AgentIndex x402

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

With no annotations, the description carries the behavioral burden and largely meets it: it discloses the audit scope, the dimensions checked, and the returned score/verdict/issues format. It does not discuss rate limits, authorization, or side effects, but 'audit' and the listed checks imply a non-mutating operational probe.

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 compact and front-loaded with the core action. Every sentence adds value, and the sample call is a practical, low-cost addition rather than 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?

The tool has a minimal schema, no output schema, and no annotations, so the description is the main source of context. It covers inputs, checks, and output well, but lacks guidance on when to use it, method-specific semantics, and any access or rate considerations, leaving noticeable gaps.

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 0%, so the description must explain both parameters. It clarifies 'url' as the target agent/API, but 'method' is only implied by the sample call and the schema default. Accepted values, required format, and behavior across methods are left undocumented.

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 action ('operational audit'), a clear resource ('public agent or paid API'), and enumerates the concrete checks performed. It also states the output score, verdict, and issues, making the tool's goal unmistakable and well differentiated from the broad sibling list.

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 description does not explain when to choose this tool over siblings such as probe, read_web_page, or x402_echo. It gives a sample endpoint but no conditions, exclusions, or comparisons to alternative tools, leaving usage decisions to inference.

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 safety burden, and it does well: 'Check' implies a read-only operation, and it discloses the return fields (balance, can_pay, shortfall). It also points to a sample endpoint. It doesn't discuss errors or exact response shapes, but the core behavior is transparent.

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 tightly packed sentences with no filler. The core action is front-loaded, the return fields are listed, and the sample endpoint is a useful addition that earns its place.

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 check tool with no output schema, the description adequately covers inputs, purpose, output fields, and a discovery hint. It could mention exact value formats or error cases, but nothing critical is missing for an agent to invoke 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 0%, so the description must add meaning. It clarifies that amount is an x402 amount denominated in USDC and notes the $0.001 default. Address is only implicitly described as a Base wallet address, which is inferable but not explicit. The description partially compensates for the missing schema descriptions.

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 ('Check'), a clear resource ('Base wallet'), and a precise condition ('holds enough USDC to pay an x402 amount'). This fully distinguishes it from the unrelated 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?

It explicitly frames the tool as 'the checklist agents run before spending,' giving clear situational context. It does not name any alternative tools or exclusions, but the sibling set is diverse enough that the intended use is obvious.

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?

No annotations are present, so the description must carry the burden. It discloses the core behavior: live prices, 24h change, and accepted identifier forms. However, it does not mention rate limits, data delays, response shape, or behavior for unknown or invalid tickers, leaving some behavioral uncertainty.

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 two short sentences with the main capability front-loaded and identifier handling second. No filler is present, and the sample-path hint is a useful extra rather than unnecessary noise.

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 simple two-parameter read tool, the description covers the return type (prices with 24h change), the input forms, and even a sample hint. Without an output schema, a bit more detail about the response structure would be helpful, but the definition is still adequate for selecting and invoking the 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 0%, so the description must compensate. It does so by explaining that coins can be tickers or CoinGecko ids and that vs_currency is a fiat currency such as USD. It could add exact currency-code formats, but it gives meaningful guidance beyond the raw JSON 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 clearly states the tool provides live spot prices for crypto coins in a chosen fiat currency, with 24h change, and names concrete examples (BTC, ETH, SOL). This is specific enough to distinguish it from all listed sibling tools, which cover news, weather, search, and general extraction.

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 opening clause implies a clear use case: use this tool when current crypto spot prices or 24h changes are needed. It does not explicitly name alternatives or exclusions, but none of the sibling tools overlap with crypto pricing, so the context is sufficient for selection.

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?

The description explicitly states that unsupported fields return null and that an unmatchable schema results in an explicit error. It also notes that values are never invented or approximated, giving clear expectations about tool behavior.

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 concise, front-loaded with the primary purpose, and includes relevant behavioral details without unnecessary fluff. It is well-structured and easy to parse.

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?

