Summarize Market History

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

Annotations already establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe-read profile is covered structurally. The description adds value beyond annotations by disclosing the 400-bucket cap and the compact aggregate nature of the response, which informs the agent about output limits and behavior. No contradiction with annotations.

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 with zero waste. The core function is front-loaded in the first sentence, and the second sentence delivers the usage guidance and the key constraint (400-bucket cap) efficiently. 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?

For a nested-request tool with an output schema and full annotations, the description covers what it returns, the time semantics, why to use it, and its output limit. The inclusive/exclusive start and end interpretation rules live in the schema descriptions, so nothing critical is missing for an agent to invoke it 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?

Schema description coverage is 100%, with all parameters (end, start, bucket, include_load) already documented with types and defaults. The description lightly reinforces the bucket enum ('hourly, daily, weekly, or monthly') and the include_load flag ('optional Alberta Internal Load statistics'), but the schema does the heavy lifting, so a baseline 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 states a specific verb ('Returns'), specific resources (Pool Price statistics, Alberta Internal Load statistics), and the time semantics ([start, end)). It clearly distinguishes this aggregation tool from the raw-series sibling tools like get_pool_prices, get_load, and get_system_marginal_prices by emphasizing it returns compact, summarized statistics.

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 instructs when to use this tool: 'Use this before requesting raw series for long periods.' This gives clear context for selecting the tool, though it doesn't name specific sibling alternatives by name. The 400-bucket cap further clarifies the boundary of when this tool is appropriate versus raw series tools.

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