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energyai

by com.energyaisolution

estimate_production

Honest-range annual solar production estimate (kWh/year ± band, with assumptions) for a ZIP code, from either a proposed system size (kW) or a monthly bill. Use to sanity-check installer quotes or size a system before talking to anyone. [20 anonymous calls/caller/24h; then 100 free calls/key/30d; active Builder required for sustained informational use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoISO 3166-1 alpha-2 country code. Omit for US. Non-US estimates use a default solar resource and say so honestly.
zipCodeNoOptional postal code of the property. US: 5-digit ZIP. Other countries: your local postal code (pass country too). Omit it to use documented national assumptions.
systemKwNoProposed solar system size in kW-DC. Omit to have a size recommended from the bill.
monthlyBillUsdNoAverage monthly electric bill in USD (used to size a system when systemKw is omitted).

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses rate limits ('20 anonymous calls/caller/24h; then 100 free calls/key/30d'), auth requirements ('active Builder required'), and the uncertainty/assumption nature ('Honest-range', '± band, with assumptions'). It doesn't explicitly state read-only status, but 'estimate' implies it, so overall strong.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core output and use case, followed by rate-limit context. It is slightly redundant ('Honest-range' and '± band, with assumptions' overlap), and the bracketed rate-limit clause is long, but every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema or annotations, the description covers output format, assumptions, input modes, use cases, and access limits. Edge cases like simultaneous systemKw/monthlyBill or non-US behavior are not described, but the schema covers those details, so the description is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description maps systemKw and monthlyBillUsd to 'proposed system size (kW)' and 'monthly bill,' but adds no additional parameter-level meaning beyond what the schema already provides (e.g., precedence, unit formats, or country nuances).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource as an 'annual solar production estimate' with a specific output format ('kWh/year ± band, with assumptions') for a ZIP code. It also distinguishes the tool from the many energy/referral siblings by focusing on production estimation and quote sanity-checking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit use case: 'Use to sanity-check installer quotes or size a system before talking to anyone.' However, it doesn't mention when not to use it or point to alternative sibling tools for comparison, which would warrant a 5.

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.

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