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Alchemy MCP Server

Alchemy MCP Server

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by alchemyplatform
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fetchTokenPriceHistoryByTimeFrame

Retrieve historical token prices using natural language time frames like 'last week' or 'past 7 days', with optional interval selection.

Instructions

Get historical token prices using natural language time frames like "last week" or "past 7 days"

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>",
      "timeFrame": "<timeFrame>"
    },
    "name": "fetchTokenPriceHistoryByTimeFrame"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYesThe token symbol to query. e.g. "BTC" or "ETH"
timeFrameYesTime frame like "last-week", "past-7d", "ytd", "last-month", etc. or use natural language like "last week"
intervalNoThe interval to query. e.g. "1d" or "1h"1d
useNaturalLanguageProcessingNoIf true, will interpret timeFrame as natural language

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It does not disclose important details such as whether the tool is read-only, what happens with invalid time frames, or that the NLP feature requires setting a boolean flag to true. The description implies natural language is always used, conflicting with the schema default.

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?

Single sentence, concise and front-loaded. Could be improved by adding brief usage context or behavioral notes without losing conciseness.

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

Completeness2/5

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

Despite the tool having 4 parameters and no output schema, the description is too brief. It does not describe the return format, error handling, or necessary steps to use NLP. Missing key context for effective invocation.

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 baseline is 3. The description adds value by explaining the natural language capability hinted by the timeFrame parameter. However, it does not elaborate on the interval or NLP flag beyond what the schema already provides.

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?

Clearly states the tool gets historical token prices using natural language time frames. Provides examples like 'last week' and 'past 7 days'. However, it does not explicitly differentiate from the sibling tool fetchTokenPriceHistoryBySymbol, which likely provides similar functionality without NLP.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. Does not mention that fetchTokenPriceHistoryBySymbol might be simpler for exact dates, or that the NLP flag should be set for natural language interpretation. Leaves the agent to infer usage context.

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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