The description covers the core functionality, input methods, output format, and error behavior for unmatched schemas. It omits details like schema format specification or potential edge cases, but for a tool of this complexity it is sufficiently complete.

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?

The description clarifies that url and text are alternative input sources and that schema is user-provided for output structure. While it does not detail each parameter individually, the key semantic distinction between url and text is conveyed, compensating for the lack of schema descriptions.

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 clearly states the tool extracts structured data from a URL or raw text into strict JSON. It specifies the resource and output format, making the purpose unambiguous.

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 description mentions it is part of a content kit and suggests trying the sample endpoint, but it does not explicitly contrast with sibling tools or state when to prefer this over alternatives. The reference to /capabilities provides indirect guidance, but explicit conditions for use are absent.

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 provided, the description carries the behavioral burden. It discloses the return shape (verdict, confidence, supporting/contradicting sources with URLs and stance) and a key behavioral trait: returning 'inconclusive' rather than a guess when evidence is unclear. This adds meaningful behavioral context beyond a simple 'check' label.

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 compact and front-loaded: the core purpose comes first, followed by output details and the valuable 'inconclusive' behavior. The sample endpoint and content-kit references are useful orientation, though the last sentence is slightly tangential to invoking the tool correctly.

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 simple one-parameter tool with no annotations and no output schema, the description is largely complete: it explains what the tool does, what it returns, how it handles uncertainty, and even provides a sample endpoint. It could add more detail on confidence-level semantics or source selection, but an agent has enough 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 0% and there is one required 'claim' parameter, so the description must compensate. The phrase 'Check a claim against live web sources' makes it clear that the claim parameter is the statement to verify, which adds meaning beyond the bare schema type. It does not specify format or length limits, but for a single string parameter this is a minor gap.

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: 'Check a claim against live web sources' and specifies the exact output (verdict, confidence, sources with URL and stance). This clearly distinguishes fact_check from sibling tools like search, read_web_page, and summarize, which serve different purposes.

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 context is clear: use this tool when you have a specific factual claim to verify against live web sources, and the tool is designed to avoid guessing by returning 'inconclusive'. It does not explicitly name alternative tools or exclusion conditions, but the use case is unambiguous enough for an agent to select it correctly.

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?

No annotations are provided, so the description carries the burden. It discloses that no RPC key or account is needed, which is useful, and mentions a sample endpoint. However, it doesn't describe the response format, error behavior, or whether the tool returns data for all networks at once or requires a network parameter.

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 two sentences with no fluff. It front-loads the core purpose, lists supported networks, and adds a practical hint about the sample endpoint. Every sentence earns its place.

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 simple one-parameter read tool, the description is mostly adequate. It covers what the tool does and the supported networks, but it lacks details on the response shape and the exact network parameter values. Given the absence of an output schema and annotations, a bit more context would be helpful.

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 0%, so the description must compensate. It mentions the network parameter implicitly by listing supported networks, but it doesn't explain the default value ('base') or the exact format expected. The description adds some context but leaves the parameter semantics partially to inference.

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 ('Read') and resource ('live gas price, EIP-1559 base fee and estimated native-transfer cost') and lists the supported networks. It clearly distinguishes itself from sibling tools like wallet_balance or crypto by focusing on gas price data.

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 description implies when to use this tool: when you need live gas price or fee data across specific networks. It doesn't explicitly state when not to use it or name alternatives, but the context is clear enough for an agent to select it over unrelated siblings.

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. It discloses that the tool is read/synthesis-oriented, produces a sourced JSON brief, and supports status polling and result retrieval. However, it does not explain whether a job must be created first, how long polling might take, or whether there are authentication or rate-limit considerations.

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 compact and front-loaded with the core value proposition, then gives actionable next steps with the sample endpoint. Every sentence contributes useful information without unnecessary padding.

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 tool with no output schema and no annotations, the description leaves key invocation details unclear: the exact endpoint to create a job, how the 'subject' parameter is sent, what the polled status response looks like, and how the final JSON brief is structured. The sample GET /jobs/sample helps but does not fully compensate for these gaps.

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?

