Cyclesite catalogue entry for a brand+model: category, year range, AI-generated description, key specs, market summary. Reference data — refreshed when models are added or specs change. Example: 'tell me about the Specialized Allez'.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | ||
| model | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, openWorld, and idempotent behavior. The description adds valuable context about freshness ('refreshed when models are added or specs change') and that the description is AI-generated, which helps the agent understand content expectations and potential staleness.
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 substantive sentences plus an example. It front-loads the core purpose, lists the return fields, adds refresh behavior, and gives a natural-language usage example without any 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?
Given this is a simple two-parameter read-only lookup with a rich output schema and strong annotations, the description is complete. It covers the data content, reference nature, and an example, while the output schema handles return structure details.
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 schema has only bare string types with no descriptions (0% coverage). The description partially compensates by specifying 'brand+model' and providing an example, but it does not elaborate on expected format, variants, or edge cases.
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 states that this tool returns a Cyclesite catalogue entry for a brand/model and enumerates the included fields: category, year range, AI-generated description, key specs, and market summary. This distinguishes it from sibling tools like get_spec_sheet or get_valuation by centering on a general catalogue overview.
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 a clear usage context through the example 'tell me about the Specialized Allez' and notes that it is reference data refreshed when models/specs change, implying a general lookup. However, it does not explicitly name alternatives or state when not to use this tool relative to its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
