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Glama

AlphaAI

by io.github.makeev

Subscribe to ticker alerts

alphai_alerts_subscribe
Idempotent

WRITE operation (creates/updates a saved subscription in the caller's account). Subscribe the caller to ticker news alerts. Optional category_filter (e.g. ['earnings','insider']) restricts which categories trigger; min_relevance_score raises the threshold. This is a partial update: omitting either field on an existing subscription preserves its current value, and a brand-new subscription defaults min_relevance_score to 7. Note the threshold applies to email and telegram delivery; a Pro webhook receives the full matched stream and filters on its own. Raises tier_not_paid / unknown_ticker / limit_reached. Crypto alerts use the -USD ticker (BTC-USD); subscribing to a collider string like BTC binds the stock/ETF that owns it and the response's note field says so.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesTicker to subscribe to (active symbol).
category_filterNoCategories that trigger alerts.
min_relevance_scoreNoMin relevance to alert on.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
tickerYes
is_activeYes
created_atYes
delivery_modeYes
category_filterNo
min_relevance_scoreYes

Schema Changelog

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

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

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations, the description discloses partial-update semantics, defaulting behavior for min_relevance_score, delivery-channel threshold differences, specific error conditions, and crypto ticker formatting. This adds substantial behavioral context that annotations alone could not convey.

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?

The description is dense yet efficient; every sentence contributes operational guidance such as update semantics, defaults, errors, or ticker edge cases. The write-operation framing is front-loaded, and there is no filler or repetition of schema details.

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 the tool's complexity, the presence of an output schema, and the annotations, the description covers all essential operational aspects: what it does, parameter effects, update behavior, errors, and special ticker cases. Nothing critical appears missing for an agent to invoke it correctly.

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

Parameters5/5

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

Even though schema coverage is 100%, the description adds meaningful parameter behavior: category_filter restricts triggering categories, min_relevance_score raises the threshold, omitting either field preserves existing values on update, and a new subscription defaults min_relevance_score to 7. The crypto ticker example also clarifies the ticker parameter beyond the schema.

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 ('Subscribe'), a clear resource ('ticker news alerts'), and the underlying operation ('creates/updates a saved subscription in the caller's account'). It clearly distinguishes this from sibling tools like alphai_alerts_list or alphai_alerts_unsubscribe by framing it as the write/subscribe operation.

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 clearly identifies this as the tool for subscribing or updating an existing subscription, which gives strong contextual guidance. It does not explicitly name alternatives or state 'use list/unsubscribe for other cases,' but the write-vs-read framing and sibling names make the intended use unambiguous.

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