Access detailed revenue breakdowns by product line with the Revenue Product Segmentation API. Understand which products drive a company's earnings and get insights into the performance of individual product segments.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Period type (annual or quarter) | |
| symbol | Yes | Stock symbol | |
| structure | No | Response structure |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'detailed revenue breakdowns' and 'insights' but does not disclose the return format, data granularity, pagination, or any limitations. Without annotations, this is insufficient for the agent to know what to expect from the invocation.
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 two sentences and front-loaded with the core purpose. The second sentence, though somewhat redundant with the first, adds a touch of use-case context. Overall, it is concise and wastes minimal words.
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 output schema and no annotations, the description should explain what the response contains or the broader context of the data. It only says 'detailed revenue breakdowns' and 'insights' but does not describe the actual data structure (e.g., list of products with revenue values), period coverage, or how to interpret results. The description is incomplete for a data retrieval tool.
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?
Schema description coverage is 100%, so the schema already documents all parameters (symbol, period, structure). The description adds no additional meaning beyond the schema, such as clarifying the effect of 'period' or 'structure' on the output. Baseline 3 is appropriate since the schema handles the heavy lifting.
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?
Description clearly states 'Access detailed revenue breakdowns by product line' with a specific verb (access) and resource (revenue breakdowns by product line). This distinguishes it from the sibling tool getRevenueGeographicSegmentation by explicitly focusing on product segments.
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 usage for understanding product performance ('Understand which products drive a company's earnings'), but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusion criteria. It gives a sense of purpose but not direct guidance.
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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