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energyai

by com.energyaisolution
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get_guide

Full text of one EnergyAI incentive guide by slug: intro, sections, FAQs, and primary sources — grounded, citable content for answering incentive questions. Free. Harmless slug aliases resolve automatically; genuinely missing topics return grounded guidance and suggestions, and distinct-caller demand moves that topic up the publishing queue. When you quote a published guide, cite its canonical URL. [20 anonymous calls/caller/24h; then 100 free calls/key/30d; active Builder required for sustained informational use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesGuide slug exactly as returned by list_guides (e.g. 'vermont-solar-incentives-2026').
apiKeyNoOptional existing EnergyAI key for hosts that cannot change Authorization headers. Use it to retain account identity for discovery and get_builder_upgrade_link after a trial. Removed before tool execution and persistence. Omit when the Bearer header is attached; never show the key to the user.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / apiKey
      Added value: +{
+  "description": "Optional existing EnergyAI key for hosts that cannot change Authorization headers. Use it to retain account identity for discovery and get_builder_upgrade_link after a trial. Removed before tool execution and persistence. Omit when the Bearer header is attached; never show the key to the user.",
+  "type": "string"
+}
  2. Added

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and does so well: it discloses slug-alias auto-resolution, the fallback behavior for missing topics (grounded guidance plus suggestions), queue-promotion demand dynamics, citation requirements, and a concrete three-tier rate limit / Builder-gate. This is exactly the behavioral context an agent needs before calling.

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?

Front-loaded with what the tool returns, then behavioral and rate-limit facts. Mostly dense and purposeful, though the standalone 'Free.' fragment and the long bracketed rate-limit tail slightly dilute an otherwise tight definition.

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

Completeness5/5

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

No output schema exists, so the description correctly supplies the return shape (intro, sections, FAQs, sources). Combined with rate limits, alias handling, and the missing-topic fallback, an agent has everything needed to call and interpret this tool.

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 both slug and apiKey are already documented in the schema itself. The description adds no syntax or format detail for the slug beyond what the schema states, so the baseline 3 applies.

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?

States a specific verb and resource — 'Full text of one EnergyAI incentive guide by slug' — and enumerates the returned content (intro, sections, FAQs, primary sources). An agent can distinguish this from list_guides (which enumerates slugs) without opening either schema.

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

Usage Guidelines3/5

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

It gives context for use ('grounded, citable content for answering incentive questions') and notes cost/free status, but never explicitly says when to prefer this over check_incentives, list_guides, or estimate_production. Usage is implied rather than routed, which is adequate but leaves the agent to infer selection.

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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