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Alpha Vantage MCP Server

Alpha Vantage MCP Server

by calvernaz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

time_series_monthly

Retrieve historical monthly stock price data for financial analysis and market trend evaluation using Alpha Vantage's comprehensive dataset.

Instructions

Fetch a time series 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>"
    },
    "name": "time_series_monthly"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
datatypeNo

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 fails to describe any behavioral traits like rate limits, authentication needs, data format, or potential side effects. This is inadequate for a tool with no annotation coverage.

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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly, though this conciseness comes at the cost of detail.

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 tool's complexity (2 parameters, 0% schema coverage, no annotations, no output schema, and many sibling tools), the description is incomplete. It lacks essential details on purpose differentiation, usage guidelines, parameter semantics, and behavioral traits, making it insufficient for effective tool 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 'datatype' are undocumented in the schema. The description adds no meaning beyond the schema, offering no explanation of what these parameters represent, their expected values, or how they affect the fetch operation. This leaves the agent guessing about parameter usage.

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 a time series monthly' states a verb ('fetch') and resource ('time series monthly'), but it's vague about what exactly is fetched (e.g., financial data, metrics) and doesn't distinguish it from sibling tools like 'time_series_daily' or 'time_series_monthly_adjusted'. It provides a basic purpose but lacks specificity and differentiation.

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 such as 'time_series_daily' or 'time_series_monthly_adjusted'. The description offers no context, exclusions, or prerequisites, leaving the agent without direction on tool selection among similar siblings.

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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