generate_energy_recommendation_preview
Generate honest cost, savings, and next-action ranges. [$0.05/call]
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| assessmentId | Yes |
energyai
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'honest' ranges and cost, but does not state whether the tool is read-only, what data it relies on, whether it mutates state, or how the preview differs from a full recommendation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states the tool's purpose and includes pricing. There is no wasted verbiage; every word contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no nested objects) and the description covers the core output types, but because there is no output schema, return format details are unspecified. Missing usage guidance and parameter context leave some gaps, though the core purpose is adequately conveyed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the only parameter, assessmentId, is not explained in the description. The name suggests it is an assessment identifier, but the description does not clarify what kind of assessment is expected or how it relates to the generated preview.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Generate') and resource ('energy recommendation preview') and explicitly names the outputs: cost, savings, and next-action ranges. This clearly distinguishes the tool's action from mere retrieval, though it does not explicitly contrast with sibling tools like generate_intelligence_bound_report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as get_information_theoretic_recommendation or generate_intelligence_bound_report. The only contextual hint is the $0.05/call pricing, which implies a cheap preview but does not constitute usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
C2.8/5.0
Disambiguation2/5
Several tools overlap heavily: get_node_score vs get_energy_node_score, find_local_installers vs match_installers, and route_lead vs submit_contractor_match_request are easy to confuse. The descriptions clarify details, but an agent would often need to inspect arguments to know which installer, incentive, or recommendation tool is the right one.
Naming Consistency3/5
All tool names use snake_case with a verb-leading style, which provides a consistent backbone. However, domain naming is inconsistent: the same concept appears as node vs energy_node, installer vs contractor, and incentive discovery is split across check_incentives, get_energy_incentives, list_guides, and get_guide without a predictable pattern.
Tool Count2/5
29 tools is too many for a single MCP server, especially since several fall into overlapping installer/lead, incentive, recommendation, and commercial-power categories. Many tools appear to be monetization gates or handoff variants that could reasonably be consolidated.
Completeness3/5
The set covers core user journeys: assessment creation/answering/completion, incentive guidance, installer discovery and routing, quote review, and commercial power screens. However, there are notable gaps in state management, such as no way to list or retrieve existing assessments/leads, and several checkout or handoff tools have no follow-up/status tool.