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

With no annotations, the description carries the burden and discloses that sources legitimately disagree due to vintages, exchange-rate conventions, and revision cycles. It explicitly says the tool surfaces these disagreements rather than hiding them, which is valuable 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?

The description is concise, front-loaded with the core purpose, and each sentence earns its place—purpose, return structure, and scope condition. No unnecessary fluff.

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?

It fully explains what is returned (values per source with year), the scope (per country), and the limitation (multi-source mapping concepts). With no output schema and only 2 parameters, the description is sufficiently complete for an agent to invoke the tool 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?

The input schema already provides 100% coverage with descriptions and examples for both parameters. The description adds no additional parameter-level detail beyond the schema, so the baseline of 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 identifies the tool as doing cross-source reconciliation, returning IMF, World Bank, and UN values side-by-side per country. It distinguishes itself from siblings like compare_countries or get_historical_data by focusing on comparing data sources rather than countries or single series.

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 states the tool works for concepts with a multi-source mapping, which implies when to use it. It does not explicitly name alternatives or state when not to use it, but the context of cross-source comparison is clear.

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