OpenChainBench
Server Details
Live, neutral benchmarks for public RPC latency, oracles, bridges, perp DEX, and prediction markets.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- ChainBench/OpenChainBench
- GitHub Stars
- 4
- Server Listing
- Openchainbench
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Tool Definition Quality
Average 4.7/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: list_benchmarks for discovery, get_benchmark for detailed citation, and query_prom for advanced custom queries. There is no overlap in functionality, and descriptions clearly differentiate them.
Two tools follow a consistent verb_noun pattern (get_benchmark, list_benchmarks), but query_prom breaks the pattern by using 'query' as the verb and an acronym as the object. However, the naming is still intuitive and readable.
With only 3 tools, the server is tightly scoped to its purpose: listing benchmarks, getting details, and executing custom PromQL queries. This is appropriate for a data access API without unnecessary surface area.
The tools cover the core workflows: discovery (list), detail (get), and advanced analysis (query). A minor gap is the lack of filtered search beyond listing all benchmarks, but query_prom can compensate. Overall, the surface is well-suited to the domain.
Available Tools
3 toolsget_benchmarkGet a single OpenChainBench benchmarkAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Benchmark slug from list_benchmarks. e.g. 'aggregator-head-lag', 'bridge-quote-latency', 'l1-finality'. | |
| chain | No | Optional chain filter, e.g. 'base', 'solana', 'bnb'. Only honored when the bench declares chain dimensions. | |
| region | No | Optional region filter, e.g. 'us-east', 'eu-west', 'ap-southeast'. Only honored when the bench declares region dimensions. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the return structure, optional filters, and error case for drafts. It does not mention destructive actions or auth, but as a read-only tool, this is sufficient.
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?
Well-structured with bullet points and examples. Front-loaded with purpose, then detail. No superfluous 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?
Despite no output schema, the description fully explains the output fields and error handling. The tool's complexity (3 params, no nested objects) is well-covered.
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%, so baseline is 3. Description adds value by explaining that chain/region filters are 'only honored when the bench declares those dimensions' and provides practical examples. This goes beyond schema descriptions.
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 'Returns full detail for one benchmark' and lists specific contents (rankings, sparkline, headline, etc.). It distinguishes from sibling tools list_benchmarks (which lists all) and query_prom (likely raw queries).
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?
Provides explicit guidance on optional chain/region parameters with examples, and mentions error handling for drafts. Does not explicitly state when not to use, but context and sibling mentions imply appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_benchmarksList OpenChainBench benchmarksAInspect
Returns a flat index of every published OpenChainBench benchmark with its current headline value, leader, category, units, and citation URL.
Call this first when the user asks a discovery question like
"what benchmarks does OpenChainBench have?" or "compare crypto aggregators".
Then use get_benchmark for the specific slug(s) the answer needs.
Returns one line per bench: { slug, title, category, metric, unit, value, leader, headline, url, asOf }
Drafts are filtered out: only live benchmarks appear.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return format, filtering of drafts, and read-only nature. Without annotations, it covers key behaviors, though it omits details like rate limits or auth (which are likely minimal given zero parameters).
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?
Concise and well-structured: first sentence states purpose, then usage guidance, then return format. No unnecessary words.
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 zero parameters and no output schema, the description provides a complete understanding of the tool's behavior, return structure, and relation to sibling tools.
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?
No parameters, so baseline 4 applies. Description does not need to add meaning beyond schema, which is empty and thus fully covered.
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?
Clearly states it returns a flat index of benchmarks with specific fields (headline value, leader, etc.). Distinguishes from sibling get_benchmark by mentioning it serves as a discovery tool.
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?
Explicitly instructs to call this first for discovery questions, then use get_benchmark for specific slugs. Also notes that drafts are filtered out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_promRun a PromQL query (scoped to benchmark metric namespaces)AInspect
Direct PromQL passthrough for advanced questions that don't map cleanly
to list_benchmarks / get_benchmark, e.g. "what was Mobula's p50
head-lag yesterday at 14:00 UTC" or "plot bridge fees over the last hour".
Prefer the higher-level tools first; reach for this when you need: • a custom time window (instant query at a specific point, or range) • a derived metric (rates, ratios, deltas) • a histogram bucket aggregation across chains/regions
Allowed metric namespaces (one prefix per OCB bench family):
head_lag_seconds (aggregator latency)
bridge_quote_latency_ms*, bridge_cost*, bridge_fees*, bridge_fix_fee*,
bridge_gas*, bridge_output*, bridge_estimated_time*, bridge_quote_success
l1_finality_*, l2_block_time_*
metadata_coverage_*, metadata_api_latency_*, network_coverage_*,
networks_supported, wallet_labels_*
perp_fees_*, ocb_buyback_*, ocb_oracle_*, ocb_validator_*, ocb_chain_*
gas_error_*, gas_predicted_*, gas_realized_*, gas_oracle_*
peg_* (stablecoin peg, both variants)
solana_landing_* (TX landing observational + active)
rpc_latency_*, rpc_call_total, rpc_health, rpc_archive_depth_supported
relay_*, per_swap_margin_usd (bridge revenue)
Queries referencing other metrics (operational/internal ones like up,
scrape_*, process_*, go_*, wallet_balance_* or any label-
enumeration shape) are refused with {error, reason}.
Example: instant p50 over 1h for Mobula head-lag on Base: query_prom({ query: "quantile_over_time(0.5, head_lag_seconds{aggregator="mobula",chain="base"}[1h]) * 1000" })
Example: 7-day sparkline of average bridge fees: query_prom({ query: "avg_over_time(bridge_fees_percent[1d])", windowSec: 604800, steps: 168 })
Returns: { query, value } for instant queries, { query, windowSec, series } for range.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | PromQL expression referencing published benchmark metric prefixes only. Function names, label keys, and quoted label values are fine; bare metric names must be allowlisted. | |
| steps | No | Number of samples for a range query (2 to 360). Default 60. Step duration = windowSec / steps. | |
| windowSec | No | If set, run a range query over the last N seconds (max 7 days = 604800). Omit for an instant query. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details allowed metric namespaces, refused query patterns, return formats for instant vs range queries, and provides examples covering both query types.
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?
Well-structured with clear sections: purpose, when-to-use, namespace list, refusal note, examples. Slightly lengthy but every section adds necessary detail; could be condensed slightly without losing clarity.
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 no annotations or output schema, the description comprehensively covers purpose, usage conditions, parameter semantics, behavioral constraints, and return types (instant vs range). Examples enhance completeness.
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% (baseline 3). Description adds value by explaining the interaction between steps and windowSec, reiterating allowed metric prefixes for query, and offering concrete examples that clarify parameter usage.
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's purpose as a direct PromQL passthrough for advanced queries not covered by list_benchmarks/get_benchmark. It provides specific examples and distinguishes from siblings by emphasizing custom time windows, derived metrics, and histogram aggregations.
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?
Explicit guidance on when to use this tool ('Prefer the higher-level tools first' and list of needs) and when not to (queries referencing operational/internal metrics are refused with error reason). Also contrasts with sibling tools.
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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