nonceCurrent transaction nonce for any address on Base or Ethereum. Agent tx bots need this before sending transactions. [price: $0.001/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | base (default) or ethereum | |
| address | Yes | EVM address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It implies a read-only lookup and discloses pricing, but it does not explicitly state that it has no side effects, whether authentication is required, rate limits, or what the exact response format is. For a simple getter this is minimally adequate but not rich.
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 substantive sentences plus a price note, with no wasted words. The core purpose is front-loaded, followed by the usage trigger and pricing.
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 two-parameter lookup, the description covers what the tool returns, the supported chains, and when to call it. The main gap is the lack of an explicit return-value shape, especially given that no output schema is provided.
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 description coverage is 100%, so the schema already documents address and chain. The description adds 'Base or Ethereum' and the tx-bot use case, but does not add parameter-level detail beyond the schema.
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 the tool as returning the current transaction nonce for a given address on Base or Ethereum, which is a specific query. It doesn't explicitly differentiate from sibling tools, but none of the listed siblings competes for this exact function, so the core purpose is unambiguous.
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?
States when to use it: 'Agent tx bots need this before sending transactions.' This gives a clear, actionable trigger. It doesn't name alternatives or exclusions, but no sibling tool offers a nonce lookup, so this context is sufficient.
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.
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.