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AgentPay x402 — Economic-intelligence layer for AI Agents

token_price

Read-onlyIdempotent

Fetch the current USD price, 24-hour change, and market cap for any cryptocurrency symbol. Use it to check live crypto values before payment or trading decisions.

Instructions

Get the current USD price of any cryptocurrency token

Use when: You need the current USD price, 24h change, or market cap of any cryptocurrency. Not for: you need volume, ATH or a pair quote — token_market_data; a one-call macro + crypto overview — market_snapshot; pool depth or slippage — orderbook_depth. Returns: price_usd, change_24h_pct, market_cap_usd, coin_id Example response: {"symbol": "ETH", "price_usd": 2069.73, "change_24h_pct": -4.04, "market_cap_usd": 250330787714.19, "source": "coingecko"}

Price: free. Read-only live public data; no API key, nothing signed or spent. Fails with an error message on an unknown symbol or an unreachable upstream source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesToken symbol, e.g. BTC, ETH, SOL

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.1
  2. Removedv0.3.0
  3. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context beyond them: it is free, needs no API key, nothing is signed or spent, and it fails with an error on an unknown symbol or unreachable upstream. It does not state rate limits or caching, which keeps it 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.

Conciseness5/5

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

Front-loaded with purpose, then structured 'Use when / Not for / Returns / Example' blocks. Every sentence earns its place; the example response and failure notes are compact and non-redundant.

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?

No output schema exists, but the description enumerates return fields (price_usd, change_24h_pct, market_cap_usd, coin_id) and gives a concrete example response, plus error behavior. An agent has everything needed to call and interpret it.

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

Parameters3/5

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

Only one parameter (symbol) at 100% schema description coverage, so the schema already documents it with an example (BTC, ETH, SOL). The description adds nothing about symbol format or case-sensitivity, matching the baseline 3 when the schema does the work.

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 ('Get the current USD price of any cryptocurrency token') and immediately names the sibling it is not, routing volume/ATH/pair quotes to token_market_data. An agent can distinguish it from token_market_data, market_snapshot and orderbook_depth without opening any schema.

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

Usage Guidelines5/5

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

Explicit 'Use when' (needs price, 24h change, market cap) and 'Not for' clauses that name the three alternative tools and the exact conditions selecting each. Nothing is left to inference.

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