RyuMoon Data Tools

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

With no annotations, the description carries the full behavioral burden and discloses meaningful traits: scanning homepage plus contact/about pages, success-based charging, and payment via x402 at $0.005 USDC on Base. It still omits failure behavior, rate limits, and payment workflow details, so it is strong but not complete.

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 front-loaded and dense, with each clause contributing useful scope, output, or payment information. The parenthetical about Cloudflare-obfuscated emails is relevant and not wasteful.

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?

Because there is no output schema and no annotations, the description appropriately lists extracted fields and discloses billing behavior. It is nearly complete for a simple two-parameter tool, but it could say more about error handling or the payment flow.

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 the schema already documents both url and maxPages. The description adds scanning context but does not clarify parameter behavior, and its 'homepage plus up to 5 contact/about pages' wording is not clearly mapped to the maxPages parameter.

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 the resource as website contacts / lead enrichment and enumerates the extracted data: emails, phones, social profiles, postal address, and organization name. This is clear enough for an agent to identify a contact-extraction tool, though it does not explicitly contrast itself with the sibling tools.

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?

The phrase 'lead enrichment' implies a usage context, and the scan scope indicates it operates on a company website. However, the description gives no explicit when-to-use or when-not-to-use guidance and does not name alternatives among the sibling tools.

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 no annotations, the description carries the full load and does well: it discloses what is removed (nav, ads, cookie banners), what is returned (main-content Markdown plus title, author, date, word count), and the billing model including the exact price and payment rail. Gaps remain on failure behavior, non-article/JS-heavy pages, and rate limits.

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?

Two tight sentences that front-load the transformation and then layer output and pricing, with zero filler. The only minor waste is the 'Web page to markdown:' prefix, which largely restates the tool name before the substantive clause.

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, and the description compensates by enumerating returned fields and the cost/auth model, which is what an agent needs before calling a paid endpoint. It is slightly incomplete on error and rendering edge cases for a paid fetch tool.

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 all five parameters are already documented inline, making 3 the baseline. The description adds only indirect hints (stripping of nav/ads corresponds loosely to mode=article) and never explains mode, links, images, or collectLinks in its own text.

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 specific verb and transformation ('Web page to markdown: read any public URL as clean, LLM-ready Markdown'), which is unmistakable against siblings like metadata or sitemap. It also scopes the input to public URLs, so the agent knows exactly what resource class this acts on.

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 clear context for when to reach for this tool: extracting readable page content for LLM consumption from any public http(s) URL. It does not name alternatives (e.g., metadata for structured page info, sitemap for URL discovery) or state when not to use it, so it falls short of a full when/when-not treatment.

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 no annotations, the description must carry the behavioral burden, and it does disclose two important traits: a single fetch ('Fast one-fetch') and the payment model ('Charged only on success. Price: $0.001 USDC on Base via x402'). However, it says nothing about failure behavior, timeouts, JS-rendered pages, or rate limits, which matter for a paid network fetch.

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 definition is front-loaded with the resource and its output fields, then closes with cost and payment mechanics in a compact sentence. The keyword-list style is dense but each item earns its place as a description of the returned payload; no filler sentences.

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?

For a two-parameter tool with no output schema and no annotations, the description adequately covers what is returned and what it costs. It leaves gaps around error handling and page-type limitations (e.g., JS-heavy sites), which an agent would want before committing to a paid call.

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 url and jsonld are already documented in the schema, and the baseline of 3 applies. The description's mention of JSON-LD hints at the jsonld toggle but adds no format or default detail beyond 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 resource and verb-equivalent: 'URL metadata / link preview' and enumerates the extracted fields (Open Graph, canonical URL, favicon, hreflang, robots, JSON-LD). This is far more specific than the bare tool name 'metadata' and lets an agent tell it apart from siblings like markdown or sitemap, which return content rather than SEO metadata. It stops short of explicitly contrasting with those siblings.

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?

Usage is implied by phrases like 'link preview' and 'SEO metadata', which signal the when-to-use case, but there is no explicit statement of when to prefer markdown or sitemap instead, and no exclusions or prerequisites. An agent must infer the choice from context.

