Toolbooth

Price history

price_history

Read-only

OHLCV candles for a Solana token (by mint, or SOL/BTC/ETH) or a specific pool from GeckoTerminal: minute (1, 5, 15), hour (1, 4, 12) or day candles in USD, oldest first, with a summary — open, close, high and low with their times, % return, % range and total volume. A token is priced in its most-traded pool over 24h (named in the result). For SOL, BTC and ETH, falls back to Coinbase Exchange and then Kraken USD candles when GeckoTerminal is unavailable. Fails (and is not charged) if no source has candles. Costs $0.002 USDC per call.

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": "price_history"
  }
}

Input Schema

Name	Required	Description
`pool`	No	A specific Solana pool address instead of the token's top pool
`limit`	No	Candles to return, newest last, 1–500 (default 100)
`token`	No	Solana token mint address, or SOL, BTC or ETH. Its most-traded pool is used
`aggregate`	No	Candles per bar: minute 1, 5 or 15; hour 1, 4 or 12; day 1 (default 1)
`timeframe`	No	Candle timeframe (default hour)

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 30, 2026

TDQS

A4/5.0

Behavior5/5

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

Goes well beyond the readOnly/openWorld annotations: it discloses how a token is priced (most-traded pool over 24h, named in the result), the Coinbase/Kraken fallback chain, that failures are not charged, and the $0.002 per-call cost. That is exactly the context an agent needs before paying for a call.

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 resource and output shape, then layers sourcing, fallback, failure, and cost in a compact paragraph. Dense but nearly every clause carries information; only minor tightening would improve it.

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 explains the returned summary fields (open, close, high, low with times, % return, % range, volume), ordering, and pool attribution. Combined with fallback and billing behavior, an agent has enough to call it correctly.

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?

Schema coverage is 100%, so each parameter is already fully documented. The description reinforces the token-vs-pool choice, oldest-first ordering, and aggregate mapping, but adds little syntax or constraint detail the schema doesn't already provide.

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?

States a specific verb and resource (OHLCV candles for a Solana token or pool from GeckoTerminal) and specifies granularity and ordering. It implicitly separates itself from spot-price siblings like crypto_price/token_price by promising historical candle series, but never names an alternative, so sibling differentiation is only inferred.

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?

The description explains data-source behavior and the failure condition, but it never says when to reach for this over crypto_price, token_price, or technical_signals. Usage is only implied by the mention of candles and timeframes.

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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