Rank certified Texas electricity plans for a zip by real twelve-month cost at a monthly kWh average. Returns verified_as_of with every number.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | Yes | Five-digit US zip code | |
| detail | No | summary (default) is the ranking. full attaches the month-by-month receipt to each plan. | |
| monthly_kwh | No | Monthly usage in kWh. Default 1000. Expanded into a Texas seasonal year. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It adds meaningful traits: the cost is 'real twelve-month cost' rather than an estimate, and 'Returns verified_as_of with every number' signals data freshness and traceability beyond what the parameter schema provides.
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 no filler. The primary purpose and the key output guarantee are front-loaded, and every clause adds information.
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 read-only ranking tool with fully documented parameters, the description is mostly complete: it states the selection scope, cost basis, and a return-value guarantee. It would be more complete if it described the response shape or ordering, since no output schema exists, but the current description is sufficient for correct invocation.
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%, so the schema already documents all three parameters. The description adds useful interpretive framing about monthly average usage feeding the twelve-month cost, but it does not significantly expand on the `detail` parameter or otherwise compensate beyond the baseline.
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 uses a specific verb ('Rank') with a clear resource: 'certified Texas electricity plans for a zip.' It also specifies the ranking basis, 'real twelve-month cost at a monthly kWh average,' which makes the tool's purpose immediately distinguishable from siblings like check_territory or explain_plan.
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 clearly contextualizes when to use the tool: when a ranked comparison of Texas electricity plans for a given zip is needed. It does not explicitly name sibling alternatives or state when not to use it, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: check_territory verifies market eligibility, compare_plans and compare_this_house rank plans but differ by input (zip vs. address), and explain_plan provides detailed receipt for a single plan. The descriptions clarify when to use compare_this_house over compare_plans, eliminating ambiguity.
All tool names follow a consistent verb_noun pattern using snake_case: check_territory, compare_plans, compare_this_house, explain_plan. The two compare tools share the same verb, and all names are descriptive and predictable.
With 4 tools, the server is tightly scoped to the domain of Texas electricity plan comparison. Each tool serves a distinct step in the user workflow—eligibility check, comparison, address-specific comparison, and plan explanation—so the count feels well-balanced and not excessive.
The tool surface covers the full lifecycle required for plan comparison: check market eligibility, compare plans by zip or address, and get detailed cost breakdowns. There are no obvious dead ends—users can flow from territory check to plan ranking to plan details without missing operations. The server's purpose is fully realized.
