lab_grown_price_indexThe latest tracked retail price reading for Lab Grown Diamonds, with source and date. Market context for shoppers, not investment guidance.
| 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe read-only operation. The description adds concrete behavioral context beyond annotations: the output includes a source and date, and the information is intended as market context rather than investment guidance. No contradiction with annotations.
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 with no filler. The core purpose is front-loaded in the first clause, and the audience/limitation note is a compact, useful addition. Every word earns its place.
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 simple, parameterless read-only tool, the description is largely sufficient: it states what is returned (latest retail price reading), the implicit output fields (source and date), and the intended context. It does not specify units or formatting, but with no output schema and a straightforward purpose, this is a minor gap rather than a blocking one.
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 schema coverage is 100%, so there is nothing for the description to add about parameter semantics. The baseline of 4 applies because no parameter documentation burden exists.
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 ('Lab Grown Diamonds') and the specific nature of the data ('latest tracked retail price reading'), and the title reinforces this. It implicitly distinguishes itself from sibling tools like search_inventory or get_product by focusing on a market-level price index rather than a specific product or inventory item. The added note that it is 'Market context for shoppers, not investment guidance' further clarifies its scope.
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 clear context for when to use this tool—when a shopper wants the latest tracked retail price reading. It also explicitly excludes a use case ('not investment guidance'), which helps prevent misuse. However, it does not name alternative tools or conditions that would route an agent to a sibling tool, though the distinction is reasonably inferable from the domain and siblings.
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.
Most tools target clearly distinct actions: searching, fetching details, looking up terms, verifying reports. The main ambiguity is between define and dutch_marquise_definition, since the latter appears to be a single encyclopedia entry surfaced as a standalone tool.
All names are snake_case and readable, but the set mixes verb-led names like search_inventory and get_product with noun-phrase names like lab_grown_price_index and dutch_marquise_definition. The pattern is not consistent enough to predict the tool name from the action alone.
Ten tools is well within the ideal range for a server that handles both diamond education and catalog shopping. Each tool contributes to a clear workflow: learn about diamonds, search inventory, fetch product details, and verify grading reports.
The tool surface covers the main user journey: research terms, search inventory, get product detail, understand grading, and verify reports. Minor gaps exist, such as only having a dedicated definition for the Dutch Marquise cut while other shapes rely on the general encyclopedia lookup.