get_started

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

With no annotations, the description carries the full burden, and it does disclose the return payload in detail plus an interaction rule ('Reply to the user in their own language'). It stops short of stating there are no side effects, though 'Returns' makes the read-only nature clear by implication.

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?

Two sentences, no filler, and the most important instruction ('START HERE. Call this first...') is front-loaded before the content enumeration. Every clause earns its place.

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 no output schema, the description compensates by listing the five categories of content the tool returns, so an agent knows what to expect without a schema. Complete for a zero-parameter onboarding tool.

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?

The tool takes zero parameters, so the baseline is 4 per the rubric. There is nothing for the description to disambiguate, and it correctly does not invent parameter guidance.

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 opens with an imperative 'START HERE' and then enumerates exactly what the tool returns: a plain-language introduction, example questions, a tool-to-question mapping, a glossary, and caveats. This is unmistakably distinct from data-retrieval siblings like get_detections_in_bbox or count_detections.

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?

It gives explicit trigger conditions — 'when the user is new, asks what this server can do, or asks a vague question about DeepPVMapper' — which is precisely the routing decision an agent needs against 16 specialized sibling tools. When-not is implied by the specificity of those triggers.

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