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offline-mcp

offline-mcp

by gabrielmahia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

open_weights_directory

Browse open-weight AI models curated for East Africa civic applications, ready for offline local inference.

Instructions

Directory of open-weight AI models suitable for East Africa civic 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": "open_weights_directory"
  }
}

Input Schema

NameRequiredDescriptionDefault
use_caseNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

C2.2/5.0
Behavior2/5

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

No annotations provided, and description does not disclose behavioral traits such as side effects, authentication needs, or whether it performs a read-only operation. The description is too brief to inform the agent about tool behavior.

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

Conciseness3/5

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

The description is concise at one sentence, but it sacrifices necessary detail for brevity. Every word earns its place, but the content is insufficient.

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 low complexity (1 optional param, no annotations, output schema exists but not described), the description fails to provide a complete picture. It lacks return format overview, usage constraints, or how the output schema complements the directory listing.

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?

Schema coverage is 0%, and description does not explain the single parameter 'use_case' (type, acceptable values, or effect on results). The description adds no meaning beyond the schema, which itself is minimal.

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

Purpose3/5

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

Description is a noun phrase 'Directory...' rather than a verb phrase indicating an action. It states the domain (open-weight AI models for East Africa civic use) but doesn't specify what the tool does (e.g., list, search, or retrieve). Compared to sibling 'list_recommended_models', the purpose is ambiguous.

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 on when to use this tool versus alternatives like 'list_recommended_models' or 'check_ollama_status'. Lacks any context about preferred use cases or exclusions.

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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