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offline-mcp

offline-mcp

by gabrielmahia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_recommended_models

Discover open-weight models recommended for East Africa AI use cases. Filter by use case or memory constraints to find suitable models for offline inference.

Instructions

List recommended open-weight models for East Africa AI use cases.

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": "list_recommended_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
use_caseNo
max_ram_gbNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries full burden but only says 'list,' implying a read-only operation. It does not disclose aspects like authentication needs, rate limits, or whether the listing is paginated or cached.

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, front-loaded sentence with no superfluous words, making it highly concise and easy to parse.

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?

Despite having an output schema, the description does not hint at return structure or pagination. It is minimal for a list tool with two parameters and multiple siblings, missing usage cues.

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 does not explain the two parameters ('use_case' and 'max_ram_gb'). The phrase 'for East Africa AI use cases' hints at 'use_case' but adds no practical semantics.

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 verb 'List' and the resource 'recommended open-weight models for East Africa AI use cases,' which is specific and distinguishes it from siblings like 'open_weights_directory' that likely lists all models.

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 explicit guidance on when to use this tool versus alternatives such as 'open_weights_directory' or 'local_deployment_guide.' The description lacks context for appropriate usage scenarios.

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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