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gabrielmahia

offline-mcp

by gabrielmahia

local_deployment_guide

Learn how to set up local AI inference on modest hardware using Ollama, designed for offline operation in low-connectivity regions like Kenya and East Africa.

Instructions

Guide to deploying local AI inference on modest hardware in Kenya/East Africa.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
device_typeNolaptop

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are present, so the description must disclose behavioral traits. It only states it is a guide, which suggests a read-only operation, but does not confirm whether it accesses external resources, requires internet, or has side effects.

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

Conciseness4/5

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

The description is a single, well-structured sentence that is concise and easy to parse. No redundancy exists, but it could be expanded slightly to include parameter details without losing brevity.

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

Completeness2/5

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

Given the simplicity (one optional parameter, output schema exists), the description is incomplete. It lacks context on how the guide is delivered (e.g., text, steps), what the output schema contains, and any usage hints. The presence of an output schema partially mitigates the need to describe return values, but the description remains insufficient.

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

Parameters1/5

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

The input schema has a single optional parameter (`device_type`), but the description provides no explanation of its meaning, allowed values, or how it affects the output. With 0% schema coverage, the description fails to compensate, leaving the agent without guidance.

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

Purpose4/5

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

The description clearly states the subject matter (deploying local AI inference on modest hardware) and geographic scope (Kenya/East Africa), distinguishing it from sibling tools like `run_local_inference` or `check_ollama_status`. However, it lacks a verb (e.g., 'provides a guide' or 'returns deployment steps') so the action is implied rather than explicit.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus siblings such as `list_recommended_models` or `degraded_mode_guide`. The description does not mention prerequisites, alternative tools, or scenarios where the guide is applicable.

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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