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AlphaAI

by io.github.makeev

Trending news (48h)

alphai_trending
Read-onlyIdempotent

Top news from the last 48h ranked by AI-assigned relevance decayed by article age (fresh stories lead; at most two stories per ticker, and market-wide macro stories — Fed/CPI, commodities, geopolitics, carrying no tickers — capped per category). Use this when the user asks 'what's moving' / 'what's the big story'. Lower min_relevance to surface weaker movers. Each item carries the full AI analysis inline — no follow-up alphai_article call needed for depth. Syndicated reprints of one story are collapsed to a single representative by default (dedupe=false to keep all), and each item reports story_id (the story root's uid — a stable story key, the same value the other news tools report for that story), sources_count and sources. Most stories run at a single outlet, so sources_count is usually 1; a value above 1 is the signal, not the number itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoStories; capped at 20 Free/Basic, 50 Pro.
dedupeNoCollapse syndicated reprints by story (default true).
min_relevanceNoMin AI relevance 1-10; default 8.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed6 schema fields changed
    • changedInput schema / properties / limit / description
      Previous value: -"Stories; capped at 10 Basic / 50 Pro."New value: +"Stories; capped at 20 Free/Basic, 50 Pro."
    • addedOutput schema / properties / result / items / properties / created_at
      Added value: +{
+  "type": "string"
+}
    • addedOutput schema / properties / result / items / properties / earnings
      Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • addedOutput schema / properties / result / items / properties / insider
      Added value: +{
+  "anyOf": [
+    {
+      "description": "Structured SEC Form 4 event block (``alphai_insider_news`` items only).\n\nAggregate of the news row's whole transaction group — same semantics as the\nREST ``/api/news/insider/`` block (openapi.yaml: ``InsiderEvent``): shares and\nvalue are GROUP sums (a 10b5-1 ladder is one event), ``avg_price_usd`` is\nvalue-weighted over priced tranches, ``is_10b5_1`` is the group OR, the\nlast fill dates the event. ``side`` is the signal label: buy (P) / sell (S)\n/ other (everything else, incl. D — sale to issuer, a buyback/redemption,\nnot an open-market disposition); the raw ``transaction_code`` rides along.\nMoney/share fields are decimal STRINGS (\"25000\", \"187.32\") — flat, precise,\nschema-simple; null when the filing prices no tranche.\n\n``filed_at`` is when EDGAR accepted the filing and ``late_filing`` marks the\nones that missed the SEC's two-business-day deadline; the rule lives in\n``_is_late_filing`` in ``repository.py`` (ported from the backend's\n``apps.insider.services.filing_lateness``). Field reference for both\nsurfaces: ``backend/openapi.yaml`` (``InsiderEvent``).",
+      "properties": {
+        "avg_price_usd": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        },
+        "filed_at": {
+          "type": "string"
+        },
+        "insider_name": {
+          "type": "string"
+        },
+        "insider_title": {
+          "type": "string"
+        },
+        "is_10b5_1": {
+          "type": "boolean"
+        },
+        "is_director": {
+          "type": "boolean"
+        },
+        "is_officer": {
+          "type": "boolean"
+        },
+        "is_ten_percent_owner": {
+          "type": "boolean"
+        },
+        "late_filing": {
+          "type": "boolean"
+        },
+        "shares": {
+          "type": "string"
+        },
+        "side": {
+          "type": "string"
+        },
+        "total_value_usd": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null
+        },
+        "transaction_code": {
+          "type": "string"
+        },
+        "transaction_date": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "side",
+        "transaction_code",
+        "shares",
+        "is_10b5_1",
+        "insider_name",
+        "insider_title",
+        "is_officer",
+        "is_director",
+        "is_ten_percent_owner",
+        "transaction_date",
+        "filed_at",
+        "late_filing"
+      ],
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • addedOutput schema / properties / result / items / properties / source_type
      Added value: +{
+  "default": "",
+  "type": "string"
+}
    • changedOutput schema / properties / result / items / required
      Previous value: -[
-  "uid",
-  "title",
-  "url",
-  "source",
-  "source_domain",
-  "summary",
-  "category",
-  "relevance_score",
-  "time_published"
-]New value: +[
+  "uid",
+  "title",
+  "url",
+  "source",
+  "source_domain",
+  "summary",
+  "category",
+  "relevance_score",
+  "time_published",
+  "created_at"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"Stories. 10 Basic / 50 Pro (tools.bulk)."New value: +"Stories; capped at 10 Basic / 50 Pro."
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly/idempotent/destructive hints), the description discloses substantial behavior: freshness-decayed ranking, at-most-two-stories-per-ticker, capped macro stories, inline AI analysis making a follow-up alphai_article call unnecessary, dedupe semantics with the dedupe=false escape hatch, stable story_id semantics shared with other news tools, and the critical interpretive rule that sources_count above 1 is the signal. None of this is available in the annotations.

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 efficiently front-loaded: purpose first, then usage trigger, then parameter guidance, then output semantics. Every sentence carries information, though the dedupe and sources_count explanations partially overlap with what the schema and output schema may already express structurally.

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 an output schema, 100% parameter coverage, and full annotations, nothing material is missing. The 48h window, ranking rule, per-ticker and macro caps, inline analysis guarantee, dedupe behavior, stable story_id, tier-based limit caps, and sources_count interpretation are all covered — an agent can invoke this tool correctly with no further research.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds practical meaning beyond the schema: it explains the effect of min_relevance ('Lower min_relevance to surface weaker movers') and details the dedupe consequence ('dedupe=false to keep all'). Only limit receives no extra elaboration, but its tier caps are already in the schema description.

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 — 'Top news from the last 48h ranked by AI-assigned relevance' — and specifies the ranking behavior (age decay, per-ticker cap, macro story cap). It also distinguishes itself from siblings like alphai_ticker_news, alphai_news_search, and alphai_macro by defining a breadth-first 'what's moving' feed rather than a ticker-specific or search result.

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 explicitly says 'Use this when the user asks "what's moving" / "what's the big story"', providing a clear invocation trigger, and advises lowering min_relevance to surface weaker movers. However, it does not explicitly name alternatives to prefer instead (e.g., alphai_ticker_news for a single ticker or alphai_article for one story), so it stops short of full when-not-to-use guidance.

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

A4.1/5.0
Disambiguation3/5

Multiple news feed tools (alphai_actionable_now, alphai_trending, alphai_macro, alphai_ticker_news, alphai_news_search, alphai_insider_news, and search) overlap in purpose, so an agent could pick the wrong one by name alone. The detailed descriptions clarify each tool's window, scope, and filtering, but the set still relies heavily on reading those descriptions to avoid misselection.

Naming Consistency3/5

Most tools share the alphai_ prefix and snake_case, but there is no consistent verb_noun pattern: alphai_alerts_subscribe and alphai_news_search are verb phrases while alphai_ticker_news, alphai_macro, and alphai_calendar are noun phrases. The un-prefixed connector tools search and fetch add a further deviation, making the naming readable but mixed.

Tool Count3/5

At 16 tools, the set is at the heavy end and includes several near-duplicates: alphai_news_search vs search, alphai_article vs fetch, and alphai_insider_news vs alphai_news_search(category='insider'). The domain is broad enough to justify many specialized feeds, but the redundancies make it feel padded.

Completeness4/5

The toolset covers news discovery, search, article retrieval, alerts lifecycle, macro calendar, earnings reads, ticker metadata, and pair comparisons, so agents can complete most workflows. Minor gaps remain, such as no story-level detail endpoint and no broader user-account or watchlist management beyond alerts.

Resources