get_prediction_duelGuesswork — the public prediction duel: every day frontier LLMs and The Aggregate's own IRT model predict newly scraped benchmark scores before seeing them, and the errors are scored. Returns the current monthly standings, wins and losses included.
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the purpose and return value but does not explicitly disclose behavioral traits such as read-only nature or side effects. Since no annotations are provided, the description partially carries the transparency burden, but it is adequate for a no-parameter tool.
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 concise, consisting of two sentences. The first sentence provides context, and the second states the output. Every word earns its place with no wasted text.
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?
Given the tool has no parameters and no output schema, the description is sufficiently complete. It explains the concept and return value. Minor gaps exist, such as not clarifying 'wins and losses' detail, but it remains highly functional for a simple tool.
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 has zero parameters and 100% schema description coverage, so the description does not need to add parameter details. The baseline score of 3 applies, and no additional value is provided beyond the schema.
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 that the tool returns the current monthly standings of a prediction duel, including wins and losses. It uses a specific verb and resource, distinguishing it from sibling tools like get_leaderboard or compare_models.
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?
No guidance is provided on when to use this tool versus alternatives. The description explains what it does but does not specify contexts or exclusions, leaving the agent to infer usage implicitly.
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 serves a unique purpose: general info, model comparison, benchmark details, leaderboard, model details, prediction game, and two search tools. No overlap or ambiguity.
All tool names follow a consistent verb_noun snake_case pattern (e.g., about_the_aggregate, compare_models, get_benchmark). No deviations.
8 tools is an appropriate number for a benchmark aggregation server, covering all necessary functionality without being excessive or sparse.
The tool set covers all major operations for the domain: information, comparison, leaderboard, search, and prediction. No obvious gaps for a read-only aggregate service.