mercury-x402-mcp

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

With no annotations, the description fully discloses behavior: returns signed receipt, deterministic verdict, responseMs not signed, keyless, no LLM, no signup, pricing. It is transparent about what is signed and what is telemetry.

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-loads the main action ('URL → a signed uptime/status probe'), and every sentence adds value without redundancy.

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 no output schema, the description explains all return values (up/down, HTTP status, class, reason, final URL, responseMs, provenance receipt) and is complete for a single-parameter 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 100% and the schema already describes the url parameter. The description does not add new semantics beyond confirming what the input is (URL), so baseline 3 is appropriate.

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's purpose: probing a URL for availability (up/down, HTTP status, etc.) and distinguishes it by mentioning 'offline-verifiable provenance receipt' and 'SLA evidence', setting it apart from siblings like fetch, headers, etc.

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 usage for SLA verification and mentions deterministic verdict, but lacks explicit guidance on when to use this tool vs siblings or prerequisites. No when-not-to-use or alternatives are discussed.

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 full burden and discloses key behaviors: deterministic output (byte-identical root and proofs for same inputs), SSRF-guarded per URL, payment model (keyless x402, USDC on Base mainnet), and cost per call. It also implies read-only snapshot (no mutation).

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 extremely concise (two sentences) yet packed with essential information. It is front-loaded with the core purpose and output, followed by key traits. Every sentence adds value without redundancy.

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 no output schema, the description adequately covers return values: clean content per URL plus a signed receipt with Merkle root and per-page membership proofs. It also includes cost, determinism, and security constraints. Missing details like exact receipt format or array structure are minor given the tool's complexity.

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% with descriptions, so baseline is 3. The description adds meaningful context beyond schema: for 'urls' it explains 'each is fetched through the shared SSRF-guarded engine and becomes one Merkle leaf', and for 'format' it clarifies 'clean text (default) or structure-preserving markdown for every page'. This enriches the agent's understanding.

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 action ('snapshot multiple URLs'), the resource ('list of URLs'), and the specific outputs ('clean content for each + ONE signed receipt committing to a MERKLE ROOT'). It also distinguishes from siblings like 'fetch' and 'markdown' by emphasizing multi-page tamper-evident snapshot with proofs.

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 explains when to use this tool (for tamper-evident multi-page snapshots with proofs), constraints (≤20 URLs, deterministic, $0.02/call), and contextual details (SSRF-guarded, keyless x402). It does not explicitly state when not to use or list alternatives, but the sibling tool names imply single-page 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 full burden. It discloses key behaviors: refetching, deterministic change detection, signed receipt generation, stateless operation, and cost. However, it does not mention side effects, authorization requirements, or data retention, leaving some behavioral aspects unclear.

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-loading the core mechanism and output. It packs significant information into a few sentences without redundancy. Slight improvement could be splitting into bullet points for clarity, but it remains efficient.

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 lacking an output schema, the description explains the output (changed? + signed receipt) and how receipts chain into an audit trail. It also mentions cost, statelessness, and keyless x402. This provides a complete understanding of the tool's functionality and context for 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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the semantic difference between prevHash (hash-only proof) and prevText (full diff), and clarifies the outcome for each. This goes beyond the 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's purpose: given a URL and prior hash or text, it refetches the page, detects changes, and produces a signed receipt. It distinguishes itself by emphasizing deterministic change-proof and audit trail features, differentiating it from sibling tools like fetch or markdown.

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 explains when to use the tool (to compare page versions) and how to choose between prevHash and prevText parameters. However, it does not explicitly state when not to use it or compare it to alternatives among the 17 sibling tools, leaving room for ambiguity in tool 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?

No annotations provided, so description bears full burden. It discloses deterministic normalisation, mutability of DNS, and the signed/timestamped nature. Does not mention rate limits or auth, but pricing is noted.

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 informative but slightly verbose. Front-loads the core action and key features. Every sentence adds value, though could be tightened.

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?

No output schema, but description explains the return format (record set with provenance). With sibling tools and high parameter coverage, it is sufficiently complete for a DNS resolution 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 100% with good descriptions. The description adds context: 'alternative to ?domain= — any http/https URL; the registrable hostname is resolved.' This clarifies the relationship between url and domain.

