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Agent wallet: what an AI agent with a small daily budget buys

get_agent_wallet
Read-only

Public log of an experiment: an AI agent spends at most a couple of USDC a day on paid x402 resources. Returns the wallet address, the spending rules (per-purchase and daily caps), totals and the latest purchases with price, seller, the agent's stated reason, the Base transaction hash and whether the purchase helped. Read-only.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_agent_wallet"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered and the trailing 'Read-only' is largely redundant. The description earns credit for disclosing what the read actually yields: caps (per-purchase and daily), totals, and per-purchase fields including price, seller, stated reason, Base transaction hash and usefulness. It does not mention rate limits, freshness or pagination, 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.

Conciseness4/5

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

Three sentences, front-loaded with the experiment context, then the return payload. The middle sentence is dense and list-like but every field named is informative rather than padding. Minor waste in restating 'Read-only' after the readOnlyHint annotation.

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

Completeness4/5

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

With no output schema, the description carries the burden of explaining return values and does so thoroughly. It says nothing about response format, currency units beyond USDC, or how many 'latest purchases' are returned, which are the remaining gaps for an agent consuming the result.

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?

The tool takes no parameters, so there is nothing for the description to disambiguate and baseline is a 4. The description instead spends its space on the shape of the returned data, which is a reasonable substitution.

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

Purpose4/5

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

The description gives a concrete verb-and-resource framing: it returns a wallet address, spending rules, totals and latest purchases for a specific agent-spend experiment. That is far more specific than the bare name 'get_agent_wallet', and it implicitly separates the tool from siblings like get_receipts or check_agent_access. It does not explicitly name a sibling it is not, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is only implied: 'Public log of an experiment' tells the agent this is an observational snapshot of an agent's budgeted spending. There is no explicit when-to-use, when-not-to-use, or routing to alternatives such as check_agent_access or get_receipts that might also concern agent activity. Adequate for a zero-parameter getter, but guidance is inferential.

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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