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Alpha Vantage MCP Server

Alpha Vantage MCP Server

by calvernaz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

sar

Calculate Parabolic SAR to identify potential trend reversals in stock prices using Alpha Vantage market data.

Instructions

Fetch parabolic sar

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>",
      "interval": "<interval>"
    },
    "name": "sar"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
intervalYes
monthNo
accelerationNo
maximumNo
datatypeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

D1.6/5.0
Behavior1/5

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

No annotations are provided, so the description must fully disclose behavioral traits. 'Fetch' implies a read-only operation, but it doesn't specify data sources, rate limits, authentication needs, or what 'parabolic sar' entails (e.g., calculation method, output format). This leaves critical behavioral aspects undocumented.

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?

The description is extremely concise with just three words, but this brevity leads to under-specification rather than efficient communication. It's front-loaded but lacks necessary detail, making it inefficient for its purpose.

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 (6 parameters, no annotations, no output schema), the description is severely incomplete. It doesn't explain the tool's purpose, usage, behavior, or parameters, failing to provide adequate context for an AI agent to use it correctly.

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%, so the description must compensate by explaining parameters. It mentions no parameters, leaving all 6 (e.g., 'symbol', 'interval', 'acceleration') undocumented. This fails to add any meaning beyond the bare schema.

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

Purpose2/5

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

The description 'Fetch parabolic sar' restates the tool name 'sar' (parabolic SAR is a technical indicator), making it tautological. It doesn't specify what resource is being fetched (e.g., financial data for a symbol) or distinguish it from sibling tools like 'sma' or 'rsi' that also fetch technical indicators.

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

Usage Guidelines1/5

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. With many sibling tools for technical indicators (e.g., 'rsi', 'macd'), the description offers no context, prerequisites, or exclusions to help an agent choose appropriately.

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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