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

Annotations already declare readOnlyHint=true and openWorldHint=false, establishing safety. The description adds meaningful behavioral context: weekly update cadence, data source (EIA), weighting methodology, and the fact that 50 indicates normal. It also discloses the return fields. This goes beyond the annotations and helps the agent understand behavior and output without contradicting structured metadata.

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 and packs essential information efficiently: what the index is, its composition, output fields, when to use, and update frequency. The second sentence is a long list of use cases but is purposeful and not redundant. Slightly less crisp than an ideal split into separate bullet points, but still concise and well-structured.

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?

There is no output schema, so the description carries the burden of explaining return values. It lists score, top_drivers, confidence, and methodology_version. Given the tool's moderate complexity (an index with a formula and use cases), the description is complete enough for an agent to select and invoke it correctly. Minor gaps like exact confidence range or top_drivers structure are not critical.

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 single optional parameter 'days' is fully described in the schema (coverage 100%), including that it returns a history series, up to 5 years, and that history requires Gold tier. The description itself adds no parameter-specific information, so it does not need to compensate; baseline 3 is appropriate given the schema already provides thorough documentation.

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 returning a 0-100 US commercial energy price elevation index, with specific formula components (z-scored commercial electricity 60% + natural gas 40% vs 30-year norms) and output fields (score, top_drivers, confidence, methodology_version). This is a specific verb+resource+scope, and the level of detail distinguishes it from any generic sibling tool.

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 explicitly states when to call: 'Call when the user asks about commercial electricity rates, natural gas costs, energy price spikes, utility cost inflation, or energy regulatory exposure, or when timing fixed-rate electricity/gas contract RFPs, procurement, or hedging decisions.' It provides clear context for use but does not mention when not to use it or name alternative tools, so it misses the full 'when-not' guidance.

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