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AlgoChains MCP Server

AlgoChains MCP Server

Official
by AlgoChains
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_macro_signals

Read-onlyIdempotent

Retrieve macro alpha signals for yield curve shape, credit spreads, DXY momentum, PMI regime, and VIX term structure using real FRED/CBOE/Polygon data to inform trading decisions.

Instructions

Get pre-computed macro alpha signal fabric: yield curve shape (2y-10y), credit spreads (HY-IG), DXY momentum, PMI regime, VIX term structure contango/backwardation. All from real FRED/CBOE/Polygon APIs.

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": "get_macro_signals"
  }
}

Input Schema

NameRequiredDescriptionDefault
signalsNoSubset: yield_curve, credit_spreads, dxy, pmi, vix. Omit for all.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv22.7.1

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds value by stating signals are pre-computed and sourced from real APIs (FRED, CBOE, Polygon), providing context beyond structured annotations.

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?

Two sentences, efficiently listing signals and sources without extraneous text. Every sentence adds value.

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

Completeness3/5

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

Description covers the tool's purpose and available signals, but lacks details on return format or structure. Since no output schema exists, more information about the output would improve completeness.

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

Parameters3/5

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

Schema coverage is 100% and already lists allowable signal values. The description enumerates signals but does not add meaning beyond the schema's parameter description.

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?

Description clearly states the tool retrieves specific macro alpha signals (yield curve, credit spreads, DXY, PMI, VIX) from known APIs. The verb 'Get' and resource 'pre-computed macro alpha signal fabric' are specific and distinguish it from other get_* tools like get_fed_policy_signals.

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 explicit guidance on when to use this tool versus alternatives. The description does not mention conditions, exclusions, or recommend it over similar tools like get_us_economic_indicators.

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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