Get pricing
get_pricingReturn public Tyndall AI subscription and credit-pack prices in USD.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_pricingReturn public Tyndall AI subscription and credit-pack prices in USD.
| 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?
No annotations exist, so the description carries the full burden. It usefully discloses that the data is public (implying no auth needed) and denominated in USD, but says nothing about caching, freshness, or rate limits. For a trivial zero-arg read this is acceptable but thin.
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 front-loaded sentence with zero filler. Every word (public, subscription, credit-pack, USD) carries information.
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 zero-parameter read tool with no output schema, the description adequately states the returned content and currency. It could name the response shape more concretely, but nothing needed to invoke it correctly is missing.
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 nothing for the description to clarify and the baseline of 4 applies. No meaningful semantic gap 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 gives a specific verb ('Return') and resource ('public Tyndall AI subscription and credit-pack prices in USD'), so an agent can tell it apart from list_capabilities and list_models. It stops short of explicitly naming the siblings, but the resource is unambiguous.
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 prerequisites, and no alternatives named (the siblings cover different resources rather than being substitutes). Usage must be inferred entirely from the purpose sentence.
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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