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 it resolves DNS records (A, AAAA, MX, etc.) and returns a signed, timestamped snapshot with provenance. It distinguishes from sibling tools like fetch or headers by focusing on DNS resolution.

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 implies use for DNS snapshot needs, mentions keyless/no signup, but does not explicitly say when to avoid or compare to alternatives. However, sibling tools are distinct enough that context aids 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?

Extensively discloses behavioral traits: no LLM call, no API key, no signup, deterministic extraction, honest charge-per-attempt, SSRF guard, 5s timeout, 10MB cap, paid via HTTP 402. This goes well beyond what missing annotations would provide.

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 long but front-loaded with the core purpose. Some details (cost, payment) are necessary but could be more terse. Overall, every sentence adds value, but some consolidation would improve conciseness.

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 lack of output schema, the description fully explains the output format (typed JSON, null for absent fields) and error behavior (ok:false reason). It covers all critical context for an agent to use 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 coverage is 100%. The description adds meaning by explaining compact form vs JSON-Schema, giving examples, and noting resolution sources. However, it does not mention the optional 'format' parameter, which 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?

Clear verb+resource (extract structured JSON from URL) and explicit differentiation from sibling /buy/fetch by emphasizing type-safe JSON vs raw text or string-only fields. The description leaves no ambiguity about what the tool accomplishes.

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 compares with sibling /buy/fetch, stating that this tool provides schema-conformant objects for LLM/RAG/trading pipelines. However, it does not provide when-not-to-use or mention other alternatives beyond that sibling.

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 provided, so description carries full burden. It details normalization steps (CDATA unwrapped, HTML stripped, dates ISO-8601), signing (EIP-191), determinism, and cost. No behavioral surprises.

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?

Dense but structured: arrow notation, bullet-like lists, parenthetical clarifications. Every sentence adds value with no fluff.

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 input, output fields, processing details, signing, cost, and determinism. No output schema needed given listed fields. Complete for single-param 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 covers the single parameter with description. The description adds overall context but no specific parameter details beyond what schema states (maxLength, protocol). Baseline 3 applies.

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 specifies the tool takes an RSS/Atom feed URL and returns a signed, normalized item list with explicit fields. It clearly distinguishes this from general fetch/extract tools by detailing feed-specific normalization and signing.

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 use for RSS/Atom feeds but does not explicitly compare to sibling tools like 'fetch' or 'extract'. It could clarify when to choose 'feed' over these, but the context is clear enough.

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 fully discloses behavioral traits: returns structured results (success or failure), follows redirects, SSRF-guarded, 5s timeout, 10MB cap, payment via 402, and the cryptographic attestation mechanism. This goes well beyond a simple 'fetch a URL' description.

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 verbose and includes marketing language and competitive comparisons that may not be essential for an AI agent. While front-loaded with the core value proposition, it could be more concise without losing critical 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 description is extremely complete given the complexity. It covers authentication (keyless), payment (402), output format (attested, structured), error handling, rate limits (implied via $0.003/call), and edge cases (redirects, SSRF, timeout, cap). No output schema is provided, but description suffices.

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% and the description adds significant context: details the 'extract' parameter's JSON mode, explains the difference between 'format' options, and describes the 'links' parameter. This enriches the semantic understanding beyond the schema alone.

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's purpose as a 'VERIFIABLE keyless web-read for autonomous agents' that fetches URLs and returns clean text with provenance. It distinguishes itself from competitors (Jina, Firecraw) and sibling tools by emphasizing keyless access, cryptographic attestation, and deterministic 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 description provides explicit guidance on when to use this tool (RAG, trading, agent-to-agent commerce requiring provenance) and how it differs from alternatives (no API key, no human loop, verifiable vs. hearsay). It also contrasts with paid tools that gate behind API keys.

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 adds value beyond missing annotations by stating the tool is 'deterministic', requires no authentication, provides a signed receipt, and costs $0.005/call. It does not cover error handling or rate limits, but the key behavioral traits are well communicated.

