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by io.github.makeev

Actionable-now feed

alphai_actionable_now
Read-onlyIdempotent

Breaking, decision-grade news from the last few hours. The primary filter is the enricher's actionability score, and the gate is strict: by default only actionability='high' (a time-sensitive development — fresh guidance cut, halted trading, breaking M&A, surprise print) qualifies. Big-but-not-urgent stories scored 'medium' (shape a position over days/weeks) never appear at the default floor no matter how high their novelty — pass min_actionability='medium' to include them, or use alphai_trending / alphai_ticker_news for the broader tape. Market-wide macro releases (an FOMC decision, a CPI/jobs print) qualify and carry no tickers. An empty list on quiet nights/weekends is expected — it means no high-actionability prints in the window, not an error; widen hours or min_actionability before concluding nothing happened. The time window is over each article's PUBLICATION time, not the underlying event's date, so a fresh pick-up of an older event can appear; min_novelty (not the window) is what drops post-event recaps of already-public stories. Ordered novelty-first; syndicated reprints 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 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. Each item carries the full AI analysis inline — no follow-up alphai_article call needed for depth. Informational and AI-generated — not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoLook-back window in hours; default 6.
limitNoStories; capped at 20 Free/Basic, 50 Pro.
dedupeNoCollapse syndicated reprints by story (default true).
min_noveltyNoMin information_novelty 1-10; default 7.
min_actionabilityNoActionability floor. 'high' (default) = only act-today items; 'medium' also includes stories that shape a position over days/weeks.high

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

A4.9/5.0
Behavior5/5

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

Annotations only note readOnly/idempotent/non-destructive. The description adds a wealth of behavioral nuance: publication-time windowing vs event date, min_novelty as the recaps filter, ordering, dedupe collapse, story_id semantics, sources_count interpretation, inline AI analysis, macro releases without tickers, and quiet-weekend empty lists. No contradictions with annotations.

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?

Though long, the description is dense with non-redundant, decision-relevant details and no filler. It front-loads the core filter and then systematically clarifies each behavioral subtlety or edge case. Every sentence earns its place for a feed with this many nuances.

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?

The description covers the essential behavioral contract, edge cases, alternatives, and output semantics even though an output schema exists. It explains story_id provenance, sources_count interpretation, why empty lists occur, and that no follow-up call is needed for depth. Given the tool's complexity, nothing important is missing.

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 baseline is 3. The description adds meaning beyond the schema for min_actionability (concrete examples of high vs medium), hours (publication-time semantics), min_novelty (what it filters vs what it doesn't), and dedupe (story-level collapse). Only 'limit' is not elaborated, but the schema fully covers it.

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 clear, specific purpose: a breaking, decision-grade news feed filtered by actionability. It differentiates from siblings by explicitly contrasting with alphai_trending and alphai_ticker_news for broader tape coverage, and names the exact actionability gate.

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?

The description gives explicit when-to-use guidance: default high-actionability only, how to include medium stories via min_actionability='medium', and when to switch to alphai_trending/alphi_ticker_news. It also tells the agent how to interpret empty results and when to widen hours or lower the floor, which is actionable context.

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