Skip to main content
Glama
Sign In
Alpha Vantage MCP Server

Alpha Vantage MCP Server

by calvernaz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

mama

Calculate the MESA Adaptive Moving Average for financial assets to identify trend changes and generate trading signals based on price data.

Instructions

Fetch MESA adaptive moving average

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>",
      "series_type": "<series_type>",
      "fastlimit": "<fastlimit>",
      "slowlimit": "<slowlimit>"
    },
    "name": "mama"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
intervalYes
monthNo
series_typeYes
fastlimitYes
slowlimitYes
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 what the tool does ('fetch'), without mentioning any behavioral traits such as data sources, rate limits, authentication needs, error handling, or output format. This is inadequate for a tool with 7 parameters and no output schema.

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 sentence, 'Fetch MESA adaptive moving average', which is front-loaded and wastes no words. However, this conciseness comes at the cost of completeness, as it omits necessary details for effective tool use.

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 (7 parameters, no annotations, no output schema, and many sibling tools), the description is severely incomplete. It doesn't cover parameter meanings, usage context, behavioral aspects, or output expectations, making it inadequate for an AI agent to select and invoke this tool correctly among alternatives.

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?

The description adds no meaning beyond the input schema, which has 0% description coverage. With 7 parameters (5 required) like 'symbol', 'interval', 'fastlimit', and 'slowlimit', the description doesn't explain what these parameters mean, their expected formats, or how they affect the MESA adaptive moving average calculation, leaving them entirely undocumented.

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 states the tool fetches a MESA adaptive moving average, which is a specific verb ('fetch') and resource ('MESA adaptive moving average'), providing a clear purpose. However, it doesn't distinguish this from sibling tools like 'sma', 'ema', or 'wma', which also fetch moving averages, leaving ambiguity about when to use this specific variant.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for technical indicators (e.g., 'sma', 'ema', 'wma', 'dema', 'tema', 'kama'), there's no indication of what makes MESA adaptive moving average unique or in what contexts it's preferred, leaving the agent to guess based on the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools