alphai_alerts_listList the caller's active ticker news-alert subscriptions, including per-subscription filters (category whitelist, minimum relevance score, delivery mode).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| tier | Yes | ||
| limit | Yes | ||
| alerts | No | ||
| current | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / properties / alerts / items / properties / noteAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context beyond annotations by specifying that only the caller's active subscriptions are returned and that per-subscription filters are included, which clarifies the scope and content of the response.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tight sentence that front-loads the primary action and resource, then adds the relevant detail about included filters. There is no redundancy with the title or schema, and every word contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only list tool with an output schema present, the description is complete: it states whose subscriptions are returned, that they are active, and what fields are included. The output schema covers return-value structure, so nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so the description has no parameter-level responsibility. Per the rubric baseline for 0-param tools, a score of 4 is appropriate; the description does not need to add parameter meaning that does not exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), a clear resource ('the caller's active ticker news-alert subscriptions'), and the kind of data included ('filters: category whitelist, minimum relevance score, delivery mode'). It clearly differentiates this from sibling tools like alphai_alerts_subscribe and alphai_alerts_unsubscribe by focusing on read-only listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied strongly by the verb and noun phrase: use this when the agent needs to see the caller's existing alert subscriptions. However, there is no explicit guidance about when not to use it or how it compares to related alert-management tools, so the agent must infer usage from the name and 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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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.
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.
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.
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.