Reading languages
get_languagesLanguages a SwiftPrism Elite member can read headlines, Deep Insights, and the Economic Calendar in, and hear Headline Voice in.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_languagesLanguages a SwiftPrism Elite member can read headlines, Deep Insights, and the Economic Calendar in, and hear Headline Voice in.
| 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=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuine scope context by enumerating the four content surfaces the languages apply to (headlines, Deep Insights, Economic Calendar, Headline Voice). It says nothing about the shape of the response or whether the list is static or entitlement-dependent.
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 compact sentence with no filler. The stacked 'read ... in, and hear ... in' construction is slightly convoluted, but the content is front-loaded and every clause carries information about the returned scope.
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 trivial zero-parameter read tool with annotations covering safety, the description is nearly sufficient. However, no output schema exists, so the burden of describing the return value falls on the description – it never says whether the result is a list of language codes, display names, or localized labels, which an agent would want before using the output.
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 the baseline of 4 applies. There is no parameter surface for the description to clarify or fail to clarify.
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 concrete resource – the set of languages a SwiftPrism Elite member can consume headlines, Deep Insights, the Economic Calendar, and Headline Voice in. It is identifiable and clearly distinct from the transactional siblings (get_plans, create_checkout_link, start_trial). It lacks an explicit verb ("List"/"Get"), reading as a noun phrase, which keeps it short of a 5.
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?
There is no statement of when to call this tool, no prerequisite, and no reference to any alternative. Usage is only implied by the content of the sentence – an agent must infer that this answers 'which languages are supported?' rather than being told.
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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