get_ai_leadersAI Leaders: 12 US public companies in the AI business, read from their own SEC annual reports — revenue, growth, operating margin, research spending, equity, return on equity, Edgarette’s 80% revenue range. No prices, no ratings. Free, CC BY 4.0.
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Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context beyond annotations: the data comes from companies' own SEC annual reports, the exact metrics included, the explicit absence of prices and ratings, and the licensing terms. It does not describe return format, but that is secondary for a zero-parameter dataset tool.
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 a single compact sentence that front-loads the dataset identity, source, contents, and exclusions. Every element earns its place, including the licensing note, and there is no redundant filler.
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?
With no input parameters and no output schema, the description carries most of the burden of explaining what the tool returns. It lists the included metrics and source, which is sufficient to set expectations. A fuller return description would help, but the annotations and metric list make the tool understandable.
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 zero parameters, so parameter semantics are not applicable and the baseline is 4. The description does not introduce any parameter-related confusion.
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 states a specific resource: 12 US public companies in the AI business, with data drawn from SEC annual reports. It lists the metrics covered, making the tool's scope clear. It does not explicitly distinguish itself from sibling dataset tools like get_company_performance or get_cash_leaders, but the subject matter is specific enough for an agent to identify it.
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?
Usage is implied by the description: call this tool when you need financial and operating metrics for a fixed set of AI companies. The description notes 'No prices, no ratings,' which gives a negative boundary, but it does not name alternatives or explain when another sibling tool would be more appropriate.
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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