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 wasted words. It front-loads the input-output pattern ('URL → ...'), immediately conveying purpose and 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?

Given one parameter with full schema coverage and no output schema, the description sufficiently explains the output (letter grade, findings, receipt), the deterministic nature, and cost. It could mention handling of invalid URLs, but overall it is complete for 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?

With 100% schema description coverage for the single 'url' parameter, the description adds no new parameter-specific details beyond the schema. It meets the baseline for this dimension.

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 performs an HTTP security-headers audit for a given URL, listing specific headers (HSTS, CSP, etc.) and outputs a letter grade, findings, and a provenance receipt. This distinguishes it from sibling tools like 'fetch' or 'metadata' 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 description implicitly indicates usage via 'URL → ...' and notes 'keyless, no LLM, no signup', suggesting it's for security header checks. However, it lacks explicit guidance on when to use this tool versus alternatives or when not to use it.

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 discloses key behavioral traits: determinism (same bytes yield identical graph), no LLM involvement, coverage limited to <a> tags (not rel/asset tags), signed receipt via EIP-191, and pricing. Without annotations, the description fully informs the agent of the tool's behavior and constraints.

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 informative, front-loading the core function and using concise language. Every sentence serves a purpose, from input-output mapping to behavioral guarantees and pricing. Slightly verbose, but still well-structured and efficient.

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 only one parameter and no output schema, the description thoroughly explains the output structure (grouped by origin, histograms, counts), determinism, scope limitations, and receipt. It provides all necessary context for the agent to understand what the tool returns and how it behaves.

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 input schema has 100% coverage for the single 'url' parameter, describing it as 'the page to map (http/https)'. The tool description adds overall purpose but no new parameter-specific details, so the schema already provides adequate documentation, warranting a baseline 3.

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 every <a href> with anchor text, classifies links as internal/external relative to the page origin, groups by origin with a third-party histogram and same/cross-origin counts. It specifies the resource (link graph), the verb (extract/classify), and distinguishes from sibling tools (e.g., fetch, metadata) by its unique behavioral guarantees.

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 context for when to use the tool: when an outbound/internal link graph with classification and grouping is needed. It implies usage by specifying what it covers (<a> hyperlinks only) and what it does (deterministic, no LLM). However, it does not explicitly state when not to use it or mention alternatives.

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 fully carries behavioral disclosure: it details stripping of boilerplate/nav/ads, preservation of headings/lists/links, provenance receipt, determinism, and cost. This is comprehensive for a read-only fetch 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 dense but efficiently packs key information front-loaded. Each sentence adds value (cleaning, determinism, receipt, cost). Slightly long but appropriate for the detail.

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 single-parameter tool with no output schema, the description covers purpose, behavior, determinism, and cost. It fully prepares the agent to use the tool without missing critical context.

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 sole parameter 'url' is described in schema. The description adds value by explaining the deterministic behavior (same URL + same bytes = same markdown) and the clean output focus, going beyond the schema's basic description.

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 converts a URL to clean, LLM-ready markdown, specifying what it strips and preserves. It positions itself as a 'RAG-ingest primitive', differentiating it from siblings like fetch or readability.

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 usage for RAG ingestion and deterministic extraction, but does not explicitly say when not to use it or compare to alternatives like fetch. The context of sibling tools provides some indirect 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?

With no annotations, the description effectively discloses behavior: deterministic, keyless, no LLM, signed output, and cost. It does not mention destructive actions or auth, which are likely unnecessary.

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 very concise, using bold for key aspects, front-loading the core purpose, and including pricing. Every sentence adds 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?

Given the single parameter and no output schema, the description is complete: it explains the output format (JSON-LD, OpenGraph, etc.) and the signed, provable nature. No additional details are needed.

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 'url' is fully described in the schema. The description adds significant meaning about the output (signed, typed metadata record), which aids in parameter understanding.

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's purpose: given a URL, returns a signed metadata record combining multiple metadata formats. It distinguishes from siblings by highlighting deterministic, keyless, no LLM, and provable output.

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 usage for obtaining metadata records but provides no explicit guidance on when to use this tool over siblings like 'fetch', 'headers', or 'links'. Usage context is left 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?

