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Earnings reads for a ticker

alphai_earnings
Read-onlyIdempotent

AlphaAI's own structured read of a company's earnings reports, newest first, plus its next confirmed report date. Each read is produced from the company's OWN SEC filing — an 8-K item 2.02 for US filers, a 6-K earnings release for foreign private issuers — within minutes of it reaching EDGAR, and every figure in it was checked against the filing text before publication, so it is the arbiter when press coverage of the same quarter disagrees. Carries reported metrics with year-over-year and sequential comparisons, segments, guidance, drivers, concerns and management quotes. This is the tool to reach for after alphai_tickers gives you a next_report_date: the feed tools return articles ABOUT a quarter, this returns the filing's own numbers. Share classes bridge, and each row reports the class the filing was made under. Depth is shallow by construction — AlphaAI's filing history starts in mid-June 2026 and reports are quarterly — so most tickers carry one or two reads and an empty list is a normal answer, not an error. Informational and AI-generated — not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesTicker symbol, e.g. 'NVDA'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes
reportsNo
unknown_tickerNo
next_report_dateNo

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description is consistent with them while adding substantial context beyond them: source documents (8-K item 2.02 / 6-K), verification against the filing text, data-depth limits ('filing history starts in mid-June 2026'), and the crucial caveat that 'an empty list is a normal answer, not an error' — which prevents an agent from misclassifying a valid empty result as a failure. The 'arbiter' role and share-class bridging further enrich the behavioral picture.

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 (~170 words) but dense — every sentence carries distinct information: purpose, provenance, content fields, usage routing, data-depth caveats, and disclaimer. It is front-loaded with the core purpose, and the boilerplate disclaimer earns its place for an AI-generated financial tool. Slightly longer than necessary, but nothing is wasteful.

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?

With an output schema present, return values need no explanation, and the description still previews the content fields anyway. It covers ordering, sources, verification, when to use, data-recency limits, empty-list semantics, and share-class behavior; annotations cover the safety profile (read-only, idempotent, non-destructive). Nothing an agent needs to call this correctly is missing, including edge cases.

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

Parameters3/5

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

Schema description coverage is 100% — the single ticker parameter is already documented as 'Ticker symbol, e.g. 'NVDA'.' The description adds only marginal parameter-adjacent context via the share-class note, but it doesn't add syntax or format details beyond the schema. Per the rubric, baseline 3 is appropriate when the schema does the heavy lifting.

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 opening sentence states a specific verb+resource: 'structured read of a company's earnings reports, newest first, plus its next confirmed report date.' It also distinguishes itself from siblings explicitly — 'the feed tools return articles ABOUT a quarter, this returns the filing's own numbers' — so an agent can tell it apart from alphai_ticker_news, alphai_news_search, and alphai_article without opening their schemas.

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: 'This is the tool to reach for after alphai_tickers gives you a next_report_date.' It also names the alternative class of tools and states what they do differently ('the feed tools return articles ABOUT a quarter'), giving clear when-not-to-use signal. No inference is required.

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.