get_friendship_challengeRetrieve one private 24-question Know Me challenge through its exact opaque code capability. Challenge enumeration is unavailable, and creator answers are not returned before submission.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral constraints: the challenge is private, answers are withheld before submission, and enumeration is impossible, which an agent needs to set expectations correctly.
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, information-dense sentence delivers the core capability first, followed by key limitations. No filler or repeated schema information is present.
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 a simple one-parameter input, strong annotations, and an output schema available, the description covers the essential retrieval semantics and caveats. Nothing critical for calling the tool 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 input schema only provides the code pattern without a description. The description compensates by explaining that the code is opaque and must be exact, and that no enumeration or listing exists. This adds meaningful semantic context to the single parameter.
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 identifies the resource as 'one private 24-question Know Me challenge' and the action as 'Retrieve' via an exact opaque code. This distinguishes it from sibling tools focused on personality instruments, daily questions, and ballot challenges.
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 states that challenge enumeration is unavailable, so the tool is only usable when the caller already possesses an exact opaque code. It does not explicitly name sibling alternatives or conditions for choosing them, but the usage context is reasonably clear.
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 resource or flow, and the descriptions include strong qualifiers like 'exact opaque code' or 'issued challenge and nonce'. However, cast_ballot, submit_friendship_prediction, and submit_personality_perception all represent submission actions and could be misselected if the agent does not carefully match the exact challenge type.
Tool names follow a consistent verb_noun snake_case pattern throughout, dominated by get_ for retrieval and submit_ for submissions, with unique verbs like cast_, create_, request_, and score_ standing out clearly. There are no mixed casing styles or unpredictable naming conventions.
14 tools is well within the ideal range and each tool appears to serve a distinct purpose across five clear subdomains: daily ballots, friendship challenges, personality perception, scoring, and aggregate results. The count feels intentionally scoped rather than bloated or thin.
Core lifecycle paths are present for ballots, friendship predictions, personality perception, and result retrieval. Minor gaps exist around explicit creation of private challenges and any update or cancel operations, but those appear intentionally constrained by the server's privacy and immutability model.