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Show recent discussion threads on a Wikipedia article's discussion page to reveal editor debates, open questions, and proposals before relying on the article.

Instructions

Show the most recently active discussion threads on an article's talk page — what Wikipedia editors are currently debating, questioning, or proposing about the topic. Each thread lists its heading, last-activity timestamp, signed-comment count, and an excerpt of the latest comment. Automated maintenance notices (redirect/deletion discussions) are filtered out. The behind-the-scenes companion to contributors and article_quality: check it to find controversies, open questions, and editorial disputes before relying on an article. Read-only — GET only, no new dependencies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoWikipedia language code (default 'en')en
limitNoMax threads to return (default 5, max 15)
titleNoArticle title whose talk page to read (e.g. 'Climate change')

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.37

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the operation is read-only, GET-only, requires no new dependencies, and filters automated maintenance notices—useful context. It stops short of mentioning rate limits, pagination, or error behavior, preventing a 5.

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 front-loaded with the core purpose and then adds supporting context in a logical order. It is slightly longer than strictly necessary (three sentences plus a parenthetical), but every sentence contributes useful information.

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

Completeness4/5

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

With no output schema, the description explains what each thread contains (heading, timestamp, comment count, excerpt) and notes filtering of maintenance notices. It is largely complete for a 3-parameter, high-schema-coverage tool, though it does not address pagination or edge cases like empty talk pages.

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 `lang`, `limit`, and `title` fully. The description adds no extra syntax, format, or constraint details beyond what the schema provides, making the baseline 3 correct.

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 states a specific verb (show) and resource (recently active discussion threads on an article's talk page), and explicitly distinguishes itself from sibling tools `contributors` and `article_quality`. An agent can immediately tell what the tool returns and how it differs from adjacent capabilities.

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?

It clearly says to check this tool to find controversies, open questions, and editorial disputes before relying on an article, and names companion tools. However, it does not explicitly state when *not* to use it or contrast its conditions against the alternatives in detail, so a 4 is appropriate.

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