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Glama

AlphaAI

by io.github.makeev

Fetch article (ChatGPT connector)

fetch
Read-onlyIdempotent

ChatGPT connector contract: fetch one article by the id returned from search. Returns the enriched digest (summary, per-ticker analysis, category, relevance) — not the full article body — plus the canonical alphai.io URL for citation. Raises not_found for an unknown id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesArticle id from a search result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
urlYes
textYes
titleYes
metadataNo

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark read-only/idempotent/non-destructive, and the description adds valuable non-obvious behavior: it returns only an enriched digest, includes a canonical citation URL, and raises not_found. 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.

Conciseness5/5

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

Two tight sentences, front-loaded with the core action, and every clause carries information (source of id, digest contents, exclusion of full body, citation URL, error behavior).

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?

For a single-parameter read tool with rich annotations and an output schema, this description tells the agent everything needed to call it correctly: where the id comes from, what to expect back, and what error is raised.

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 coverage is 100% and the schema already says the id is 'from a search result,' so the description adds little semantic value beyond restating that provenance. The not_found detail is behavioral, not parameter-level.

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 clear verb+resource: fetch one article by id. The phrase 'not the full article body' plus 'id returned from search' distinguishes it from search and alphai_article.

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?

Clearly says the tool is for fetching an article after obtaining an id from search. It does not explicitly name an alternative for full-article needs, but the digest-not-body exclusion provides enough 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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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