get_benchmarkOne benchmark in depth: what it measures, the original source leaderboard URL, IRT stats (difficulty, noise, model coverage), skill weights, and the current top models on it.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | How many top models to list (1-50, default 10). | |
| benchmark | Yes | Benchmark name or slug, e.g. "Aider polyglot". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It lists all returned fields (measure, URL, IRT stats, skill weights, top models), implying a safe read operation. It lacks explicit mention of side effects or auth, but for a read-only lookup, transparency is adequate.
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?
Single sentence listing all returned items is efficient and front-loaded. Slightly dense at 29 words, but no redundancy. Could be split for readability, but it's clear and compact.
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?
No output schema exists, so description must cover return values. It lists key items (measure, URL, IRT stats, skill weights, top models). Parameters are well-documented in schema. For a simple retrieval tool, this is sufficiently complete for an AI agent to understand 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?
Schema coverage is 100% with clear descriptions for both parameters ('benchmark' and 'top'). The tool description adds no extra parameter details beyond summarizing the output. Baseline 3 applies as schema does the heavy lifting.
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 retrieves detailed information for a single benchmark, listing specific data points (measure, URL, IRT stats, skill weights, top models). This distinguishes it from sibling tools like get_leaderboard (list) or search_benchmarks (search).
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?
Implicitly, the tool is for deep-diving into one benchmark, contrasting with sibling tools for listing or searching. However, no explicit guidance on when to use or avoid is provided, missing a clear when-not or alternatives statement.
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.