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AntonioBlago

VisiblyAI MCP Server

by AntonioBlago

get_article

Fetch a single article's text in chunks for reading or SEO analysis. Use offset and content flags to page through long articles and detect edits via content hash.

Instructions

One article. Text only with include_content=true, delivered in chunks of up to 20000 characters. Credits: 0, read-only.

Use next_content_offset to continue; content_hash changes when the article was edited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_idYes
project_idNo
content_limitNo
content_offsetNo
include_contentNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.1

TDQS

B3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it discloses that the operation is read-only, costs 0 credits, returns text only when include_content=true, chunks content up to 20000 characters, and exposes content_hash changes after edits. It does not cover auth requirements or rate limits, but the core behavioral traits are clearly stated.

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 very short and front-loads the core output behavior and pagination note. The opening fragment "One article." is terse and structurally weak, but there is little wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with an output schema available, the description covers key behaviors such as chunking, credits, read-only status, and edit detection. However, with 0% schema description coverage and five input parameters, it leaves important parameter semantics and the project_id argument unaddressed.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It explains include_content and implies the 20000-character chunk size for content_limit, but article_id and project_id remain unexplained, and it refers to "next_content_offset" while the schema parameter is content_offset, which can confuse pagination calls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description says "One article," which identifies the resource but does not itself state a retrieval verb; the tool name get_article supplies that context. It distinguishes a single-article fetch from list-oriented siblings like list_articles, but gives no further differentiation from other article-related tools.

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

Usage Guidelines2/5

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

The description gives a pagination instruction ("Use next_content_offset to continue") but never explains when to use get_article instead of alternatives such as list_articles, get_content_query, or get_content_briefing. There is no when-to-use or when-not-to-use 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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