The only parameter, 'subject', has no schema description and the overall schema description coverage is 0%. The description never explicitly says that 'subject' is the research topic, query, or input text, so the agent must infer this from the tool name and surrounding text. This is a significant gap for a required 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 clearly states that the tool performs multi-query web research and returns a synthesized, sourced JSON brief in one call. This distinguishes it from a plain search tool, though it never explicitly names the 'search' sibling or defines the async job lifecycle.

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 provides clear context: use this for broad research that would otherwise require many individual search calls. It also mentions free status polling and result retrieval, but it does not explicitly state when to prefer 'search' over 'jobs' or list exclusions.

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. It discloses the output contents and freshness, which is useful, but it does not explain ordering, limit behavior, pagination, errors, or rate limits. The 'Try GET /news/sample' line is vague and adds little concrete behavioral detail.

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 short and front-loaded with the most important information. The first two sentences earn their place, but the final 'Try GET /news/sample' sentence is vague and does not add clear actionable value.

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 simple no-required-parameter list tool, the description conveys what data comes back and when it is useful. However, without an output schema or annotations, it omits response item structure, ordering semantics, and how limit interacts with results, so completeness is only partial.

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?

The only parameter, limit, has 0% schema description coverage and is never mentioned in the description. An agent gets no guidance on how limit affects results or what a sensible value is, and the description was the only place that could have compensated for the schema gap.

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 clearly identifies the resource as current Hacker News headlines and lists the returned fields (title, URL, score, comments), which separates it from siblings like search or read_web_page. It lacks an explicit imperative verb, but 'Top Hacker News headlines right now' conveys the retrieval intent well.

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 use case: agents that need current tech/startup signal and fresh links. It does not name alternatives or exclusion criteria, but the recency framing makes the intended context reasonably explicit.

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?

No annotations are provided, so the description carries the full behavioral burden. It does reveal that the tool returns paywall metadata and works on public URLs, but it never explicitly states that the operation is read-only or side-effect free, nor does it mention how it handles non-public URLs or failures. This is a meaningful gap for an arbitrary-URL probing tool.

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 short and front-loaded with the core purpose, followed by a value proposition and a sample path. There is no redundancy, but the closing 'Try GET /probe/sample' could be confused with the method parameter and is not essential for understanding the tool, slightly undermining an otherwise tight structure.

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, the description conveys the main purpose, the URL input, and the expected output fields, giving a workable baseline. However, it omits method semantics, lacks an explicit read-only or safety statement, and has no output schema to clarify the return structure. The gaps are noticeable but not blocking for straightforward use.

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?

The schema has 0% description coverage, so the description must define both parameters. It explains that 'url' is any public URL, but it says nothing about the 'method' parameter, its default GET value, or what methods are supported. With only one of two parameters semantically covered, this is insufficient.

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 action ('Probe any public URL') and the exact output ('report if it is an x402 paywall - price, network, payTo, scheme'). It clearly differentiates from siblings like read_web_page by focusing on paywall detection, though it does not explicitly name alternatives or exclude other tools, so it stops short of a 5.

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 'Shop paywalls before you spend' implies the intended scenario (checking paywalls before payment), and 'Try GET /probe/sample' gives an example. However, there is no explicit guidance on when not to use the tool, nor any comparison to alternatives such as can_pay or read_web_page, leaving usage purely implied.

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 burden of behavioral disclosure. It explicitly states that text content is never generated or altered, only structure is inferred, and it discloses metadata extraction (title/author/page count/date) and a real token count. It does not mention rate limits or failure modes, but the core behavioral traits are clearly disclosed.

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 compact and front-loaded with the core action and input format. The extra details (metadata, token count, sample endpoint, content kit) are useful and each sentence earns its place. It is slightly dense with the parenthetical list of sibling tools, but not bloated.

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, the description covers the input modes, the output format, the behavioral guarantee, and a sample endpoint. It does not explain the exact structure of the returned Markdown or error cases, but the provided information is sufficient for an agent to invoke it correctly in most cases.

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 0%, so the description must compensate. It explains that the tool accepts a public PDF by HTTPS URL or base64, which directly maps to the two parameters (url and pdf_base64). It does not detail the exact base64 format or URL requirements, but it gives enough semantic meaning for an agent to understand the two input options.

