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twikit-mcp

twikit-mcp

by tangivis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_article

Fetch X articles (long-form posts) by ID or URL, returning preview, plain text, or full rich content for analysis or archiving.

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).

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": {
      "article_id": "<article_id>"
    },
    "name": "get_article"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNoplain
article_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.39
    • addedInput schema / properties / article_id / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "string"
+  }
+]
    • removedInput schema / properties / article_id / type
      Removed value: -"string"
  2. First observedv0.1.33

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the two-hop reader flow, output sizes and fields for each format (e.g., 'preview' ~1 KB, 'full' ~150 KB+), and the response body location. It lacks explicit statements about failure modes or side effects, but as a read operation, the coverage is strong.

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?

The description is well-structured with a clear first sentence, a detailed but relevant flow explanation, and a bulleted args list. The two-hop flow might seem technical, but it's directly useful for understanding behavior and debugging. No redundant sentences.

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's moderate complexity (2 params, 3 output formats) and the presence of an output schema, the description is highly complete. It covers the full request/response landscape: what is fetched, how it works, parameter options, output sizes, and required auth. There are no obvious gaps for an agent to invoke it 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?

Schema coverage is 0%, so the description must fully explain both parameters. It does so excellently: article_id is defined as 'Article rest_id (numeric string) or full /i/article/<id> URL,' and format lists each option with size, included fields, and a recommendation for when to use it. This fully compensates 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 opens with a specific verb and resource: 'Fetch an X Article (long-form post) by rest_id or URL.' This clearly states what the tool does and distinguishes it from tweet-focused siblings. The mention of 'long-form post' differentiates it from standard tweets, and the parameter detail ('by rest_id or URL') adds scope.

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 description provides clear context for when to use the tool ('Fetch an X Article'), plus format selection guidance ('Use only when you need it') for the 'full' format. It also notes authentication requirements ('Requires authentication via cookies'). However, it does not explicitly compare with the sibling tool get_article_preview, leaving some ambiguity about when to choose one over the other.

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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