Store information
get_store_infoHow buying from Seduction.lk works: delivery areas, times and charges, payment methods, cancellations and returns, and how to contact customer service.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_store_infoHow buying from Seduction.lk works: delivery areas, times and charges, payment methods, cancellations and returns, and how to contact customer service.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and a closed-world scope, so the safety profile is fully covered without the description. The description adds only that the returned content spans named policy topics, but says nothing about format, currency of the information, or whether it is static versus account-specific.
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?
A single well-formed sentence that front-loads the core purpose and then lists covered topics. No filler, though the colon-separated topic list borders on enumerating rather than explaining.
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?
With no parameters, no output schema and fully covered annotations, the description carries the remaining burden well by enumerating the topics it covers, effectively serving as loose return-value documentation. It could be stronger only by stating when to prefer this over the product-search siblings.
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 takes zero parameters, so there is no parameter semantics to convey and the baseline is a 4. The description correctly makes no attempt to describe inputs.
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 names a specific resource and enumerates its scope (delivery areas/times/charges, payment methods, cancellations and returns, customer-service contact). An agent can immediately tell this is the store's static policy/FAQ content rather than product data, so it is distinguishable from product-oriented siblings even without naming them.
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?
Usage is only implied: the topic list signals this is the right call for policy, shipping, payment or returns questions. There is no explicit 'use this when...' statement, no exclusions, and no reference to alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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