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 ('Extract'), a specific resource ('a public PDF by HTTPS URL or base64'), and a concrete output ('clean Markdown'). It also distinguishes itself from siblings by naming the content kit (pdf, web-read, extract, summarize, detect-language) and pointing to GET /pdf/sample, so an agent can tell it apart from read_web_page and extract_structured.

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 description gives clear context: it handles public PDFs via URL or base64 and produces Markdown. It does not explicitly state when not to use it or name alternatives like read_web_page for non-PDF content, but the content-kit mention and the sample endpoint provide enough context for an agent to select it for PDF-specific extraction.

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 behavioral disclosure burden. It explains what the tool does with the fetched page, what output to expect (clean markdown, stripped navigation/ads, resolved links, token count), and notes that it is standalone extraction. It does not cover failure cases or URL access limits, but the core behavior is well disclosed.

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 with the core purpose and output, then adds useful context about the sample endpoint and the content kit. The final sentence about the content kit is slightly tangential but still helpful for tool discovery, and the overall length is appropriate.

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?

Given the low complexity (one parameter, no annotations, no output schema), the description covers the essential behavior: input URL, output markdown, token count, and relationship to /search. It lacks explicit error/limitation notes, but is otherwise sufficient for an agent to select and invoke the 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 coverage is 0% and the description adds almost no parameter semantics beyond the schema's 'url' string. It does not specify accepted URL schemes, formats, or constraints, and mostly restates that the tool fetches a URL. For a low-coverage schema the description should compensate more than this.

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 opens with a specific verb and resource: 'Fetch a URL and return its main article as clean markdown.' It also clearly distinguishes this from sibling tools like read_pdf by focusing on web pages, and adds distinctive details like stripping boilerplate and returning a token count.

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 description gives clear context: this is for fetching a URL you already have, using the same extraction as /search on result pages. It does not explicitly state when not to use it or name alternatives like extract_structured or summarize, but the intended use case is reasonably clear.

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 behavioral burden and does well: it discloses no-account/no-key/no-quota, up to 10 results, default top-3 full-page Markdown, optional summary, and multi-query merge/dedup behavior. It omits error handling and exact response formatting, which prevents a top score.

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 and front-loaded, with the core behavior in the first sentence. The 'Try GET /search/sample' and content-kit references are mild extras but do not bloat the text; every sentence adds some value.

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?

Despite having no output schema, the description provides a fairly complete picture: return contents, defaults, batching, and authentication requirements. It lacks exact field names and error behavior, but an agent can reasonably infer how to invoke the tool 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 description coverage is 0%, so the description must compensate, and it largely does: query's array form and batching, extract/summarize toggles, max_results implied by 'up to 10 results', content_results and include_content implied by 'top 3 full-page Markdown'. Only content_chars is left unexplained.

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 action—real-time web search and content retrieval—with concrete result details (title, URL, snippet, publish date) and full-page Markdown for top results. The verb and resource are clear, though it does not explicitly differentiate from siblings like read_web_page or summarize.

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: it describes what the tool does, including batch query support, and points to the AgentIndex content kit and GET /capabilities. However, it does not explicitly state when to use this tool versus alternatives such as read_web_page or news, nor does it provide when-not-to-use guidance.

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?

Mentions neutrality, factual accuracy, and source attribution, which are behavioral guarantees. However, with no annotations, it lacks explicit mention of side effects, auth, or rate limits, though the tool is likely read-only.

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?

Concise, two sentences plus a tip and context note. Front-loaded with the main action and outputs.

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?

Covers main behavior and mentions source attribution, but doesn't describe return format or edge cases. Given no output schema, it's reasonably complete for a simple summarizer.

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?

The description implies the four parameters (url, html, text, length) but gives no formal definitions or constraints. It mentions 'short, medium or long' for length but doesn't document allowed values or interactions between inputs.

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?

Clearly states the tool summarizes input content (URL, text, HTML) at a requested length. Though it doesn't explicitly contrast with sibling tools, the verb and resource are specific enough to distinguish it.

