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Business cycle

rayana_ai_macro
Read-only

The economy behind the market, descriptive only. cycle: the business-cycle phase (expansion / slowdown / contraction / recovery), the composite and its components, plus recent month-ends. earnings: S&P 500 (index="spx") or Nasdaq-100 profit growth by quarter (sum and median, net and operating income), breadth, beat rate and coverage, including the quarter in season. indicators: the FRED series behind the cycle with source citations. history sets the window (month-ends, quarters or observations); omit it for the default.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "rayana_ai_macro"
  }
}

Input Schema

NameRequiredDescriptionDefault
viewNocycle
indexNospx
historyNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the main added value is the phrase 'descriptive only', which reinforces that nothing is computed or mutated, plus the per-view disclosure that earnings includes breadth, beat rate, coverage and the in-season quarter. Return shape is left to the output schema, which is acceptable.

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?

Front-loaded with the one-line purpose, then structured as a view-by-view breakdown with backticked parameter names. Dense but nearly every clause carries information; the run-on earnings sentence is the only readability cost.

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

Completeness5/5

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

With an output schema present and no required parameters, the description covers all that an agent needs: what each view returns, how `index` and `history` behave, and the default. Nothing material is missing for correct invocation.

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

Parameters4/5

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

Schema coverage is 0%, so the description must carry the parameter meaning, and it largely does: it explains `view` values, states `index="spx"` maps to S&P 500 with Nasdaq-100 as the alternative, and defines `history` as the window in month-ends, quarters, or observations. The ndx value is only implied, and `history` units per view could be sharper.

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

Purpose4/5

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

The description opens with a concrete framing ('The economy behind the market, descriptive only') and then defines the three views: cycle phase, earnings/profit growth, and FRED indicators. That is enough to separate it from siblings like rayana_ai_market_brief or rayana_ai_rotation, though no sibling is named explicitly.

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?

It tells the agent to omit `history` for the default and implies which view to pick for which question, but it never states when to prefer this tool over rayana_ai_market_brief, rayana_ai_rotation, or rayana_ai_trends. Usage is inferable rather than prescribed.

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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