Verify a model's reachability and API key by sending one small request. Use provider:model format to test any AI model.
Check that a model is reachable and its API key works, with one tiny request.
Args: model: "provider:model", e.g. "openai:gpt-4o" or "ollama:llama3.1:8b".
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses that the check is lightweight ('one tiny request') and validates both reachability and auth, but says nothing about failure behavior, error surfacing, rate limits, or cost beyond the request being tiny.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Purpose is front-loaded in one sentence and the args note is short. The 'Args:' block is mildly boilerplate since it repeats the param name, but the format guidance justifies its presence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need no explanation, and the single parameter is fully specified. The only real omission is failure behavior, which is minor for a low-risk ping utility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the single param is an undescribed string, so the description must compensate. It does so well by giving the required 'provider:model' format plus two concrete examples covering both cloud and local providers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: it checks that a model is reachable and its API key works. This is unambiguous and, given the run/report-oriented siblings, self-evidently a distinct diagnostic utility that needs no differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The stated purpose implies usage (validating a model/provider before launching a run), but there is no explicit when-to-use, when-not, or named alternative. The context is implied rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.