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?

Provides a sample endpoint and notes it's part of a content kit, but does not specify when to choose this over other tools like extract_structured or detect_language. No explicit guidance on use cases or prerequisites.

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 provided, the description carries the full burden and does so impressively. It discloses batching limits, preservation of Markdown/HTML/{x} placeholders, order and count integrity, source auto-detection, multi-model fallback for uptime, exclusion of providers that train on prompts, and no-account/no-API-key access. This goes well beyond a generic 'translate text' statement, though it omits output shape and error behavior.

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 a dense, front-loaded paragraph that leads with the core function and batching advantage before listing supporting features. Each clause adds distinct information, so there is little waste. The closing 'Try GET /translate/sample' is a minor appendix but not harmful; the structure could be slightly improved with bullets, though it remains clear.

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?

Given 4 parameters, no output schema, and no annotations, the description covers many practical aspects: input flexibility, format preservation, auth requirements, and provider privacy. However, it leaves meaningful gaps: the return shape is not explicitly stated (string vs array), there is no guidance on what happens beyond 200 segments, and target_lang's accepted value format is unspecified. These are relevant for an agent to invoke the tool correctly and interpret results.

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 0%, so the description must compensate, and it does partially. It explains that text can be a single string or array, clarifies source_lang through auto-detection, and illustrates preserve_format via placeholder and markup preservation. However, it does not specify the expected format for target_lang (e.g., language codes like 'es') and only implicitly covers preserve_format's full semantics.

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 ('Translate') and resource ('text segments'), and quantifies the capability ('up to 200 text segments in a single call'). It clearly positions the tool as a translation endpoint and is unmistakable against siblings 'jobs' and 'search', which are unrelated. The added context about replacing 200 separate calls further sharpens the purpose.

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 description provides clear contextual guidance: use for batch translation of up to 200 segments, a single string also works, format is preserved, and source language is auto-detected. It does not explicitly mention when not to use the tool or name alternative tools, but given the siblings are unrelated to translation, the guidance is sufficient for an agent to select it appropriately.

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 provided, the description carries the full burden of behavioral disclosure. It clearly indicates a read-only operation, mentions the absence of auth requirements, and specifies that results include atomic amounts and the current block. It doesn't cover rate limits or error conditions, but for a simple read tool this is solid context.

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 two sentences with zero fluff. The main action, scope, and networks are front-loaded, followed by the auth note and a sample endpoint hint, making every word earn its place.

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, the description covers purpose, supported networks, output highlights, auth requirements, and even a sample endpoint. It doesn't detail the exact output field names or structure, but that is a minor gap given the tool's simplicity.

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?

The schema only defines 'address' and 'network' as plain strings with 0% description coverage, so the description must compensate. It does so by explaining that address is any EVM wallet and by enumerating the supported networks for the 'network' parameter. However, it doesn't explicitly map these to parameter names or mention the default network, leaving a minor gap.

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 ('Read') with a precise resource ('native coin and USDC balances for any EVM wallet') and explicitly lists supported networks. This clearly differentiates it from the generic sibling tools like weather or search, and from financial siblings like gas_price by focusing on wallet balances.

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 description gives clear context by noting it works across five networks and requires no RPC key or account, positioning it as a quick, credential-free balance check. It stops short of explicitly naming alternatives or stating when not to use it, so it earns a 4 rather than a 5.

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 provided, the description carries the burden of behavioral disclosure. It does disclose the return categories and multi-network scope, and the 'returns' wording implies a read-only operation. Still, it omits details such as data freshness, network availability, failure behavior, or required authentication — meaningful context for an unannotated tool.

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 compact and efficient: a lead sentence stating the core value, a sentence listing outputs, and a practical sample-endpoint hint. The final 'Try GET /wallet-intelligence/sample' is slightly extraneous but not wasteful. Overall, it is well-structured and front-loaded with the most important information.

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?

