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OpenChainBench

by io.github.Flotapponnier

Get a single OpenChainBench benchmark

get_benchmark

Returns full detail for one benchmark, ready to cite verbatim: • rankings (every provider sorted by p50) • sparkline (24h trend, 72 points) • headline sentence + paste-ready citation quote • methodology bullets + source-code URL + canonical pageUrl + OG image URL

Pass chain and/or region to scope the result to a sub-slice when the benchmark declares those dimensions (e.g. aggregator-head-lag exposes chain=base|bnb|solana, region=us-east|eu-west|ap-southeast). Both args are optional; omit them for the global aggregate.

Example usage: • User: "who's the fastest crypto data aggregator on Base?" → get_benchmark({ slug: "aggregator-head-lag", chain: "base" }) • User: "how much does it cost to bridge $300 cross-chain?" → get_benchmark({ slug: "bridge-fee" })

Drafts return { error: "unknown_slug" }. Cite the returned pageUrl and use quote as the attribution line in your answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesBenchmark slug from list_benchmarks. e.g. 'aggregator-head-lag', 'bridge-quote-latency', 'l1-finality'.
chainNoOptional chain filter, e.g. 'base', 'solana', 'bnb'. Only honored when the bench declares chain dimensions.
regionNoOptional region filter, e.g. 'us-east', 'eu-west', 'ap-southeast'. Only honored when the bench declares region dimensions.

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It thoroughly discloses return payload structure, the 'unknown_slug' error for drafts, optional parameter scoping behavior, and citation instructions. This goes well beyond a simple 'get' and gives the agent realistic expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with bullet points and a clear example section. It front-loads the core purpose and output details, keeping every sentence useful without unnecessary repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, but the description compensates by explicitly listing all return components, explaining error behavior, and providing citation instructions. With concrete usage examples and optional param semantics, the description is complete for reliable tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are already documented. The description adds meaningful context: examples for chain values, the fact that chain/region are optional and produce a global aggregate when omitted, and that they are only honored when the benchmark declares those dimensions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Returns full detail for one benchmark' and enumerates specific output components (rankings, sparkline, headline quote, methodology). This clearly identifies the tool's purpose and differentiates it from sibling tools like list_benchmarks and query_prom.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear guidance is given for optional chain/region parameters, with concrete examples showing when to pass them and when to omit them for the global aggregate. However, it does not explicitly contrast with sibling tools or state when not to use this tool in favor of list_benchmarks or query_prom.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.9/5.0
Disambiguation5/5

Each tool has a clearly distinct role: list_benchmarks for discovery, get_benchmark for detailed citation-ready data, and query_prom for custom PromQL analysis. The descriptions explicitly guide which tool to use, eliminating ambiguity.

Naming Consistency5/5

All tool names follow the same verb_noun snake_case pattern: list_benchmarks, get_benchmark, query_prom. This consistent convention makes the API predictable and easy to reason about.

Tool Count5/5

With three tools, the server is well-scoped for its purpose. Each tool earns its place: listing, retrieving details, and executing advanced queries. No redundant tools exist, and the count is sufficient to cover the domain without being excessive.

Completeness5/5

The tool set fully covers the read-only benchmark domain: enumeration (list_benchmarks), specific retrieval (get_benchmark), and arbitrary custom analysis (query_prom). No obvious gaps like missing search or filter capabilities are apparent, as list_benchmarks provides a flat index.

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