AlphAI

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 AlphAI 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 AlphAI's read of it.

Input Schema

TableJSON Schema

Name	Required	Description
`q`	No	Ticker-prefix matches first, then company-name substring matches.
`limit`	No	Max rows to return.
`offset`	No	Pagination offset.
`sector`	No	Filter by sector (case-insensitive).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	No
`total`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 26, 2026

changedInput schema / properties / q / anyOf

Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "maxLength": 64,
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

changedInput schema / properties / sector / anyOf

Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "maxLength": 64,
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

Changed1 schema field changedSep 6, 2026

addedOutput schema / properties / items / items / properties / next_report_date

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}

First observedJun 17, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial behavior beyond that: the q matching semantics (ticker-prefix first, then name substring, then brand-name matching with the spacex→SPCX example), currency and country field semantics (null for crypto), and critically the next_report_date null semantics — that null means 'no confirmed date' rather than 'no report', with the ~5-week lead-time context. This prevents a real mis-invocation failure mode where an agent treats null as a fact. No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence earns its place — there is no filler. It is logically structured, moving from purpose to filters to field semantics to the special next_report_date capability. It is on the verbose side for a listing tool, but the subtle semantics it must convey justify the length.

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 tool has an output schema, so return values need no explanation. The description covers all non-obvious semantics that would cause an agent to mis-invoke: brand-name q matching, crypto country nulls, and the next_report_date null meaning with its timing context. Nothing an agent needs in order to use the filters or interpret results correctly 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 the baseline is 3. The description adds genuine value for q by explaining brand-name matching (where a company's brand differs from its registered name, with a concrete example), which goes well beyond the schema's 'ticker-prefix matches first, then company-name substring'. limit/offset/sector are already fully covered by the schema, so the description need not repeat them.

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?

States a specific verb+resource ('List supported tickers') and defines the scope precisely (US stocks/ETFs, crypto, foreign listings). The purpose is unmistakable and clearly distinct from the sibling tools, which are all about alerts, news, earnings, radar, and watchlists rather than the ticker catalog itself.

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

Usage Guidelines3/5

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

The description gives one concrete routing hint — 'When the report lands, alphai_earnings(ticker) returns AlphAI's read of it' — which tells the agent where to go after using this tool. However, it never states when to use this tool versus the many siblings, nor any exclusion conditions. The usage is implied by its role as the ticker catalog rather than made explicit.

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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