AlphAI

Macro tape (market-wide)

alphai_macro

Read-onlyIdempotent

The market-wide macro tape from the last N hours: central-bank decisions and statements (Fed/FOMC), inflation and jobs prints, commodities, geopolitics — one call, novelty-first, so on a release day the release itself leads and the commentary follows. Use this for 'what did the Fed say', 'any macro news today', 'what's moving the whole market'. Macro rows are market-wide events and mostly carry NO tickers — that is by design; each item still carries the full AI analysis inline. Narrow with categories, or raise min_relevance to 7 to keep roughly the release-day events and drop the commentary. Syndicated reprints are collapsed by story (dedupe=false to keep all), and each collapsed item reports story_id (the story root's uid — the same key alphai_trending and the search tools report), sources_count and sources; sources_count is usually 1, a value above 1 is the signal. For what is COMING — the scheduled release calendar — use alphai_calendar. Informational and AI-generated — not investment advice.

Input Schema

TableJSON Schema

Name	Required	Description
`hours`	No	Look-back window in hours; default 24.
`limit`	No	Items; capped at 20 Free/Basic, 50 Pro.
`dedupe`	No	Collapse syndicated reprints by story (default true).
`categories`	No	Subset of the macro categories; default all three.
`min_relevance`	No	Min AI relevance 1-10; default 4. 7+ keeps release-day events.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 23, 2026

addedOutput schema / properties / result / items / properties / search_match

Added value: +{
+  "anyOf": [
+    {
+      "description": "Per-item evidence from the text path: ``score`` orders the page (cover\ndensity × recency; comparable within one response, not a probability),\n``terms_matched`` is set in broadened mode, ``context`` marks the matched\nwords in the summary with ``**``.",
+      "properties": {
+        "context": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        },
+        "score": {
+          "type": "number"
+        },
+        "terms_matched": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        }
+      },
+      "required": [
+        "score"
+      ],
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}

Changed1 schema field changedSep 22, 2026

addedOutput schema / properties / result / items / properties / filing

Added value: +{
+  "anyOf": [
+    {
+      "description": "Structured SEC 8-K filing block (``source_type == \"sec_form8k\"`` items on\nthe news tools).\n\nA read-only projection of the 8-K sidecar\n(``apps.data.models.EightKEventModel``) — same fields and semantics as the\nREST ``/api/news/`` block (openapi.yaml: ``EightKFiling``): ``items`` is\nevery item code the filing carries (the ``item`` filter matches any of\nthem; empty for a header-only filing), ``primary_item`` the one that drove\nthe category, ``accession_number`` the id to cite, ``filed_at`` EDGAR's\nacceptance time, ``event_date`` the filing's own date of report and\n``exhibit_url`` the press-release exhibit the summary was built from.\nDates are ISO strings like the insider block; empty sidecar strings are\n``null``, never ``\"\"``.",
+      "properties": {
+        "accession_number": {
+          "type": "string"
+        },
+        "event_date": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        },
+        "exhibit_url": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        },
+        "filed_at": {
+          "type": "string"
+        },
+        "items": {
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        },
+        "primary_item": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        }
+      },
+      "required": [
+        "items",
+        "accession_number",
+        "filed_at"
+      ],
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}

AddedSep 6, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds substantial context beyond that: the novelty-first ordering, collapse of syndicated reprints via dedupe, the significance of sources_count>1 as a signal, that rows mostly carry no tickers by design, and each item includes full AI analysis inline. It also discloses the 'not investment advice' disclaimer. This goes well beyond the structured metadata.

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 a single dense paragraph of about 150 words. Every sentence carries functional meaning—scope, use cases, dedupe behavior, source signal, and pointer to calendar. It is front-loaded with the core purpose and flows logically. While it is lengthy, the density justifies the length; only a slight structure split would make it a 5.

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?

Given that an output schema exists, the description need not detail return fields, yet it still mentions story_id, sources_count, and inline AI analysis, which are useful for interpretation. It covers parameter tuning, behavioral nuances, and explicitly points to the calendar for upcoming events. Combined with the safe annotations, an agent has everything needed to call this tool correctly under different intents.

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

Parameters5/5

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

Schema coverage is 100%, so the baseline is 3, but the description significantly enriches parameter semantics. For example, it explains that raising min_relevance to 7 keeps roughly the release-day events and drops commentary, and it clarifies that dedupe=false keeps all syndicated reprints and that story_id matches the key alphai_trending and search tools report. These interpretations are not in the schema, adding real value.

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 states a specific verb and resource: it returns the 'market-wide macro tape' covering central-bank decisions, inflation/jobs prints, commodities, and geopolitics. It clearly distinguishes itself from sibling tools by noting that macro rows are market-wide and carry no tickers, and it explicitly routes to alphai_calendar for upcoming events. The 'novelty-first' behavior adds specificity.

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

Usage Guidelines5/5

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

It gives explicit use cases ('what did the Fed say', 'any macro news today') and names an alternative ('For what is COMING — the scheduled release calendar — use alphai_calendar'). It also provides concrete tuning guidance (narrow with categories, raise min_relevance to 7 to drop commentary), which tells an agent when and how to adjust parameters.

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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