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scrape_wikipedia

Fetch Wikipedia article content by title or search term. Returns full text, paragraph list, or section headings as structured data.

Instructions

Fetch a Wikipedia article by title or search term and return its text content.

Makes a live network request to Wikipedia, resolving the query to the best-matching article and extracting its body. The shape of the returned text depends on mode: "full" returns the entire article as one string; "paragraphs" returns the article split into a list of paragraph strings; "headers" returns a list of the article's section heading strings (its table of contents). If no article matches the query, an empty result is returned (empty string for "full", empty list for "paragraphs" or "headers").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoString, one of "full", "paragraphs", or "headers". Selects the return shape as described above. Example: "paragraphs". No default (required).full
queryYesString. Article title or search term. Example: "Model Context Protocol". No default (required).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
countNo
errorsNo
scraperNo
source_urlsNo
Behavior4/5

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

Since annotations are absent, the description carries the full burden. It discloses that a live network request is made, explains how query resolution works, and details the return shape for each mode plus the empty-result behavior. It does not mention rate limits or error handling, but the key behaviors are clearly covered.

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 concise but not overly terse. The first sentence immediately states the purpose, and the second paragraph adds behavioral detail in a structured way. Every sentence contributes value, though the content could be slightly tightened.

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?

The description covers the core behavior, return shapes, and empty results, which is appropriate given that an output schema exists. It does not describe error handling or network failure scenarios, but for a scraper with clear modes, this is sufficient.

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?

The description adds substantial meaning beyond the schema: it defines what 'full', 'paragraphs', and 'headers' return, and explains the empty-result behavior per mode. The schema itself only lists the mode as 'one of...' without these details, so the description elevates semantic clarity.

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 (Fetch), a specific resource (Wikipedia article), and the action's outcome (return its text content). It clearly distinguishes from sibling tools like scrape_url or scrape_news by being exclusively about Wikipedia.

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 clearly implies this tool is for Wikipedia content retrieval, which differentiates it from generic URL scrapers and other domain-specific scrapers. However, it does not explicitly list when not to use it or mention alternative tools, so it misses the highest bar.

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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