Wash-filtered intelligence layer for x402 — read-only access to category stats, service details, wash analysis aggregate, service search, and 24h trends.
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Average 4.5/5 across 5 of 5 tools scored.
Each tool has a distinct purpose: aggregate wash report, categories listing, single service detail, trends snapshot, and search. No two tools overlap in function, making selection unambiguous.
All tools follow the x402_ + verb_noun pattern (check_wash, get_categories, get_service, get_trends, search_services). The naming is perfectly consistent and predictable.
With 5 tools, the server is well-scoped for its purpose as an x402 watch/analytics service. Each tool provides a distinct view of the ecosystem without being overwhelming or sparse.
The tool set covers the core needs of the domain: aggregate wash data, category breakdowns, per-service details, trend monitoring, and service search. This provides a complete free-tier analytics surface, with paid endpoints mentioned for additional deep-dive features but not required for basic usage.
x402_check_washAInspectGet the aggregate wash-report dataset: 30-day total active buyers, real-volume %, suspected_wash and self_test counts, full 8-label distribution, 14-day wash percentage time series, and five anonymized case studies (Service A through E) with pattern signals.
For per-address real-time wash analysis with full signal breakdown, use the paid POST /api/v1/wash/check HTTP endpoint ($0.05 USDC) — that endpoint speaks x402, agents pay and receive data in a single HTTP round-trip. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
| address | No | Optional wallet or seller address. When provided, the response includes a hint about the paid per-address endpoint. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses key behaviors: no payment required, use of the same v2.0 algorithm as paid endpoints, and the conditional hint behavior for the address parameter. It falls short of a 5 by not mentioning potential rate limits or error conditions, but it carries the transparency burden well.
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: one sentence lists the dataset components, followed by a short alternative-endpoint note and a free-tier confirmation. 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?
The description covers all necessary context: what data is returned, cost, algorithm equivalence, and the sibling paid endpoint. Since an output schema exists, return-value details are not needed here. Complete for an agent to decide and invoke 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 covers 100% of parameter meaning, stating the optional address and its effect. The description adds no further parameter syntax or details, only indirectly clarifies scope via the paid-endpoint comparison. 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 opens with 'Get the aggregate wash-report dataset' and enumerates the specific contents (30-day active buyers, real-volume %, suspected_wash counts, etc.), clearly distinguishing it from sibling tools like x402_get_trends or x402_search_services by focusing on wash analysis.
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 explicit guidance: for per-address real-time analysis it directs users to the paid POST /api/v1/wash/check endpoint, and confirms 'Free tier. No payment required.' This clarifies when to use this free aggregate tool versus the paid alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
x402_get_categoriesAInspectList all 33 x402 service categories with aggregate stats: services count, 24h volume, transaction count, real-volume %, and label distribution. Use this to understand the shape of the x402 ecosystem before drilling into specific services or wallets. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It discloses that the operation is free ('Free tier. No payment required') and that results are 'wash-filtered data using the same v2.0 algorithm as the paid endpoints,' adding useful behavioral context beyond a simple list call.
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 compact, well-ordered sentences: first states the purpose and output fields, second gives usage guidance, third covers cost and filtering. Every sentence earns its place with no redundancy or 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 zero-parameter, read-only category listing, the description covers the output fields, intended usage before more specific queries, cost expectations, and a data-quality note about wash filtering. The presence of an output schema also handles return structure, making the description complete for this 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?
The tool accepts zero parameters, so the empty input schema is fully covered and there is minimal semantic burden. The description adds value by enumerating the returned aggregate stats fields, which is sufficient for a 0-parameter tool (baseline 4).
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 the specific verb 'List all' and clearly identifies the resource ('33 x402 service categories') with the output scope ('aggregate stats' including services count, volume, transactions, etc.). This clearly distinguishes it from sibling tools focused on wash checks, single services, trends, or 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?
The description states an explicit use case: 'Use this to understand the shape of the x402 ecosystem before drilling into specific services or wallets.' This implies when not to use it (for specific drill-downs) and notes the free tier, but it does not explicitly name alternative sibling tools as recommended substitutes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
x402_get_serviceAInspectGet the full detail record for one x402 service: name, description, seller address, chain, price, 24h and total transaction stats, 30-day daily volume time series, buyer-label distribution, and top buyers. Use this to evaluate a single service's traffic composition. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes | Numeric x402 service id (visible in /services list and detail URLs). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full disclosure burden. It does well by noting 'Free tier. No payment required' and 'Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints,' adding important context about cost and data provenance. It does not cover error handling or rate limits, but given the simple read-only nature, this is adequate and above average.
