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

Annotations already declare readOnlyHint=true and idempotentHint=true, so no contradiction. The description adds valuable context on the data source (GNERC), the unit (tetri per kWh), the time range (2015-2026), and the precise meaning of each series. This exceeds the baseline and helps the agent understand what the returned values represent.

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?

A single, dense sentence contains all essential information: source, region, time range, unit, and the three series names. There is no filler, no repetition of the schema, and the most important distinguishing content is front-loaded. 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?

Given the tool is a simple read-only query with two parameters, a clear schema, and no output schema, the description is nearly complete. It could add what the return format looks like or whether year is optional, but the schema already covers year optionality. No major context gap exists.

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% parameter coverage, giving descriptions for both 'year' and 'series', including enum meanings. The tool description reinforces the series definitions concisely without repeating the schema. Since the schema does the heavy lifting, the description's incremental semantic value is modest but sufficient.

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 returns monthly Georgian electricity prices from GNERC, covering 2015–2026 in tetri per kWh, and enumerates the three specific price series. It uses a specific verb+resource, is complete, and distinguishes itself from sibling tools like gnerc_station_generation by focusing on prices rather than generation.

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 implicitly conveys when to use this tool: when querying regulatory electricity price series for Georgia. It explains the three series clearly, which helps the agent select the right series. It does not explicitly mention alternatives or exclusion cases, but the tool's niche is distinct among siblings and the series explanations effectively guide use.

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