AlphAI

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. When the article has a published SEC earnings read, text also includes its reported metrics with year-over-year and sequential comparisons, segments, guidance, concerns and missing items. Raises not_found for an unknown id.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "fetch"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Article id from a search result.

Output Schema

Name	Required	Description	Default
`id`	Yes
`url`	Yes
`text`	Yes
`title`	Yes
`metadata`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 26, 2026
- addedInput schema / properties / id / maxLength
```
Added value: +64
```
AddedJul 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnly, idempotent and non-destructive, but the description goes further: it discloses that the payload is an enriched digest rather than the article body, that a canonical alphai.io URL is included for citation, what the SEC earnings read adds, and that an unknown id raises not_found. That is exactly the behavioral context annotations cannot 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?

Three sentences, front-loaded with the contract, then the return shape, then the error case. Each sentence carries distinct information with no repetition of the title or annotations.

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?

Despite an output schema existing, the description still characterizes the response shape and gives the failure mode, and the single required parameter is fully covered. Nothing an agent needs in order to call this 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% and there is only one parameter, so the baseline is already satisfied. The description still adds value by tying the id to a prior search result and by defining the error outcome for an invalid id, which the schema does not state.

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 uses a specific verb and resource ('fetch one article by the id') and pins the id's provenance to a search result, which is what separates it from the search sibling. An agent can tell immediately that this is a single-item retrieval keyed on a token it already holds.

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 phrase 'by the id returned from search' establishes the intended workflow (search first, then fetch) and implicitly excludes bulk or query-based use. It never explicitly names an alternative such as alphai_article or says when not to use it, so it falls short of full routing guidance.

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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