x402-data-gateway

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions 'No auth, no API keys; pay-per-call USDC', but lacks details on rate limits, error handling, data retention, or what happens on invalid domains. This is insufficient for a tool that likely makes external calls and produces complex output.

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 long with no redundant phrases. The first sentence efficiently lists all output components, and the second explains bundling and payment. It is well-structured and concise.

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 tool's complexity (multiple output types), the description covers most components but lacks details on return format (e.g., how the JSON-LD fix list is structured, pagination limits, etc.). No output schema exists, so the description should provide more structural clarity. It is adequate but not 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 single parameter 'domain' has 100% schema coverage with its description. The tool description adds nothing beyond what the schema already provides (e.g., 'A domain or URL, e.g. stripe.com'). 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 performs a comprehensive AI-search-readiness audit including firmographics, tech stack, contacts, DNS/email infra, an AI-readiness score, gap analysis, JSON-LD fix list, and a letter grade. It distinguishes itself from siblings by calling itself 'the bundled deep tier (enrich + schemaforge in one call)', making its purpose specific and distinct.

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 when both enrich and schemaforge functionalities are needed, but it does not explicitly state when to use this tool versus alternatives like enrich, schemaforge, extract, read, or scan. No when-not or exclusion criteria are provided.

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. It discloses that the tool is read-only (no mutation), requires no authentication, and costs per request. However, it omits details on rate limits, error handling, or response format, which limits full transparency.

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 effective, front-loading the core purpose and then listing output categories. It is slightly long but each part adds value, and the structure is clear. A minor improvement could be trimming redundant phrases.

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 one parameter and no output schema, the description thoroughly explains what the tool returns (identity, tech stack, social, contacts, DNS, AI signals) and usage context (pay-per-request, no auth). It provides all necessary information 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?

The schema already fully describes the single 'domain' parameter with an example. The description adds little beyond restating that a domain is input. Given 100% schema coverage, the baseline is 3, and the description does not provide additional semantic value.

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 'enrich' and states it converts a domain into company intelligence, listing numerous output categories. It distinguishes itself from siblings like 'deep_audit' and 'wallet_enrich' by focusing on general company data enrichment.

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 states the tool is easy to use (no auth, no API keys, pay per request) and implies its use for enriching domains into company profiles. However, it does not explicitly mention when to avoid this tool or suggest alternative siblings for different tasks.

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 must disclose behavioral traits. It lists the output components but does not mention potential limitations (e.g., URL accessibility, authentication needs, rate limits, or error handling). The description is informative but not comprehensive regarding 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 single, well-structured sentence that immediately states the tool's core function (URL -> clean structured data) and enumerates the key output fields. No unnecessary words or repetition.

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 provides a detailed list of extracted elements (title, description, text, JSON-LD, meta, headings, links, AI-readiness signals), giving a good sense of the tool's output. However, it does not specify the output format (e.g., JSON structure) or whether these are returned in a particular object shape.

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 describes the single parameter 'url' as 'Public http(s) URL to extract' (100% coverage). The description does not add additional meaning beyond this, such as formatting constraints or supported protocols. Baseline 3 is appropriate as the schema already covers the 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?

Description clearly specifies the tool's action: extracting structured data from a URL, listing specific output fields (title, description, text, JSON-LD, meta, headings, links, AI-readiness). It distinguishes itself from siblings by focusing on extraction of clean structured data, vs. 'read' (likely raw content) or 'scan' (likely checking).

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?

Description implies usage when structured data extraction from a URL is needed, but does not explicitly state when to use this tool vs. alternatives (e.g., 'read' for simple fetch, 'scan' for security checks). No exclusion conditions or prerequisites are mentioned.

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 the behavioral trait of stripping nav/ads/scripts and preserving structure, which adds value beyond the schema. However, it omits details on error handling, authentication, or rate limits, leaving gaps for a tool with no annotations.

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, efficient, and front-loaded with the core purpose, containing no redundant wording.

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 output schema or annotations, the description covers the key aspects: input, output format, and transformation behavior. It is largely complete, though an example or error handling note could enhance it.

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, the baseline is 3. The description adds meaningful context about the output format and content processing, going beyond the parameter's schema 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 takes a URL and returns full page content as clean Markdown, with specific transformations. However, it does not explicitly differentiate from sibling tools like extract or scan.

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 no guidance on when to use this tool versus alternatives. It only implies usage for reading full pages, but no exclusions or comparisons.

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 must cover behavior. It lists what it flags (exfil sinks, obfuscation, etc.) and output risk levels. Implies no side effects, but could explicitly state it is 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?

Single sentence packs all essential info: purpose, scope, what it flags, output format. No wasted 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?

For a simple scan tool with no output schema, description covers purpose, input, and output (risk levels). Complements sibling tools by specifying its niche.

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 100% of param with description 'Public GitHub repo: owner/name or URL'. Description adds meaning beyond schema by specifying input format alternatives.

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 performs a static supply-chain security scan of a public GitHub repo, specifically before installation or execution. It distinguishes from siblings like deep_audit by focusing on pre-install checks.

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

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

Explicitly says 'before an agent installs/runs it', giving a clear usage context. Does not mention alternatives or when not to use, but context is strong.

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 full burden. It discloses outputs (bundle, diff, fix list) and outcome (page citation eligibility) but omits side effects, auth needs, or rate limits. The description is moderately transparent but lacks behavioral depth.

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 three sentences, front-loaded with the main action and key outputs. While efficient, it could be slightly tighter without losing 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 (multiple schema types, diff, fix list) and lack of output schema or annotations, the description is moderately complete. It covers core functionality but omits specifics about output format and interpretation of the diff/fix list.

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 clear descriptions for all three parameters. The description adds context about business verticals and query fields but does not substantially enhance parameter understanding 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 generates a JSON-LD structured-data bundle for business sites, including gap diff and ranked fix list, with a specific verb and resource. It distinguishes from siblings by its unique focus on structured data generation for AI assistant citation eligibility.

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 no explicit guidance on when to use this tool versus siblings like deep_audit or enrich. It implies usage for generating structured data but does not exclude alternatives or specify context 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, the description carries the full burden. It discloses that it uses pure Base-mainnet RPC, public data only, no keys, no subscription, and is pay-per-call. It also lists all returned data categories, giving the agent a complete understanding of behavior and safety.

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 single dense paragraph but front-loads the purpose and lists features concisely. Every sentence adds value, and it avoids unnecessary repetition. Slight improvement could be structure, but it is appropriately sized.

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 only one parameter and no output schema, the description thoroughly explains what the tool returns (holdings, metadata, proxy detection, etc.). It provides sufficient context for agent decision-making, though it could clarify response format.

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 the single required parameter 'address'. The description does not add additional semantics beyond the schema description 'Base/EVM 0x address'. Baseline is 3, and no extra value is provided 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 that the tool enriches a Base/EVM address into an on-chain profile, specifying exactly what it returns (EOA vs contract, holdings, metadata, proxy detection, activity, label). The verb 'enrich' and resource 'wallet' are specific, and the description distinguishes this tool from general enrich tools by focusing on Base/EVM addresses.

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 explicitly says 'The frictionless, pay-per-call way for an agent to size up a wallet/contract before it sends funds, swaps, or calls it,' providing clear usage context. However, it does not explicitly compare to sibling tools or state 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.