Despite no annotations, the description discloses key behavioral traits: it is deterministic, keyless, involves no LLM, and produces a signed receipt with a cost per call. It does not mention any side effects, authentication needs, or rate limits, but the core behavior (witnessing bytes at a time) is well explained.

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 extremely concise (two sentences) and front-loaded with the essential action and output. Every sentence adds value: the first explains what the tool does and its output, the second explains its significance and pricing. No unnecessary words.

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 absence of an output schema, the description adequately explains the return value (signed receipt with contentHash and timestamp) and its purpose. Parameter details are clear, and the context signals (2 params, 100% coverage) are well addressed. The tool's function is fully understandable from the description alone.

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% (both parameters have detailed descriptions), so the baseline is 3. The description adds value by explicitly stating the mutual exclusivity of 'url' and 'content', which is not fully captured in individual parameter descriptions. It also reinforces the 256KB size limit for content.

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's purpose: notarizing inline or URL content into a signed, offline-verifiable provenance receipt. It specifies the verb 'notarize' and the resource 'content', and the mention of 'sha256 contentHash + witnessed timestamp' distinguishes it from sibling tools like 'fetch' or 'extract' which only retrieve content.

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 explains when to use the tool (to get a third-party witness for byte existence) and provides a clear instruction to provide either 'url' or 'content' but not both. However, it does not explicitly state when not to use this tool or mention 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 full burden. It discloses determinism (no LLM, byte-identical output), cost ($0.005/call), output fields, and the stripping process using DOM density heuristics. It does not cover error handling or non-article URLs, but provides substantial 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 extremely concise: two sentences, front-loaded with purpose, followed by details on behavior and cost. Every sentence adds value with no redundancy.

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 simple single-parameter input and no output schema, the description is remarkably complete: it explains the output format, cleaning algorithm, determinism, and pricing. It could briefly mention error cases (e.g., non-article pages) but is otherwise sufficient.

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% for one parameter (url). The description adds significant meaning beyond the schema: it explains the output structure, cleaning mechanism, determinism, and pricing, helping the agent understand the tool's behavior and appropriate use.

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's function: converting a URL into a clean article record with specific fields (title, byline, publishedAt, text) by stripping boilerplate. It distinguishes itself from sibling tools like 'markdown' and 'extract' by emphasizing it's a 'clean-citation primitive' using deterministic DOM heuristics, not raw markdown.

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: for extracting clean article text, distinct from raw markdown (sibling 'markdown'). It provides context but lacks explicit when-not-to-use or alternatives. The mention of 'clean-citation primitive' helps differentiate from other extraction tools like 'extract' or 'metadata'.

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 fully discloses behavior: follows HTTP 3xx only, no JS execution, deterministic, provides a signed receipt (EIP-191), and includes cross-origin flags. It also explains what it does not do (follow meta-refresh).

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 efficiently packed with information about behavior, output, and limitations. It is front-loaded with the main purpose. However, it could be slightly more structured (e.g., separate sentences for clarity) without losing content.

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 single parameter and no output schema, the description explains all output components (hop chain, final URL, canonical, hop count, cross-origin flag, distinct origins, signed receipt) and limitations (no JS/meta-refresh execution). It is thorough enough for an agent to understand what to expect.

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% with a clear description of the 'url' parameter. The tool description adds context about what the URL is used for (resolving shortlinks, affiliate URLs) but does not add substantial new semantic meaning 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 clearly states the tool resolves a URL through redirects, providing the full hop chain, final URL, canonical link, and other details. It uses specific language like 'URL → a SIGNED redirect/canonical resolution', and distinguishes from sibling tools like 'fetch' or 'headers' by focusing on redirect tracing and signed output.

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 specifies that it follows HTTP 3xx only (no JS/meta-refresh) and is deterministic. It mentions the cost per call and the signed receipt. However, it does not explicitly state when to use this tool versus alternatives like 'fetch' or 'validate', though the specialization is 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?

Discloses key behaviors: deterministic (no LLM), signed and timestamped output to prove policy at fetch time, and cost ($0.005/call). No annotations provided, so description fully carries this burden.

