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by io.github.Schoasch
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Get Macro Regime Snapshot

arena_get_macro_regime

What is the macro backdrop doing? Daily Macro Regime snapshot from 18 components in 6 tiers (Liquidity 30%, Financial Conditions 20%, Risk Appetite 15%, Crypto Liquidity 10%, Business Cycle 15%, Inflation/Real Rates 10%). FRED-sourced. Returns composite_score (0-100), regime_label (risk_off/neutral/risk_on_leaning/risk_on), cycle_phase_label (contraction/early_expansion/mid_expansion/late_expansion), matrix_quadrant (sweet_spot/late_cycle_warning/crisis/recovery), tier_scores (6 sub-scores), components (flat key/value of all 18), plus stale_components_detail dating each stale input (last_good_date + age_days + discontinued flag for series the upstream has retired for good) so freshness is quantified, not a vague caveat. Two component keys mean something narrower than their name suggests, so read them carefully: vix_score is the derived 0-100 score (a value of 71 means VIX around 18.6), NOT the VIX index level — the raw Cboe level is not redistributed over this channel; and broad_dollar_index is FRED DTWEXBGS (Broad USD Index, Jan 2006 = 100), NOT the ICE DXY, so readings near 120 are normal. fed_funds_rate is FRED FEDFUNDS, the monthly AVERAGE effective rate (lags; not the daily DFF, not the target range); global_m2_yoy is NOT M2 but the YoY change of G3 central-bank balance sheets (Fed+ECB+BoJ in USD) — it can fall while US M2 hits a record. component_notes carries these definitions in the payload. The former names vix and dxy were removed on 2026-09-01 after their announced deprecation window; consumer_confidence_value went with them (OECD retired the series, frozen since 2024-01-01, never weighted in the composite). [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}
    • removedInput schema / required
      Removed value: -[
-  "context"
-]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses FRED sourcing, stale-component freshness quantification, discontinued flags, and detailed semantic caveats for component keys like vix_score, broad_dollar_index, fed_funds_rate, and global_m2_yoy. It even documents removed legacy names, giving agents strong behavioral grounding.

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 long but front-loaded with the core snapshot summary and output fields. The caveats are dense and mostly necessary given the absence of an output schema, though a few details (deprecation history, 'Free tier') could be trimmed without losing essential guidance.

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?

Because there is no output schema and no annotations, the description must fully specify return values and interpretation. It does: composite_score, regime_label, cycle_phase_label, matrix_quadrant, tier_scores, components, stale_components_detail, and component_notes are all described. It also explains freshness quantification and key naming traps, making it complete for a zero-parameter snapshot tool.

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?

The tool has zero parameters and schema coverage is 100%, so the baseline is 4. The description adds no parameter-level semantics because there are none to document; it instead uses the space to explain output semantics, which is appropriate.

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 opens with a specific question ('What is the macro backdrop doing?') and names a concrete resource: a Daily Macro Regime snapshot built from 18 components in 6 tiers. It clearly distinguishes this from sibling tools by focusing on the composite macro regime output rather than single indicators or strategies.

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 intended use case is clear: retrieve the current macro backdrop as a daily snapshot with composite scores, regime labels, and tier breakdowns. It does not explicitly name alternative tools or state when not to use it, so it stops short of a 5, but the context is unambiguous.

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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