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trading-mcp-server

trading-mcp-server

by mukul8896
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

calculate_volume_analysis

Analyzes trading volume by comparing to its exponential moving average, detecting volume spikes, and computing a 20-period average for market activity insights.

Instructions

Volume vs its EMA, spike detection, 20-bar 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>"
    },
    "name": "calculate_volume_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
timeframeNoONE_DAY

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.2/5.0
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only lists three aspects of the analysis but does not state whether it is read-only, what side effects exist, or how it handles errors. The behavioral model is insufficient for reliable use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, but it is a fragmented list rather than a cohesive sentence. It could be more structured while maintaining 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?

Given the tool has 2 parameters, no output schema, and no annotations, the description is incomplete. It does not describe return format, spike detection criteria, or the baseline for the 20-bar average, leaving significant gaps for an AI agent.

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%, and the description does not mention or explain any parameters (symbol, timeframe). It fails to add meaning beyond the schema, leaving the agent guessing how inputs affect the analysis.

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 mentions volume vs its EMA, spike detection, and 20-bar average, indicating a volume analysis tool. However, it is fragmented and lacks a clear verb+resource structure, making it somewhat vague. It does not explicitly state that it computes or returns a volume analysis, but it distinguishes from siblings by focusing solely on volume.

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 like other indicators (e.g., calculate_rsi, calculate_atr). There is no mention of prerequisites, contexts, or when not to use it.

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

Deploy Server

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