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?

Single sentence conveying purpose and key features, plus pricing as independent clause. No fluff, front-loaded with core function.

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?

No output schema; description does not detail the structure of the signed verdict (e.g., which fields, format). Pricing is useful but not part of tool semantics. Lacks return value specifics.

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 has 100% coverage; description adds context that path is optional and defaults to '/', evaluating verdict for a specific path. The phrase 'whole-site question' clarifies default behavior.

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?

Description clearly states it audits a domain for per-AI-crawler allow/block policies from robots.txt, llms.txt, ai.txt, with deterministic, signed, timestamped results. Distinguishes from sibling tools like fetch, headers, dns by focusing on crawler policy evidence.

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?

Implicitly guides usage for auditing crawler policies, but lacks explicit when-not-to-use or alternatives. Could mention that raw content retrieval tools like fetch or sitemap are for different purposes.

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 fully discloses behavior: it discovers sitemaps via robots.txt then fallback, parses multiple sitemap types, deduplicates, bounds results to ≤2000, signs the deterministic part, and notes that the optional fetch is non-deterministic and SSRF-guarded.

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 every sentence adds value. It is front-loaded with the core function. A minor reduction in length could improve readability, but it remains clear and informative.

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 the tool's complexity (multi-step sitemap discovery, optional fetch, signing), the description covers all critical aspects without relying on an output schema. It explains the deterministic nature, bounding, and the non-deterministic probe.

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%, and the description adds meaningful context beyond the schema descriptions: 'url' uses only the origin, 'fetch' is a liveness probe not covered by the signature, and 'limit' defaults to the maximum.

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 is specific and actionable: it takes a domain/URL and returns a signed snapshot of the published sitemap. It clearly distinguishes from sibling tools like 'robots' and 'fetch' by focusing on sitemap discovery and parsing.

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 explains what the tool does and mentions the optional fetch probe for liveness. It does not explicitly state when not to use it or point to alternatives (e.g., 'fetch' for individual pages), but the context from sibling tools makes it fairly 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?

No annotations provided, but description fully discloses: deterministic, keyless, no LLM, pricing ($0.006/call), and provenance receipt. Adequately covers behavioral traits.

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?

Extremely concise: two sentences covering purpose, output, pricing, and constraints. No waste.

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?

No output schema, but description explains return types (JSON+CSV) and includes pricing. Complete for a data extraction 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 100% with descriptions. Description adds value by explaining output format ('typed, header-keyed rows', 'clean RFC-4180 CSV') and hinting at format 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?

Description clearly states it parses HTML <table> elements from a URL into typed, header-keyed rows (JSON) and RFC-4180 CSV. Distinguishes from siblings like 'fetch' (raw content), 'markdown', etc.

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?

Clear when to use (extract tables from web pages), but no explicit when-not-to-use or alternative tools. Context from sibling names helps, but could be more 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?

The description discloses key behavioral traits: deterministic, keyless, no LLM, x402 payment on Base mainnet, cost per call, and provenance binding to response bytes and schema. With no annotations provided, the description carries full burden and covers safety, payment, and determinism comprehensively.

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 and front-loaded with the core function. While it includes useful details about payment and provenance, some phrases (e.g., 'USDC on Base mainnet') could be trimmed without loss of clarity.

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 complexity (3 parameters, no output schema, no annotations), the description explains inputs, outputs (pass/fail, errors, receipt), and behavioral guarantees. It does not detail output format, but the provenance receipt and verdict are mentioned.

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?

Input schema already provides detailed descriptions for all three parameters (100% coverage). The description reinforces the purpose but does not add significant new meaning beyond what the schema defines, meeting the baseline for high schema coverage.

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 validates a JSON/API endpoint against a JSON schema, returning deterministic pass/fail with per-field errors and a signed provenance receipt. The verb 'validate' and resource 'URL + JSON Schema' are specific, and it distinguishes from siblings like 'fetch' or 'extract'.

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 explicitly state when to use this tool versus alternatives. While it implies usage for deterministic validation without LLM, it lacks explicit guidance on when not to use it or how it compares to sibling tools like 'fetch' combined with external validators.

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