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Ratio Chart — preset pair facts

get_ratio_pair
Read-only

Closelooknet’s Ratio Chart presets: relative-performance facts for ten pairs — India vs US (Nifty 50 / S&P 500, INDA / SPY), EM vs US (EEM / SPY), developed ex-US vs US (EFA / SPY), Japan vs US (Nikkei / S&P), growth without tech (QQXT / QQQ), health care vs tech (XLV / XLK), low-vol vs momentum (SPLV / SPMO), gold vs US equities (GLD / SPY), bitcoin vs S&P 500. Per pair: current ratio, 1y change, all-time low/high with dates, percentile in its own history, distance from the low, lowest-since date. Weekly since each pair’s first common date. Mirrors closelook.net/lab/ratio/ (where any pair can be charted).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pairNoPreset slug (nifty-spx, inda-spy, eem-spy, efa-spy, n225-spx, qqxt-qqq, xlv-xlk, splv-spmo, gld-spy, btc-spx) or omit for all

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=true, so the safety profile is covered. The description adds real behavioral context beyond that: it is a fixed preset set (not arbitrary pairs), values are weekly since each pair's first common date, and the exact per-pair fields (current ratio, 1y change, all-time low/high with dates, percentile, distance from low). It omits pagination/rate-limit detail but adds substantive scope and cadence information.

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-loads the domain and coverage up front, then lists the facts returned. It is dense but every clause carries information; the pair enumeration overlaps somewhat with the schema's slug list, which keeps it just short of a 5.

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?

With no output schema, the description carries the burden of describing return content, and it does so thoroughly (per-pair fields, weekly resolution, historical scope). It does not describe the response envelope or error/empty behavior, but an agent has enough to call and interpret it correctly.

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 the schema already documents the slug-or-omit behavior, so the baseline is 3. The narrative pair list partially maps names to slugs but adds little beyond what the schema's enum-like description already provides.

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?

States a specific verb+resource (relative-performance ratio facts for ten preset pairs) and enumerates exactly what is returned per pair. It is clearly distinguishable from siblings like compare_indices or get_index_snapshot because it is charted ratio data, not index levels.

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 through 'relative-performance facts for ten pairs' and the closelook.net/lab/ratio/ mirror, but never states when to choose this over compare_indices, get_price_history, or get_index_snapshot. It gives context but no explicit when/when-not or alternative routing.

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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