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article_at_date

Find how a Wikipedia article read on a given date by retrieving the revision in effect at or before that day, including lead text, editor, and permanent link.

Instructions

Show what an article said on a given date — the time machine. Given an article title and a YYYY-MM-DD date, finds the latest surviving revision at or before that day and returns its lead text: how the article read then, with the revision ID, timestamp, editor, edit summary, and a permanent link to the exact revision. Follows redirects; reports clearly when the article had no revision before the date. The history companion to summary (the article now), revisions (the raw edit log), and revision_diff (what changed between revisions). Read-only via the action API — GET only, no new dependencies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesDate to look back to, YYYY-MM-DD (e.g. '2023-01-01')
langNoWikipedia language code (default 'en')en
titleYesArticle title (e.g. 'ChatGPT')

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.37

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does well: it discloses redirect following, the exact return payload (revision ID, timestamp, editor, edit summary, permanent link), explicit edge-case messaging ('reports clearly when the article had no revision before the date'), and access semantics ('Read-only via the action API — GET only'). Nothing behavioral is left for the agent to guess.

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?

Front-loaded with the core purpose and the 'time machine' framing, then details in descending order of importance (inputs, return, edge cases, sibling routing, access). Dense but every sentence earns its place; marginally long, which keeps it just below a 5.

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?

No output schema exists, so the description appropriately enumerates the return fields and the no-revision edge case. Combined with 100% schema coverage for inputs, an agent has everything needed to invoke and interpret results.

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 description coverage is 100%, so the schema already documents title, date format, and lang enum/default. The description restates the title and YYYY-MM-DD inputs but adds no new syntax or constraints beyond the schema. Baseline 3 is appropriate.

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 specific verb and resource—'Show what an article said on a given date'—and clearly frames it as the historical counterpart to summary/revisions/revision_diff. An agent can distinguish it from siblings without opening schemas.

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

Usage Guidelines5/5

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

Explicitly positions the tool relative to named alternatives: 'The history companion to `summary` (the article now), `revisions` (the raw edit log), and `revision_diff` (what changed between revisions).' The when-to-use condition (latest revision at/before a date) is spelled out.

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