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, each earning its place: the first states the core purpose and contents, the second gives a usage guideline, and the third discloses cost and data-quality aspects. It is front-loaded with the most important information and contains 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?
With an output schema present to cover return values, the description provides sufficient context for a single-parameter tool. It explains what data is returned, how it is filtered, the cost, and the intended use case. This is a complete, self-contained description for 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?
The input schema already provides a clear description for service_id ('Numeric x402 service id (visible in /services list and detail URLs)'), covering 100% of the parameter. The description does not add further parameter semantics beyond restating that it fetches a single service, so the baseline of 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 action with a specific verb and resource: 'Get the full detail record for one x402 service' and enumerates the exact fields returned. It distinguishes from siblings by emphasizing 'one service' versus the search/trends/categories tools, and the usage note 'evaluate a single service's traffic composition' reinforces its unique scope.
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: 'Use this to evaluate a single service's traffic composition.' It also mentions cost/free tier and data filtering, which helps selection. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
x402_get_trendsAInspectGet the last-24-hour trends snapshot: new services count vs the previous 24h, total transaction count, total USDC volume, active buyer count, daily new-services bar (14 days), recent new services (top 10), category volume movers, and hot services with traffic surges (>= 100 24h tx and >= +50% growth). Refreshed every 5 min. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 goes beyond a bare 'get trends' by specifying refresh cadence, free access, and the wash-filtering algorithm. It does not disclose every behavioral nuance (e.g., authentication, error handling), but for a read-only trends endpoint the provided details are substantial and useful.
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?
Every sentence in the description serves a purpose. The bulk of the text is an efficient, structured list of the trend metrics, followed by essential operational notes (refresh, free access, wash-filtering). It is front-loaded with the core value proposition and does not waste 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?
The tool has no parameters, a rich output schema, and a description that enumerates exactly what data points are returned. The description also adds relevant context about freshness and pricing, making it complete for an agent to decide when and how to invoke this 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?
The tool takes zero parameters, so the schema coverage is trivially 100%. The description adds no parameter-specific semantics because there are none to document, and the baseline for zero-parameter tools is set at 4.
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 this tool retrieves a 24-hour trends snapshot with a detailed list of included metrics, such as new services count, transaction count, volume, active buyers, and hot services. This specific verb-resource pairing and content list distinguish it from sibling tools like check_wash, get_categories, get_service, and search_services.
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 strong contextual guidance by noting that it is free-tier, requires no payment, refreshes every 5 minutes, and returns wash-filtered data using the same v2.0 algorithm as paid endpoints. It does not explicitly state when to prefer this over alternatives, but for a parameterless trends snapshot the usage context is clear and helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
x402_search_servicesAInspectSearch the index of 36k+ x402 services with filters. Returns a paginated list of matching services with their stats and label mix. Use this to find services by topic, chain, or seller wallet. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-indexed page number. | |
| sort | No | Sort key: tx_24h | volume_24h | tx_total | price | real_pct | wash_pct | first_seen | alpha. | tx_24h |
| chain | No | Filter to one chain: 'base', 'solana', 'arbitrum', 'base-sepolia'. | |
| search | No | Free-text match against name, description, or seller address. | |
| category | No | Filter to a single category slug (e.g. 'ai_inference', 'wallet_analytics'). | |
| page_size | No | Page size (max 200; default 24). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the transparency burden. It discloses the free tier, no payment required, and that the returned data is wash-filtered using the same v2.0 algorithm as paid endpoints. It also notes pagination and stats/label mix, adding valuable behavioral context beyond the schema.
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 with all information front-loaded. Every sentence contributes: purpose, return content, use case, and free/wash-filtered behavior. No redundancy or 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 6 parameters and an output schema, but the description covers the core function, use context, cost (free), data quality (wash-filtered), and pagination. The output schema handles return structure details, so the description is sufficiently complete for an agent to decide when and how to call 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?
Schema coverage is 100%, so the baseline is 3. The description adds some alignment by mentioning filters for topic, chain, and seller wallet, which maps to the search, chain, and category parameters, but it doesn't provide new semantic detail beyond what the schema already explains.
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 'Search' and clearly identifies the resource: 'the index of 36k+ x402 services'. It also mentions filters and the types of matching ('by topic, chain, or seller wallet'), which distinguishes it from sibling tools like get_service or get_trends.
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 states when to use: 'Use this to find services by topic, chain, or seller wallet.' It doesn't explicitly name alternatives or exclusions, but the context makes clear this is for discovery/search rather than singular lookups or trends, providing sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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