get_glossary_term
Read one ecommerce glossary term by slug.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Glossary term slug. |
Read one ecommerce glossary term by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Glossary term slug. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds minimal behavioral context beyond the basic read operation.
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 6-word sentence, concise and front-loaded with the core information. No unnecessary words or sentences.
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 read tool with full schema coverage, clear annotations, and no output schema, the description provides adequate context to understand the tool's purpose and usage.
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 100% with the slug parameter described as 'Glossary term slug.' The description repeats 'by slug,' adding no new semantic value. Baseline 3 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 action (read), resource (one ecommerce glossary term), and method (by slug). It distinguishes itself from sibling tools like search_glossary, which likely returns multiple results.
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 when to use this tool (retrieve a single term by slug) but does not explicitly state when not to use it or mention alternatives. The context of sibling tools provides some differentiation.
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.