AI 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?
With no annotations provided, the description carries the behavioral disclosure burden. It adds meaningful context: data is sourced from SEC annual reports, the dataset covers 12 companies, it excludes prices and ratings, and it is openly licensed under CC BY 4.0. It does not mention auth, rate limits, or latency, but for a zero-parameter read-only retrieval these are 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, opening with the tool's identity and core scope. Every element—scope, source, metrics, exclusions, licensing—adds value, though the dense metric list makes it slightly less skimmable than it could be.
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 parameterless tool with no output schema, the description is largely complete: it states what the tool returns, the data source, the size of the dataset, and its limitations. A description of the output shape would add some value, but an agent has enough information to invoke the tool correctly.
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 and the schema has 100% coverage (an empty properties object), so the baseline is naturally high. The description does not need to explain parameter behavior and none is missing.
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 clearly identifies the resource ('AI Leaders'), its scope (12 US public companies in the AI business), and the data it contains (revenue, growth, operating margin, etc.). It also distinguishes itself from sibling tools by explicitly stating 'No prices, no ratings,' which separates it from price_program and similar offerings.
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 provides useful context for when to use this tool: when an agent needs AI-company fundamentals from SEC filings, not prices or ratings. However, it does not explicitly name alternative tools or provide when-not-to-use conditions, leaving the agent to infer the routing decision from the sibling list.
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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