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FernTool Network Tools

ai_service_status

Live status of AI services (ChatGPT/OpenAI, Claude, Gemini API, GitHub Copilot, Perplexity, Cohere) read from their official status pages, cached ~60s. level is up, degraded, down or unknown.

Input Schema

NameRequiredDescriptionDefault
serviceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that data is cached for ~60 seconds and defines the possible level values, which are useful. However, it does not mention return format, error handling, or whether the service list is fixed (though the enum implies it).

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 efficient sentence that front-loads the core functionality (live status) followed by service names and cache detail. Every part earns its place with no redundancy.

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?

Given no output schema, the description adequately explains what the tool returns (status levels) and cache behavior. It could be more complete by specifying return structure or whether the service parameter can be omitted, but it covers the essentials for a simple status-check tool.

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?

The schema shows one optional parameter 'service' with an enum of six values, and the description lists the same services. The description adds that the service names map to the official status pages, but does not explain the default behavior when the parameter is omitted (since it is not required). With 0% schema description coverage, the description partially compensates by naming the services, but a baseline of 4 is appropriate for a single enum parameter.

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 it returns the live status of specific AI services from their official status pages. It specifies the exact services covered and the metric (level), making the purpose unambiguous. It is distinct from all sibling tools, which are unrelated to AI service monitoring.

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

Usage Guidelines3/5

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

The description implies usage for checking current availability, but does not explicitly state when to use this tool versus alternatives or what conditions warrant its use. There are no sibling tools that offer similar functionality, so guidance is less critical, but explicit usage context is absent.

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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