get_modelOne model in depth: aggregate rank, Elo with standard error, provider, what it is, cost per task where known, and its most notable benchmark results (with percentiles).
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model name or slug, e.g. "Claude Opus 4.5" or "gpt-5-5". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It describes the output content but does not mention idempotency, authentication needs, rate limits, or any side effects. As a read operation, it likely has minimal impact, but this is not confirmed.
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 a single, front-loaded sentence that efficiently lists the tool's output. It is concise with no redundant words, though it could be slightly more structured (e.g., bullet points).
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?
Despite lacking an output schema, the description enumerates the key return fields: rank, Elo, provider, cost, and benchmarks. For a single-parameter tool, this provides a clear picture of what the tool returns. Missing details like output format or pagination are not critical here.
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?
Schema coverage is 100%, and the description adds minimal value beyond the schema: the parameter 'model' is described in the schema as 'Model name or slug, e.g. "Claude Opus 4.5" or "gpt-5-5". The description does not add new semantic detail (e.g., case sensitivity, format constraints), so a baseline of 3 is appropriate.
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 the tool provides in-depth profile for one model, listing specific data: aggregate rank, Elo, provider, cost, and notable benchmarks. This distinguishes it from siblings like 'compare_models' (multiple models) and 'get_leaderboard' (list).
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 implies the tool is for obtaining detailed single-model info, but it does not explicitly state when to use it over alternatives like 'compare_models' or 'search_models'. No when-not-to-use or prerequisite guidance is given.
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.