Renewable & Geothermal Energy: 12 US public companies in solar, wind, grid, fuel cells and geothermal, read from their own SEC annual reports — the same fields as get_ai_leaders. No prices, no ratings. Free, CC BY 4.0.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavior. It discloses the data source (SEC annual reports), the universe (12 US companies), sector coverage, and exclusions, and points to the schema of get_ai_leaders for field semantics. It does not state update frequency or an explicit read-only guarantee, but zero parameters and the described dataset make that less critical.
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?
The description is compact and front-loaded, covering scope, source, exclusions, and licensing in a few clauses. Minor redundancy appears in saying 'geothermal' both in the title phrase and the sector list, and it is one dense sentence rather than structured, but no word is wasted.
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 zero-parameter tool with no output schema and no annotations, the description gives enough information to select and invoke it: what it returns, its source, scope, and non-price nature. The only gap is that 'same fields as get_ai_leaders' defers the exact return structure to another tool's description.
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?
The tool has no parameters and schema coverage is 100%, so there is no parameter-level gap. The description still clarifies the data content, which is all an agent needs for invocation.
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 identifies the resource (a dataset of 12 US renewable/geothermal public companies) and scope (solar, wind, grid, fuel cells, geothermal), and distinguishes it from get_ai_leaders by noting same fields. It lacks an explicit action verb like 'returns' or 'lists,' so it is clear but not maximally precise.
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 implies use for energy-leader data by naming the sector and comparing it to get_ai_leaders, and it states exclusions ('No prices, no ratings'). However, it never explicitly says when this tool should be chosen over siblings such as get_ai_leaders or price_program, leaving the routing partly to inference.
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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