Measured latency (TTFB p50/p95) and uptime rankings of AI inference API providers by region, from llmlatency.dev.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | eu-hetzner, us-central, ap-tokyo or sa-east; omit for all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden and explicitly states the data is measured and sourced, implying a read-only operation. It does not mention side effects, which is fine for a data retrieval tool.
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?
The description is a single, clear sentence that conveys all necessary information without waste.
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?
Given the tool's simplicity (one optional parameter, no output schema), the description fully explains what the tool returns and the data source, making it complete.
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 100% and the single parameter 'region' is well described in the schema. The description does not add additional parameter semantics, so baseline 3 is appropriate.
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 clearly states the tool provides measured latency (TTFB p50/p95) and uptime rankings for AI inference API providers, sourced from llmlatency.dev, with optional region filtering. This is specific and informative.
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 description implies usage for retrieving latency and uptime data, but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The two tools target entirely distinct concerns: one measures API latency/uptime and the other tracks model deprecations. There is no overlap or plausible selection confusion between them.
Both tools follow the same get_ + descriptive noun phrase pattern. The naming style is uniform, readable, and predictable.
Two tools feels thin for a server positioned as a tracker, but each tool covers a meaningful, non-overlapping data domain. It is at the borderline rather than clearly over-scoped or under-scoped.
The server covers the two main query needs implied by its name: latency/uptime data and deprecation timelines. Minor gaps remain, such as per-model latency detail or provider-specific filtering, but agents can likely work around them.
