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Glama

AlphaAI

by io.github.makeev

List supported tickers

alphai_tickers
Read-onlyIdempotent

List supported tickers (US stocks/ETFs, crypto, and foreign listings). Optionally filter by query (prefix on ticker, substring on name, or the company's brand name where that differs from its registered one — q='spacex' finds SPCX, registered as SPACE EXPLORATION TECHNOLOGIES CORP) or by sector. currency is the trading currency (USD for US listings and crypto pairs, the local currency for foreign ones); country is the ISO alpha-2 country of domicile and is null for crypto, which has no jurisdiction of incorporation. Also answers 'when does X next report': next_report_date is the company-confirmed date of the next earnings report, in America/New_York. It is NEVER an estimate — null means AlphaAI holds no confirmed date, which is not the same as the company not reporting. A confirmed date typically surfaces about five weeks ahead, so a null this far out is normal. When the report lands, alphai_earnings(ticker) returns AlphaAI's read of it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoTicker-prefix matches first, then company-name substring matches.
limitNoMax rows to return.
offsetNoPagination offset.
sectorNoFilter by sector (case-insensitive).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNo
totalYes

Schema Changelog

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

  1. Changed1 schema field changed
    • addedOutput schema / properties / items / items / properties / next_report_date
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark readOnly, idempotent, and non-destructive, and the description adds substantial behavioral detail: null country for crypto, next_report_date is never an estimate, null means no confirmed date rather than no report, typical five-week lead time, and timezone America/New_York. This goes well beyond 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 text is dense but front-loaded with the core listing purpose and then groups related nuances around q and next_report_date. It is longer than the average description, but nearly every sentence carries semantic value; minor redundancy such as 'company-confirmed' and 'never an estimate' is acceptable for disambiguation.

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, the description does not need to restate return shapes; it instead covers ambiguity that the schema cannot: brand-name matching, jurisdiction nulls, date confirmation semantics, and the handoff to alphai_earnings. This is complete for a read-only lookup tool.

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; the description raises it by enriching q with prefix/substring/brand-name matching and a concrete example ('q=spacex' finds SPCX). Other parameters (limit, offset, sector) already carry clear schema descriptions and need little extra.

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 starts with a precise verb-resource pairing: 'List supported tickers (US stocks/ETFs, crypto, and foreign listings).' It also states an additional purpose, answering when a ticker next reports, and clarifies through the q example how listing and search behave, making the tool's scope unmistakable.

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?

It gives clear contexts: filter by query or sector, and use next_report_date for confirmed earnings dates. It references alphai_earnings as the follow-up for reading the report after it lands, which orients an agent among siblings, though it does not spell out exhaustive when-not-to-use conditions for every sibling.

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.

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