ScrapeMole Wiki

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial context beyond them: individual-person entities are refused, results are capped at ~50 curated properties, name lookups are resolved to the best-matching item, and the tool is paid via x402 USDC on Base at $0.002 per successful call, with an unpaid call returning a PaymentRequired object. That is exactly the kind of behavior an agent cannot learn from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The 'Use when...' trigger and the entity-refusal rule are front-loaded, which is good. The long enumeration of ~50 property types is somewhat padded but it does concretely demonstrate the return shape, so it earns most of its space; a little trimming would make it tighter.

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

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

There is no output schema, so the description carries the full burden of describing returns and does so (field list plus property examples and the ~50-property cap). Combined with the payment model, the person-entity refusal, and ID/name input handling, an agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, and the description adds meaning on top: it shows the Q-ID format (Q95), explains that a plain name is 'resolved to the best-matching item' (a non-obvious resolution behavior), and clarifies the id-or-q choice. Only 'lang' is left entirely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

The description names a specific verb+resource (retrieve Wikidata entity facts by Q-ID or name) and enumerates the exact payload: label, description, aliases, Wikipedia link, and ~50 curated properties. The phrase 'structured, machine-readable facts rather than prose' directly contrasts it against the prose-oriented sibling 'summary', so an agent can route 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.

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

It gives an explicit trigger ('Use when you need structured, machine-readable facts rather than prose') and an implicit contrast with the prose tool. However, it never names the alternatives (search, summary) nor states when-not-to-use, so selection still requires inference from the sibling names.

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

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

Annotations only cover safety (readOnly/openWorld/idempotent); the description adds substantial non-annotation behavior: the x402 USDC-on-Base payment model, $0.001 per successful call, and that calling without payment returns a PaymentRequired object. It also discloses the exact result fields and relevance ranking.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense but purposeful sentences, front-loaded with the use case and then the return shape and payment model. No filler and nothing repeated from structured fields.

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

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

With no output schema, the description compensates by enumerating the returned fields (title, snippet, URL, word count, last edit time). Payment/error behavior is also covered, so an agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so q, lang, and limit are already documented. The description only reinforces the limit ceiling ('up to 20 results') and ranking behavior, adding marginal value over the schema baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

States a specific verb+resource (full-text search over one Wikipedia language edition) and its scope (returns titles/snippets, ranked by relevance). It also distinguishes itself from siblings by naming /v1/summary as the content-reading alternative.

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

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

Explicitly says when to use it ('find the right Wikipedia article before reading it') and when not to rely on it for content ('use /v1/summary for content'). The sibling routing is unambiguous.

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

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.

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.

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.

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.

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.

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.