call402 — x402 Agent Services

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

With no annotations, the description carries the full burden and does meaningful work: it discloses determinism ('same input, same output'), the latency profile ('in milliseconds'), and that no LLM call is involved — all traits an agent cannot infer from the empty schema. It does not cover failure modes, input size limits, or behavior when no entities match, which keeps it out of 5 territory.

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?

Front-loaded with the core verb-resource-output framing, then the positioning, then the supported entity list. The enumeration of extraction types is long but directly aids tool selection, so it earns its place; a slight trim would make it tighter.

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?

There is no output schema, so the description must describe returns — and it does ('strict, machine-readable JSON', 'exactly the fields you need'). Combined with the stated determinism and supported entity types, an agent has enough to call it correctly, though input limits and empty-match behavior remain unaddressed.

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 0%, so the description must compensate for three bare parameters. It explains the intent of the two optional ones ('pass fields or a JSON schema to receive exactly the fields you need') and implies 'text' is the source input, but never states the expected shape of 'fields' (array of strings) or 'schema' (a JSON schema object), leaving the schema's type-only definitions to do that work.

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?

States a concrete verb and resource ('Turn unstructured text into strict, machine-readable JSON') plus the exact extraction categories it supports. The positioning as 'a deterministic, reproducible alternative to an LLM extraction call' makes it unmistakable against siblings like web-search or token-risk-summary, which do entirely different things.

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?

Gives clear context for when to reach for it: agent pipelines needing reproducible structured output from invoices, emails, messages, logs or documents, and the 'pass fields or a JSON schema' instruction tells the agent how to steer results. It stops short of explicit when-not-to-use guidance or naming an alternative tool to use instead.

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

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

With no annotations, the description carries the full burden and does well: it discloses the data sources (Honeypot.is simulation, GoPlus audit, DexScreener liquidity/volume/pair age, on-chain checks) and the composite outputs (0-100 risk_score, risk_level, flags). It does not mention auth needs, rate limits, or latency, so it stops short of a 5.

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?

Front-loaded with the core question and the returned artifact, with no wasted preamble. The sentence is long and packs several source names, but each clause adds decision-relevant information rather than filler.

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, and the description duly enumerates the return shape (risk_score, risk_level, flags) and supported chains. For a two-parameter tool this is nearly complete; only address format/expectations are left unstated.

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 0%, so the description must compensate. The chain parameter is effectively covered by 'any token on Base or Ethereum', matching the enum. But the required 'address' parameter is never clarified as the ERC-20 contract address, leaving a real gap for the most important input.

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?

States a specific verb (pre-trade safety check) and resource (ERC-20 token), then enumerates exactly the questions it answers: honeypot, buy/sell taxes, mintable supply, reclaimable ownership, liquidity depth. It also names the two supported chains, so an agent can distinguish this from the unrelated siblings (web-search, wallet-activity-summary).

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 says when to use it: 'Run it before swaps, listings or purchases.' That is clear context for invocation. However, it names no alternative tool and gives no when-not conditions, which would be needed for a 5.

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

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

With no annotations, the description carries the full burden and does well: it names the data sources (Blockscout, Etherscan) with an on-chain RPC fallback and discloses that responses state sources and data_quality, which tells the agent how much to trust the result. It does not mention rate limits, auth needs, or pagination, so it is not exhaustive.

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 purpose and return contents are front-loaded in the first sentence, with use cases second and provenance third. The opening sentence is dense and list-heavy, but no sentence is filler, so it is close to optimally sized.

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 two-parameter read tool with no output schema or annotations, the description covers return contents (balance, transactions, holdings, anomaly flags) and data-quality caveats well. The remaining gap is the optional chain parameter's default behavior, which an agent would have to guess.

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 0% for two parameters, so the description must compensate. It implies the chain enum's scope by saying 'Ethereum or Base' and clarifies the subject is 'any EVM address', but adds no format or default-chain detail for the required address parameter and never states what happens if chain is omitted.

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 states a concrete verb+resource ('Wallet due diligence for any EVM address on Ethereum or Base') and enumerates exactly what is returned: native balance, transactions with counterparties, token holdings with USD values, and anomaly flags. This scope cleanly separates it from siblings like token-risk-summary and web-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?

It lists concrete use cases ('counterparty screening, airdrop eligibility checks, portfolio snapshots, or checking a wallet before transacting'), giving clear when-to-use context. It stops short of naming an alternative tool or stating when-not to use it, so it falls short of a 5.

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

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the provider (Exa), that results are ranked, the response fields, and crucially the auth/payment model ('No Exa account, API key or subscription needed: pay per call in USDC via x402'). It stops short of rate limits, failure modes, or result-capping behavior.

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?

Front-loaded with the core capability, then return contents, then use cases, then the payment caveat. Dense but nearly every clause adds information; the marketing-flavored positioning phrase ('for AI agents, powered by Exa') is the only near-waste.

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 two-parameter search tool with no output schema and no annotations, the description covers purpose, output shape, usage contexts, and the unusual payment requirement. The remaining gap is the undocumented num_results parameter and any error/pagination behavior, which an agent could plausibly need.

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 0% and annotations supply nothing, so the description must compensate. It clarifies that 'query' is natural-language and free-form, which adds real meaning, but it never mentions 'num_results' at all — no default, no maximum, no range — leaving half the parameters undocumented anywhere.

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?

States a specific verb+resource ('Live web search ... send a natural-language query and get ranked results') and even enumerates the return payload (titles, URLs, publish dates, text highlights). An agent knows exactly what this does without opening the schema, and the sibling tools (text-to-structure, token-risk-summary, wallet-activity-summary) are in unrelated domains, so no differentiation burden applies.

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?

Gives concrete when-to-use contexts: 'research, fact-checking, news and docs lookups, price and competitor checks inside agent workflows.' That is clear positive guidance, though it names no exclusion or alternative tool for cases where a web search is the wrong choice.

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