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terminalfeed

by io.github.RipperMercs

tf_premium_correlation_matrix

Pre-computed macro correlation matrix for AI trading and portfolio agents. Returns 30-day Pearson correlations on daily simple returns for 4 FRED series (US gov, public domain): 10Y treasury yield, 2Y treasury yield, trade-weighted USD index, and WTI crude oil. Output includes both a pairs array (sorted by absolute r descending) and an NxN matrix object for easy lookup. Each pair tagged with relationship strength (negligible / weak / moderate / strong) and direction (positive / negative). Costs 2 credits ($0.04 USDC). 30-min cache. Bearer auth required. Note: crypto and equity legs were removed 2026-07-23 for market-data licensing compliance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses cost (2 credits), caching (30-min), authentication (Bearer), data source (FRED public domain), and a licensing compliance note. These add significant context beyond the annotations and clarify the non-read-only nature (credit consumption) without contradiction.

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?

The description is efficiently structured, front-loading the core purpose and then adding pertinent details about output format, cost, cache, auth, and licensing. Every sentence earns its place without redundancy or fluff.

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?

For a zero-parameter, pre-computed tool with no output schema, the description fully specifies expected outputs (sorted pairs array, NxN matrix, strength/direction tags), operational constraints (cost, cache, auth), and data limitations. It leaves no significant gap for an agent to invoke it correctly.

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?

With zero parameters, the schema is fully covered and the description clarifies that no input is needed. The description does not need to explain parameter syntax, but it confirms the tool is pre-computed and requires no user configuration, matching the baseline for 0 params.

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 returns a pre-computed macro correlation matrix with a specific verb ('Returns') and resource (correlations for 4 named FRED series). It distinguishes itself from siblings like tf_premium_macro or tf_economic_data by specifying the exact output (pairs array + NxN matrix) and data scope.

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

Usage Guidelines4/5

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

The description implies usage for AI trading and portfolio agents needing macro correlations, and notes the data coverage and removal of crypto/equity legs. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to decide when to use this tool versus others.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., tf_btc_price vs tf_fear_greed), but there is some overlap between free and premium aggregated tools (e.g., tf_briefing, tf_premium_briefing, tf_premium_agent_context). However, descriptions explicitly differentiate them by content and cost.

Naming Consistency5/5

All tools follow a consistent pattern: 'tf_' prefix (with 'tf_premium_' for premium ones) and snake_case. Names are descriptive and predictable, e.g., tf_btc_price, tf_earthquakes, tf_premium_macro.

Tool Count3/5

27 tools is on the high side, but the server covers a broad domain (crypto, finance, earthquakes, AI trends, payment system, etc.). Each tool serves a specific purpose, so the count is borderline acceptable but feels slightly heavy.

Completeness4/5

The tool surface covers a wide range of data feeds: crypto, forex, macro indicators, earthquakes, HN, HuggingFace, prediction markets, payment system, and service status. Minor gaps exist (e.g., no dedicated stock prices tool beyond premium macro, no weather), but overall it's comprehensive for a terminal feed.

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