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QuickBuild agent tools (x402)

Base transaction status

base_tx_status
Read-onlyIdempotent

[PAID $0.002 USDC per call via x402 on Base; errors are free] Look up one transaction on Base mainnet by hash: status (success, failed or pending), block, timestamp, confirmations, from/to, value, method id, gas used, effective gas price, fee split into L2 execution and L1 data fee (ETH and USD), contract created, and decoded ERC-20 Transfer events (token, symbol, from, to, amount; up to 20). Useful to confirm a payment landed. Read live from public Base RPC. All params optional: defaults to a confirmed USDC x402 payment. Unknown hash = 404, invalid hash = 400, never charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hashNoTransaction hash (0x + 64 hex). Optional: defaults to 0x20215b4a54f0a39995f3afbd748d7b52c3a1922685ddf08e5f694ea1e89b52bf.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by disclosing pricing ($0.002 USDC via x402), that errors are free and unknown/invalid hashes are never charged, the specific 404/400 error semantics, and that data is read live from a public Base RPC. These are operationally critical traits an agent cannot infer from annotations or schema.

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

Conciseness4/5

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

Front-loads the pricing/error bracket, then the purpose, then the return fields, then the defaults and error codes — well ordered with no filler. It is dense and reads as a long run-on, so slightly below ideal, but each clause carries distinct information.

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?

With no output schema, the description compensates by enumerating the return fields (status values, fee split, decoded ERC-20 events capped at 20) and covers error behavior and the default request. An agent has everything needed to call it correctly and interpret results.

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

Parameters4/5

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

Schema coverage is 100%, so the hash format is documented, but the description adds meaning the schema lacks by explaining that the default value is a confirmed USDC x402 payment (the schema only shows the raw hex). That clarifies why the parameter can be omitted and what result to expect.

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?

States a specific verb and resource ('Look up one transaction on Base mainnet by hash') and enumerates exactly what is returned (status, block, confirmations, fee split, decoded Transfer events). This clearly distinguishes it from siblings like base_gas_price or base_wallet_balances, which cover different resources.

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?

Gives clear context ('Useful to confirm a payment landed') and notes the notable default behavior (all params optional; defaults to a confirmed USDC x402 payment). It stops short of stating explicit when-not-to-use or naming an alternative sibling, but the usage context is unambiguous.

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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