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ScrapeMole Wiki: Wikipedia summaries, Wikipedia search and Wikidata facts for AI agents (attributed): Wikipedia summary

summary
Read-onlyIdempotent

Use when you need a short, sourced explanation of a topic, place, organization, product or concept: returns the Wikipedia article's lead section as plain text plus title, short description, thumbnail and original image, page URL, Wikidata ID and last-modified time, in any Wikipedia language (redirects followed). Every response carries the CC BY-SA 4.0 license, credit line and source link required for reuse. Articles about individual people are not served (non-personal data only). Paid tool: x402 USDC on Base, $0.001 per successful call; call without payment to receive the PaymentRequired object.

Input Schema

NameRequiredDescriptionDefault
langNoWikipedia language code (en, de, fr, lt, ja…).en
titleYesArticle title (e.g. "Cloudflare", "Vilnius") or a Wikipedia article URL.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

With readOnly/openWorld/idempotent already covered by annotations, the description adds a lot the annotations cannot: redirects are followed, a CC BY-SA 4.0 license/credit/source link is required and included on every response, biography articles are refused, and the tool is paid via x402 USDC on Base at $0.001 per successful call, with the explicit instruction that an unpaid call returns a PaymentRequired object. That payment/error protocol disclosure is exactly the kind of behavior an agent must know before invoking.

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 dense but front-loaded with the usage trigger, then layered: return payload, licensing, exclusions, payment. Every sentence contributes something actionable. It is somewhat over-packed into long clauses, which costs a little readability, but there is no filler or restatement of the name.

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?

There is no output schema, yet the description fully enumerates the returned fields, so the agent knows what a call yields. Combined with the license/attribution requirement, the person-article exclusion, and the x402 payment flow with its PaymentRequired response, an agent has everything needed to call this correctly and handle the payment case.

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 both parameters (lang, title) are already documented in the schema. The description adds only marginal meaning — that any Wikipedia language is supported and that redirects resolve — plus an example-free note that the title accepts an article URL, which the schema also states. Baseline 3 is the right level 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a precise verb+resource: it returns the lead section of a Wikipedia article as plain text, and enumerates the accompanying fields (title, short description, thumbnail, image, page URL, Wikidata ID, last-modified). An agent can tell this is the summary-fetch tool of the trio. It does not, however, explicitly contrast itself with the sibling tools 'entity' and 'search', leaving the differentiation to inference.

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 gives a clear when-to-use trigger ('Use when you need a short, sourced explanation of a topic, place, organization, product or concept') and a when-not condition ('Articles about individual people are not served (non-personal data only)'). What is missing is an explicit pointer to alternatives — neither 'search' nor 'entity' is named as the route for search queries or raw Wikidata facts, so the agent must infer sibling selection.

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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