Real-world asset tokenization benchmarks — tokenized T-bill yields (Ondo, BlackRock BUIDL, Superstate, Franklin Templeton), RWA market TVL by category, YoY growth. $12.8B total RWA market. Source: DeFiLlama + public data.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive, so the description adds valuable context beyond that: it reveals the source (DeFiLlama), the included metrics (T-bill yields, TVL, YoY growth), and a concrete market size. No contradictions with annotations.
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 two sentences, front-loaded with the primary purpose, and adds specific details without redundancy. Every word contributes meaning.
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 simple read-only tool with one optional parameter, the description covers the data contents and source but omits the category parameter behavior and does not describe the output format. Adequate but with notable gaps.
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 description does not mention the `category` parameter at all, despite the schema having 0% description coverage. It uses the phrase 'by category' but never maps that to the input parameter or its enum values, leaving the agent to guess how filtering works.
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 a specific resource: real-world asset tokenization benchmarks, with concrete data points like tokenized T-bill yields and TVL by category. This distinguishes it from sibling benchmark tools such as get_chain_tvl_benchmark or get_crypto_correlation_benchmark.
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 usage for RWA-related metrics and quotes specific data categories, but it does not explicitly state when to choose this tool over alternatives or provide exclusions. The inclusion of specific examples makes the intended use 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 benchmark category (e.g., chain TVL, yield, options IV, RWA), with no overlapping purposes. The overview tool is clearly different. Agents can easily distinguish them.
Most tools follow 'get_<descriptor>_benchmark' pattern, but 'get_stratalize_overview' breaks the pattern. The naming is otherwise consistent and descriptive.
With 9 tools covering major DeFi and crypto benchmarks (TVL, yields, gas, options, RWA, etc.), the set is well-scoped and each tool serves a clear purpose.
The tool surface covers a broad range of crypto/DeFi benchmarks. Minor gaps exist (e.g., spot price feeds, exchange data), but the set is sufficient for benchmarking and agent discovery.