The tool has 3 parameters, no annotations, and no output schema, so the description must provide most of the operational context. It only covers high-level capabilities. Missing pieces include how networks are specified (format/values), the role of amount_usdc, the exact return structure, and error/edge-case behavior. This is insufficient for an agent to invoke it reliably.

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 0% and the description does not compensate: it never explains the meaning of address, networks, or amount_usdc. An agent cannot infer from the text that networks is an optional array of chain names or that amount_usdc probably controls the x402 payment readiness check. This is a critical gap.

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 uses a specific verb-resource construct: 'One call replaces wallet-balance and gas reads' and enumerates the concrete outputs (native and USDC balances, current blocks, fees, Base x402 payment readiness). It explicitly frames this tool as an aggregate alternative to wallet_balance and gas_price, making it distinguishable from its siblings 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?

The description clearly positions the tool as a replacement for separate wallet-balance and gas reads, implying 'use this when you need combined multi-chain wallet/gas data'. It also notes a cost benefit ('One signature instead of ten paid requests'). However, it does not state explicit exclusions, such as when to choose wallet_balance alone or when a specific network is unsupported.

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 provided, the description carries the behavioral disclosure burden. It usefully reveals the auth-free nature and the scope of data, and the sample endpoint is a helpful preview. However, it does not explain units, behavior when both city and coordinates are supplied, error conditions, or the response structure.

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 tightly written sentences put the core purpose first, then add the critical auth fact and a practical sample endpoint. Every sentence earns its place 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?

For a simple 3-parameter weather lookup, the description covers the essential selection criteria and output fields. Because there is no output schema, the return structure and units are not specified, but the sample endpoint partially compensates for that gap.

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 0%, so the description must compensate. It maps the parameters conceptually by saying 'any city or lat/lon', which adds meaning beyond the bare schema. Still, it does not define formats, constraints, or whether lat/lon must both be provided, leaving some ambiguity.

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 clearly states the tool provides current weather and a 3-day forecast with specific output fields (temperature, humidity, wind, conditions) and accepts city or lat/lon inputs. This makes it unambiguous and sufficiently distinct from sibling tools like crypto, news, and search.

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 explicitly notes that no account or API key is required and gives a concrete sample endpoint to try. It does not explicitly exclude alternatives or state 'use this when needing weather', but the context is clear enough for an agent to route weather-related requests to this tool.

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 of behavioral disclosure. It does reveal that this is a paid call (costs $0.000001), that it settles a transaction, and that it returns payer identity, echo, and receipt. However, it does not mention any authentication requirements, potential side effects (e.g., irreversible money transfer), or rate limits, which would be important for an agent to 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 two sentences, both informative and front-loaded with the core purpose and cost. The suggestion to 'Try GET /x402-echo/sample' is a practical pointer that doesn't add unnecessary bulk. No wasted words, and the structure guides the agent from what it does to how to try it.

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 tool with only one optional parameter and no output schema, the description covers the essential context: purpose, cost, outputs, target audience, and a sample endpoint. It doesn't mention prerequisites like having a wallet funded, but given its simplicity and the explicit cost disclosure, it's sufficiently complete for an agent to understand the call and its effects.

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?

The schema has one optional parameter 'message' with default 'hello agent', and schema description coverage is 0%. The description's phrase 'request echo' implies the message is what gets echoed back, but it does not elaborate on format, constraints, or interaction with defaults. It adds minimal semantic value beyond what the schema already provides, so a score of 3 is appropriate given the low coverage 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?

The description clearly states a specific verb and resource: it 'settle[s] exactly one atomic unit of USDC' and 'receive[s] payer identity, request echo and receipt.' This distinguishes it from sibling tools like wallet_balance or can_pay by focusing on a conformance call with a fixed cost and specific outputs. It also names the testing audience (wallets, Payment-Signature, facilitators, settlement), making the tool's role unambiguous.

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 description provides useful context ('Built for agents testing wallets, Payment-Signature, facilitators and settlement') and even suggests a sample endpoint, but it does not explicitly state when to choose this tool over alternatives like wallet_balance or can_pay, nor does it mention any exclusions or conditions for use. The when-to-use guidance is implied through the intended audience rather than stated directly.

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