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 read-only nature by saying it 'returns' counts. It also clarifies that the result is 'eligibility, not a hiring guarantee,' which is a useful behavioral caveat beyond the basic schema.

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 two sentences with zero filler. It front-loads the main purpose, states the exact output, and adds one important caveat. Every sentence 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?

This is a simple one-parameter tool with no output schema and no annotations, so the description needs to cover the return meaning. It does: it explains that the result is counts of accepting and location-independent jobs, plus the eligibility caveat. It could mention edge cases like invalid country codes, but that is not critical for a tool this simple.

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 country parameter is already fully described with ISO-2 format and examples. The description adds only the context that the country is the location being checked, not new semantic meaning beyond the schema.

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 names a specific verb and resource: it answers the eligibility question for a country and returns concrete counts. The output focus on aggregate counts clearly separates it from siblings like search_jobs or get_job, even without naming them.

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 clear context by framing the exact user question it answers: 'can I work remotely from <country>?'. It does not explicitly mention alternatives or when not to use the tool, but the purpose is specific enough that an agent can infer appropriate usage.

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