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Alpha Vantage MCP Server

Alpha Vantage MCP Server

by calvernaz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

digital_currency_monthly

Fetch monthly historical data for digital currencies to analyze long-term price trends and market performance using Alpha Vantage financial data.

Instructions

Fetch digital currency monthly

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>",
      "market": "<market>"
    },
    "name": "digital_currency_monthly"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
marketYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.1/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'fetch', implying a read operation, but doesn't cover critical aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., data format, pagination). This is inadequate for a tool with no annotation support.

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?

The description is extremely concise with a single phrase 'Fetch digital currency monthly', which is front-loaded and wastes no words. However, this conciseness comes at the cost of completeness, as noted in other dimensions.

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

Completeness1/5

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

Given the complexity (a data-fetching tool with 2 required parameters), no annotations, 0% schema coverage, and no output schema, the description is highly incomplete. It lacks essential details on behavior, parameters, output, and usage context, making it insufficient for effective tool selection and invocation.

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

Parameters1/5

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

Schema description coverage is 0%, meaning parameters 'symbol' and 'market' have no documentation in the schema. The description adds no information about these parameters, such as what they represent (e.g., currency symbol like BTC, market like USD), expected formats, or examples. This fails to compensate for the lack of schema details.

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

Purpose3/5

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

The description 'Fetch digital currency monthly' states a verb ('fetch') and resource ('digital currency monthly'), providing a basic purpose. However, it's vague about what 'monthly' means (e.g., monthly data, monthly updates) and doesn't distinguish it from siblings like 'digital_currency_daily' or 'digital_currency_weekly' beyond the time period in the name.

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 is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like 'digital_currency_daily' or 'digital_currency_weekly' for different timeframes, or other tools like 'crypto_intraday' for intraday data, leaving usage context unclear.

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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