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ai_availability

Whether ChatGPT, Claude, Gemini and Muse officially support a country (ISO 3166-1 alpha-2, e.g. US, HK, CN), from each company's published list.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "country": "<country>"
    },
    "name": "ai_availability"
  }
}

Input Schema

NameRequiredDescriptionDefault
countryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden and does add useful provenance context: results come from 'each company's published list'. However, it does not state the return shape (per-service booleans? statuses?), whether results are cached or live, or any caveats about list staleness. Adequate but incomplete for a zero-annotation tool.

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, well-formed sentence with the core purpose front-loaded and the parameter format tucked into a parenthetical. Every clause earns its place; nothing is redundant.

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 low-complexity, single-required-parameter read tool with no output schema, the description covers purpose, data source, and input format. The main remaining gap is what the response actually looks like (per-company results vs. a summary), which is a minor omission at this complexity level.

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?

Schema description coverage is 0%, so the description must compensate, and it does: it documents the single 'country' parameter's expected format (ISO 3166-1 alpha-2) and gives concrete examples (US, HK, CN). It does not mention validation behavior for invalid codes, but the format guidance is the key missing piece and it is supplied.

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 states a specific resource and scope: whether named AI services (ChatGPT, Claude, Gemini, Muse) officially support a given country, sourced from each company's published list. That is clearly distinguishable from siblings like check_ip or my_exit. It does not explicitly name a sibling, so a 4 rather than a 5.

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?

There is no when-to-use or when-not-to-use guidance and no mention of alternatives. The agent must infer that this is the tool for country-level service availability queries rather than e.g. service_status. No prerequisites or invocation context are given.

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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