list_productsList every product slug tracked (nodejs, python, react, postgresql, ubuntu, …). Call this first when unsure of the exact product slug to pass to get_product.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds that it returns a list of slugs with examples, which is slightly more than the bare annotations but does not disclose additional behavioral details like output format or pagination (though there are no params). This is adequate but not rich.
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?
Two sentences, no fluff. The first sentence states the core function with examples; the second gives a direct usage directive. Every word earns its place, and it is front-loaded with the verb 'List'.
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 read-only list tool, the description fully covers purpose and usage. It names the primary sibling (get_product) and explains when to use it. The absence of an output schema is not a gap here since the description says it returns slugs. Slightly more detail on return type (e.g., 'array of strings') would make it perfect, but it is complete enough.
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 schema coverage is 100% and there is nothing to explain about inputs. The description adds semantic value by enumerating example slugs, which indirectly clarifies what the tool returns and what kind of data the user can expect. Baseline for 0 params is 4, which is appropriate.
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 the tool lists all tracked product slugs, with concrete examples (nodejs, python, react, etc.). It explicitly differentiates from get_product by explaining its role as a precursor when the slug is unknown, making the purpose unmistakable.
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?
It provides explicit guidance: 'Call this first when unsure of the exact product slug to pass to get_product.' This tells when to use it and relates it to a sibling tool. However, it does not mention get_cycle or give situations when not to use it, but the instruction is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: listing products, getting all cycles for a product, and getting a single specific cycle. The descriptions explicitly state when to use each, reducing ambiguity for agents.
All tool names follow a consistent verb_noun pattern: get_cycle, get_product, list_products. The verbs are distinct and the nouns clearly indicate the resource type, and the pattern is uniform.
With only 3 tools, the count is minimal but appropriate for a focused purpose: querying version information. It covers the essential operations (list, get all, get one) without unnecessary bloat, though it's on the low end of the ideal range.
The tool surface covers the core workflow: discover products, retrieve product cycles, and drill into a specific cycle. It lacks update/create operations, which is fine for a read-only API, but might miss advanced queries like searching for a specific version across products, though the current set covers the main use cases.