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 no annotations at all, the description carries the full burden and does a solid job: it discloses the 5000-URL ceiling, that the fallback crawls homepage links when no sitemap is found, that billing occurs only on success, and the exact price and payment rail (x402, USDC on Base). It stops short of stating wallet/auth prerequisites, rate limits, or error behavior beyond the success-charging rule.

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 definition is dense and front-loaded, leading with the resource and its sources before moving to output fields, fallback, and pricing. Every clause carries information, though the parenthetical "(indexes, gzip, text)" and the pricing sentence make it a tightly packed run-on that could be split for readability.

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?

For a four-parameter read tool with no output schema, the description covers what is returned (URLs plus lastmod/changefreq/priority), the volume cap, the fallback path, and the payment model, which is what an agent needs to invoke it responsibly. Missing only edge behavior such as failure responses and any payment prerequisites.

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 schema already documents all four parameters, and the description only loosely restates two of them ("optional keyword filter" for contains, "homepage-link fallback" for fallback). The one genuinely additive fact is the 5000-URL server-side cap, which contextualizes the limit parameter's 500 default.

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 and resource ("list every page URL on a site") and enumerates the discovery sources it draws from (robots.txt, XML sitemaps, indexes, gzip, text). Siblings are unrelated tools, so an agent can tell instantly what this does and that it does not overlap with contacts, markdown, or metadata.

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?

Usage context is implied rather than stated: the mention of a homepage-link fallback tells the agent it still returns something when no sitemap exists, but there is no explicit "use this when..." or "do not use this for..." guidance. No alternatives are named, which is partly because no sibling performs URL discovery.

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 no annotations, the description carries the full burden and does well: it discloses no login required, success-only charging, the price ($0.005 USDC on Base via x402), and a hard cap of 200 results. It omits rate limits and failure/error behavior, which are the remaining behavioral gaps for a paid external scrape.

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?

A single dense block that front-loads the action, then the return shape, then the access/payment terms. Every sentence carries information; only the payment detail at the tail could be trimmed or split for readability.

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?

For a paid, unauthenticated search tool with no output schema, the description covers what is returned (date, text, views, reactions, links, media, channel title, subscribers), the result cap, and the billing model. The main omission is match-mode semantics, which an agent needs to use the any/all parameter 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 75%, so the schema already documents channel, keywords and limit. The description adds one genuinely new fact (the 200-result maximum, versus the schema's stated default of 50), but leaves the match enum (any/all) completely unexplained and does not clarify keyword separators or limits beyond what the schema says.

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?

"Find messages in a public Telegram channel by keyword" gives a specific verb (find), resource (messages in a public Telegram channel) and filter (keyword). None of the listed siblings (contacts, markdown, metadata, sitemap, youtube_transcript) overlap, and the scoping to public channels is stated up front.

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?

Usage is only implied: search a channel when you want messages matching keywords, and the schema hints that an empty keywords value returns latest messages. There is no explicit when-to-use / when-not-to-use statement, no guidance on combining keywords with the match mode, and no mention of what to do if the channel is private or not found.

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 no annotations, the description carries the full burden and does a fair job: it discloses the billing model (charged only on success, $0.004 USDC on Base via x402), the language-fallback behavior, and that timestamps default to on. It does not cover failure modes such as disabled/unavailable captions, which is the main remaining gap.

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?

Two dense sentences, front-loaded with what the tool returns before the commercial terms. Every clause conveys distinct information — output shape, fallback/translation behavior, billing — with no filler.

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 correctly takes on the job of describing the return payload (text plus segments, title, channel, duration, language), which makes the tool callable without guesswork. Only failure behavior for videos lacking captions is unaddressed.

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 all four parameters are already documented in the schema, including the timestamps default of true. The description adds marginal value only through the 'language fallback' notion, which hints at how lang behaves when the preferred language is missing; it adds nothing about video format or translate interaction.

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?

Names the specific resource (a YouTube video's captions) and enumerates exactly what comes back: clean plain text, timestamped segments, title, channel, duration and language. The scope ('one video') is explicit, and no sibling tool (contacts, markdown, metadata, sitemap, telegram_search) overlaps, so no differentiation is needed.

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?

The one-video scope and the language/translation options imply when this tool applies, but there is no explicit when-to-use or when-not-to-use statement and no named alternative. It is adequate but leaves the agent to infer usage context.

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