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Trading Volatility MCP

Trading Volatility MCP

Official
by tradingvolatility
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_trade_setup

Retrieve a compact trade setup for any ticker: market state plus deterministic recommendation (regime, bias, opportunity score, trade type, direction, entry/stop/target framing, caution flags).

Instructions

Get the compact agent-oriented trade setup for a ticker: market state plus a deterministic trade recommendation (regime, bias, opportunity score/tier, trade type, direction, structures, entry/stop/target framing, and caution flags).

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": {
      "ticker": "<ticker>"
    },
    "name": "get_trade_setup"
  }
}

Input Schema

NameRequiredDescriptionDefault
tickerYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It mentions the output is deterministic and lists content, but it does not explicitly state that the tool is read-only, whether authentication is required, or how it handles invalid tickers. It adds some context but is not fully transparent.

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 a single, dense sentence that front-loads the core verb and resource, then uses a colon to list the output contents. Every phrase adds value with no redundancy. It is concise and well-structured, though slightly run-on.

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

Completeness4/5

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

The description thoroughly enumerates the components of the trade setup, and an output schema is present to explain return structure. Lacking only explicit usage guidance and safety details, it is largely complete for a one-parameter read-only tool.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate. It only echoes the parameter name ('for a ticker') without adding format details, constraints, or examples. While it ties the ticker to the purpose, it provides minimal additional semantics beyond the parameter name.

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

Purpose5/5

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

The description clearly states the tool gets a trade setup for a ticker, listing specific outputs (regime, bias, opportunity score/tier, etc.). It distinguishes itself from siblings like get_ticker_state (state only) and get_signals (signals only) by aggregating market state and a deterministic recommendation into a compact, agent-oriented package.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('compact agent-oriented') but does not explicitly state when to use this tool over alternatives like get_ticker_state or explain_ticker. No direct comparison or exclusions are provided, so the guidance is only implicit.

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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