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Semantic search over the x402 catalog (paid)

x402_semantic_search

Search the x402 endpoint catalog using plain-language queries to discover endpoints ranked by semantic similarity and trust score.

Instructions

Free-text SEMANTIC SEARCH across the entire monitored x402 endpoint catalog. Given a plain-language query (e.g. "weather forecast", "image generation", "EVM gas price oracle"), returns the up to 25 endpoints whose advertised purpose is semantically closest. Ranking is deterministic and fully specified: cosine similarity bucketed to whole percentage points first (80.3% and 80.5% are the same bucket, so sub-percent noise never outranks a better endpoint), then trust score (0-100), then described-before-undescribed, then endpoint id as a stable final tiebreak. Matches below a 0.5 cosine-similarity floor are dropped entirely, so a query can return fewer than the requested limit (or none). Endpoints that advertise no description are still matched: they are embedded from their service name and URL path tokens instead of a written description (host name as a last resort). The EXACT effect of a missing description: no fixed point deduction, and no direct similarity malus either; the only deterministic penalty is the described-before-undescribed tiebreak (at equal similarity bucket AND equal trust score, a described endpoint ranks first). Beyond that the effect is purely indirect: the shorter fallback text typically yields lower cosine similarity than a prose description, so undescribed endpoints tend to land in lower similarity buckets, by a query-dependent (never fixed) amount. Use this for DISCOVERY: find candidate endpoints for a capability before checking any of them in depth. Each match carries 'id', 'resource' URL, trust 'score' (0-100), 'grade' (A-F), raw cosine 'similarity' (0-1; ranking buckets it to whole percents), 'description' when advertised, and a free 'endpointPage' URL. 'score'/'grade' are null for endpoints not yet scored. Deliberately NO verdict/recommendation or flag detail: the per-endpoint trust report (x402_trust_score) carries those. Pay-per-call over x402 (~$0.001); auto-pays if a wallet is configured, otherwise returns the price quote.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax matches to return (1-25, default 25)
queryYesFree-text search query, e.g. "weather forecast". Describe the capability you need in plain words; matching is by meaning, not substrings.
Behavior5/5

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 and does so thoroughly: deterministic ranking with exact tie-break order, the 0.5 similarity floor, the effect of missing descriptions, null score/grade behavior, payment/auth behavior, and the exact returned fields are all disclosed. There is no contradiction with any annotation because none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well structured and front-loaded: purpose first, then ranking behavior, then edge cases, then output fields, then cost. It is verbose, especially in the missing-description paragraph, but nearly every sentence earns its place given the absence of annotations and output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema and no annotations, the description is self-contained: it enumerates all returned fields, the null behavior for unscored endpoints, pricing, payment fallback, and the deterministic ranking semantics. An agent has everything needed to invoke the tool and interpret its result correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents both parameters fully at 100% coverage, so the description does not need to add much. It reinforces the semantic-matching nature of 'query' and the 'up to 25' limit, but it does not materially add new parameter-level constraints or syntax beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb ('SEMANTIC SEARCH'), a specific resource ('the entire monitored x402 endpoint catalog'), and a clear output ('up to 25 endpoints whose advertised purpose is semantically closest'). This makes the tool's role as a discovery/search tool obvious and distinguishes it from the trust-score, leaderboard, and watch siblings.

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

Usage Guidelines5/5

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

The description explicitly says 'Use this for DISCOVERY: find candidate endpoints for a capability before checking any of them in depth.' It also explicitly excludes verdicts, recommendations, and flag details, and directs the agent to x402_trust_score for those, which is clear when-to-use and alternative 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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