get_productReturns one canonical ProductTwin and its source-level provenance by stable identifier.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| tool | Yes | ||
| facts | Yes | ||
| status | Yes | ||
| warnings | Yes | ||
| requestId | Yes | ||
| confidence | Yes | ||
| provenance | Yes | ||
| schemaVersion | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: the returned ProductTwin is 'canonical' and includes 'source-level provenance', which goes beyond the annotations. It does not introduce contradictions or disclose additional behaviors like rate limits or error cases, but the annotation coverage lowers the burden.
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 a single, front-loaded sentence with no wasted words. It states the action ('Returns'), the object ('one canonical ProductTwin'), the additional attribute ('source-level provenance'), and the input mechanism ('by stable identifier'). Every phrase 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?
This is a simple single-parameter lookup tool with an output schema present, so the description need not explain return values. The description, combined with annotations and schema, provides enough for correct invocation: pass a stable identifier to get a canonical twin. The only minor gap is explicit usage routing with sibling tools, but it is not critical for this simple operation.
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 coverage is 0%, and the schema only defines 'id' as a string with a pattern. The description helps by calling it a 'stable identifier', giving semantic meaning to an otherwise opaque parameter. However, it does not elaborate on the identifier format or where to find it, so it only partially compensates for the missing schema descriptions.
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 uses a specific verb 'Returns' and a specific resource 'canonical ProductTwin and its source-level provenance', which clearly states what the tool does. The phrase 'by stable identifier' explains the lookup mechanism and distinguishes this from search-oriented siblings like search_parts and resolve_problem, which do not retrieve by canonical identifier.
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: call this tool when you have a stable identifier and need the canonical ProductTwin with provenance. However, it does not explicitly name alternatives or state when NOT to use this tool, leaving the agent to infer the boundary with sibling tools like search_parts.
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 targets a distinct capability: exact product retrieval, search, pairwise compatibility, fit guidance, offer lookup, generation-family listing, and problem resolution. The closest pair (get_compatibility and get_fit_guidance) is separated by interface matching vs. manufacturing compensation and physical-test warnings.
All tool names are lowercase snake_case with a clear verb-object form: get_*, list_*, search_*, resolve_*. There are no mixed naming conventions or vague generic verbs.
Seven tools is well-scoped for a parts decision-support server. Each tool covers a distinct operation without redundancy, and the count is neither thin nor bloated.
The surface covers core lookup, search, compatibility, fit, offer, generation-option, and problem-resolution workflows well. The only minor gap is that generation options are listed but not expanded into detailed generation variants, so an agent may need to work around that via resolve_problem.