start latency on subsequent requests.

Instructions

Pre-warm a local model into memory/VRAM with keep_alive=-1 so subsequent calls have zero cold-start delay.

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": "local_prewarm_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It reveals that the model is loaded into memory/VRAM, persistence is set via keep_alive=-1, and the intended effect is eliminating cold-start delay. It does not mention resource consumption or response behavior, but the core behavioral profile is transparent.

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?

A single dense sentence front-loads the action and mechanism with no filler. Every clause contributes meaningful information.

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?

For a simple single-parameter tool with an output schema present, the description is mostly complete. The main gap is not explicitly directing the agent to provide the model name or consult local_list_models for available models.

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

Parameters2/5

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

Schema description coverage is 0% and the description never references the 'model' parameter. The parameter name is inferable from the tool name, but no guidance is given on valid model identifiers or the need to supply an already-available local model.

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 uses a specific verb ('Pre-warm') naming the exact resource ('local model') and the mechanism ('keep_alive=-1'). It clearly communicates the tool's function and distinguishes it from sibling generation/extraction tools.

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

Usage Guidelines4/5

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

The description implies when to use it: before subsequent calls needing zero cold-start delay. It does not explicitly name alternatives or exclusion cases, but the intended context is clear enough for an agent to decide.

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

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