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article_sections

Retrieve a Wikipedia article's table of contents with section numbers, headings, and nesting levels. Navigate long pages and decide which sections to read before fetching full text.

Instructions

Get the table of contents (section headings) for a Wikipedia article — section number, heading text, and nesting level. Useful for navigating long articles before committing to the full body via article_extract. Major articles can have 50KB+ of body text; article_sections gives the TOC in a compact numbered list so callers can pick what to read next. Pairs with summary (lead), article_sections (structure), article_extract (full body).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoWikipedia language code (default 'en')en
titleYesArticle title (e.g. 'Tyrannosaurus' or 'Albert_Einstein')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations were provided, so the description carries the full burden. It discloses that the output is a compact numbered list and notes that major articles can have 50KB+ of body text, giving useful context about the cost of alternatives. However, it does not state whether the tool is read-only (implied by 'Get') or mention any pagination, rate limits, or error cases. This is a moderate gap for a tool with zero annotation coverage.

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?

Three sentences, each front-loaded with purpose: first defines what it gets, second explains why (use case), third maps the tool family. Zero waste, and the most important information (what it returns) is first.

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 simplicity (2 params, no output schema, no annotations) and the complexity of the sibling ecosystem, the description is complete: it covers purpose, usage context, return format, and pairing. Nothing an agent needs to call it correctly is missing.

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 coverage is 100%, so the schema already documents both 'lang' (with enum and default) and 'title' (with examples). The description does not add any parameter-specific meaning beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Get the table of contents (section headings) for a Wikipedia article') and immediately specifies the returned fields (section number, heading text, nesting level). It clearly distinguishes itself from siblings like article_extract and summary by naming them in the pairing sentence.

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 describes when to use this tool: 'before committing to the full body via article_extract' and 'for navigating long articles.' It names alternatives (article_extract, summary) and explains the workflow, making the selection condition unambiguous.

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