Skip to main content
Glama
tangivis

twikit-mcp

by tangivis

get_article

Fetch an X article by rest_id or URL, returning content in preview, plain text, or full GraphQL format with media and metadata.

Instructions

Fetch an X Article (long-form post) by rest_id or URL.

Two-hop reader flow (issue #10):

  1. ArticleRedirectScreenQuery resolves the article rest_id to the underlying tweet rest_id.

  2. TweetResultByRestId (twikit's existing helper) fetches the tweet with article fieldToggles enabled. The body lives at tweet.article.article_results.result.

Requires authentication via cookies — same as every other authenticated tool here. No env-var setup.

Args: article_id: Article rest_id (numeric string) or full /i/article/ URL. format: Output shape, one of: - "preview" (~1 KB) — rest_id, title, preview_text, cover_image - "plain" (~20 KB, default) — above + plain_text + media URL list + lifecycle_state. The LLM-friendly shape. - "full" (~150 KB+) — raw GraphQL payload including the heavy content_state rich-block tree. Use only when you need it (rich-content rendering, archiving, structure analysis).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_idYes
formatNoplain

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It details the two-hop internal flow, authentication requirement, and format effects. Lacks mention of error conditions or rate limits, but is otherwise transparent.

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 well-structured with sections but is somewhat lengthy. It includes detailed internal flow information that might be considered noise for an agent, but remains clear and organized.

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 complexity and presence of an output schema, the description covers authentication, internal flow, parameter details, and format use cases. It is complete for an agent to select and invoke the tool 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?

Despite 0% schema description coverage, the description fully explains both parameters: article_id accepts rest_id or URL, and format provides three options with sizes and use cases. This compensates entirely for the schema gap.

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 clearly states the tool fetches an X Article by rest_id or URL, using a specific verb 'Fetch' and resource 'X Article', and distinguishes from siblings like get_article_preview.

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

Usage Guidelines3/5

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

The description implies usage through format options but does not explicitly state when to use this tool versus alternatives like get_article_preview. No when-not or exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tangivis/twitter-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server