AI API latency leaderboard by region

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

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), the description defines what is actually measured (edge TTFB = DNS+TCP+TLS+first byte), the probe cadence (every 5 minutes, direct connections, no gateway), the response contents (region label, dataset timestamp, ranked rows), and the cost trade-off of omitting region (larger response). This is unusually rich behavioral context.

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?

Front-loaded with purpose and scope, and every sentence carries distinct information (use cases, sibling routing, metric definition, return shape, region omission). It is on the long side for a two-parameter tool, but no sentence is redundant.

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?

With an output schema present, return values need not be explained, yet the description still summarizes them helpfully. Combined with the metric definition, region omission behavior, and sibling routing, an agent has everything needed to call this correctly.

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%, so baseline is 3; the description earns a notch above by adding the consequence of omitting region ('all four regions at once (larger response)'), which the schema omits, and by restating the human-readable region mapping. The 'limit' parameter is left to the schema, which is acceptable given full coverage.

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?

States a specific action (rank hosted AI inference API providers), the exact metric (measured network latency inside a probe region), the time window (last 24 hours), and the population size (46 tracked, with named examples). It explicitly distinguishes itself from sibling get_provider_latency, so an agent can route without opening either schema.

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?

Gives concrete triggering questions ('which provider responds fastest from Europe/US/Tokyo/Sao Paulo') and the decision rule to pick an endpoint near users. It names the alternative (get_provider_latency) and the condition that selects it (following one named provider across regions), and states exclusions (not tokens per second or model quality).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.