# ADH Macro Synthesis Intelligence Layer > ADH Macro Synthesis Intelligence Layer is a paid API for AI agents from agentdatum.com, paid per call via x402, $0.3/call, status unknown (last checked 2026-09-15). Returns a synthesized US macroeconomic intelligence report combining Fed policy, Treasury rates, gold, CPI, and GDP into regime classifications with provenance and confidence scores ## Facts - Endpoint: GET https://agentdatum.com/api/v1/d/l5-macro-synthesis - Price: $0.3/call - Payment: x402 - Status: unknown - Last checked: 2026-09-15 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/adh-macro-synthesis-intelligence-layer-6c5d9575 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_DYFOxa0QsBUUOBooLon4o Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently. ## How to call it through Zero Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`): ```sh zero fetch --capability adh-macro-synthesis-intelligence-layer-6c5d9575 ``` Example prompt: Give me the current US macro regime snapshot — I need to know whether we're in expansion or contraction, what the Fed's policy stance is, whether the yield curve is normal or inverted, and what the real interest rate looks like right now. ## When to prefer this Choose this endpoint when you need a pre-synthesized, multi-source US macro intelligence layer rather than fetching and joining raw FRED series yourself. It saves significant computation by delivering regime classifications, real rates, and policy stance with provenance already resolved across 239 sources. Prefer it over raw economic data endpoints when your agent needs decision-ready macro context (e.g. regime-conditional portfolio logic, risk-on/risk-off signals) without building your own ETL pipeline. ## Known failure modes - Upstream data source unavailability may return null raw fields with degraded confidence scores - Stale data if FRED or Treasury feeds are delayed — check schema timestamp - Payment failure via x402 returns 402 with payment details required - Schema version mismatch if consuming older intel-l5 schema versions - Truncated provenance on large response payloads ## How this service works ADH 金融情报层：把 239 个原始数据源洗成带实体解析、时间同步、溯源与派生结论的情报（宏观regime / 加密尽调 / 事件窗口），免费注入你的 AI 上下文。Agent 走 x402 结算，人类订阅（PayPal）即将上线。 ## Output A structured JSON intelligence object containing: macro_regime label (e.g. 'goldilocks_expansion'), policy_stance with hawk/dove scores, real_rate (nominal 10Y minus CPI YoY), curve_shape (normal/inverted with spread), real_gold spot price with CPI-adjusted purchasing power index and 4-week momentum, growth_momentum from GDP, inflation_regime classification, fiscal_cost_pressure level, and full provenance chain showing which of 239 raw sources contributed to each derived field, with per-field confidence scores. ## Request schema (JSON Schema) ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "input": { "type": "object", "properties": { "type": { "type": "string", "const": "http" }, "method": { "type": "string", "enum": [ "GET", "HEAD", "DELETE" ] }, "queryParams": { "type": "object", "properties": {}, "required": [] } }, "required": [ "type", "method" ], "additionalProperties": false }, "output": { "type": "object", "properties": { "type": { "type": "string" }, "example": { "type": "object" } }, "required": [ "type" ] } }, "required": [ "input" ] } ``` ## Response schema (JSON Schema) ```json { "type": "json", "example": { "raw": null, "title": "美国宏观跨源合成（Fed×Treasury×Gold×CPI×Macro）", "inputs": [ "econ-fred", "econ-fred" ], "schema": "intel-l5-2026-08-12", "derived": { "real_gold": { "spot": 4340.7002, "value": 4340.7002, "sources": [ "data/sig-gold:raw.signals.commodities__GC_F", "data/sig-gold:raw.signals.commodities__GC_F" ], "confidence": 0.9, "momentum_4w": 0.0734, "transformation": "L5_derive: GC_F spot + 4w momentum, CPI-adjusted", "real_purchasing_power_idx": 4195.39 }, "real_rate": { "unit": "pct", "value": 1.226, "cpi_yoy": 3.464, "sources": [ "data/econ-fred:raw.series.DGS10", "data/econ-fred:raw.series.DGS10" ], "confidence": 0.9, "nominal_10y": 4.69, "transformation": "L5_derive: DGS10 - CPIAUCSL_yoy" }, "curve_shape": { "value": "normal", "sources": [ "data/econ-fred:raw.series.T10Y2Y" ], "confidence": 0.95, "spread_pct": 0.46, "transformation": "L5_derive: sign(T10Y2Y)" }, "macro_regime": { "value": "goldilocks_expansion", "sources": [ "data/econ-fred:GDP", "data/econ-fred:GDP" ], "components": { "growth": "expanding", "policy_rate_pct": 3.63, "unemployment_pct": 4.1, "inflation_yoy_pct": 3.46 }, "confidence": 0.85, "transformation": "L5_derive: multi-series regime classification" }, "policy_stance": { "value": "neutral", "sources": [ "data/fed-policy:rate_outlook", "data/fed-policy:rate_outlook" ], "chairman": "Kevin Warsh", "confidence": 0.7, "dove_score": 0, "hawk_score": 0, "transformation": "L5_derive: NLP stance from rate_outlook+ai_analysis" }, "growth_momentum": { "value": 2.161, "sources": [ "data/us_macro:gdp_growth_pct" ], "prev_year": 2.793, "confidence": 0.85, "transformation": "L5_derive: latest vs prior year GDP growth" }, "inflation_regime": { "value": "low", "sources": [ "data/us-cpi:raw.recent" ], "confidence": 0.85, "cpi_yoy_pct": 0.119, "transformation": "L5_derive: inflation level classification" }, "fiscal_cost_pressure": { "value": "moderate", "sources": [ "data/treasury-rates:data[*].avg_interest_rate_pct" ], "confidence": 0.88, "transformation": "L5_derive: mean(avg_interest_rate_pct) regime", "avg_interest_rate_pct": 3.417 } }, "category": … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/adh-macro-synthesis-intelligence-layer-6c5d9575/health.json - [Zero catalog index](https://www.zero.xyz/llms.txt) - [Other services from agentdatum.com](https://www.zero.xyz/host/agentdatum.com/llms.txt)