Semantic API Search for Agents

Semantic API search for agents connects the task a buyer states to the endpoint that can perform it. The agent may ask to verify a business, resolve a domain, or check a company record without using the product name or category your team uses internally.

That vocabulary gap is a provider problem. If your listing repeats a brand name and a broad label such as “data enrichment,” semantic retrieval has too little operational meaning to work with. If it lists every adjacent phrase, the endpoint may appear for jobs it cannot complete. The useful middle is a compact capability record that says what happens, which inputs make it possible, and where the result stops.

Search begins with the requested job

An agent normally inherits task language from a user instruction, workflow step, or another tool result. That language describes an outcome. It rarely mirrors a route name. A request for “confirm whether this EU tax number is active” may need an endpoint marketed as business identity verification, while a request for “find the legal company behind this website” may belong to a domain-to-company capability.

Semantic retrieval helps because it can match related meanings rather than requiring one exact phrase. Current tool-discovery systems commonly search tool names, descriptions, parameter schemas, and category tags, then expose only a relevant subset to the agent. Lexical matching still matters for exact objects, identifiers, and domain terms. The provider therefore needs both natural task wording and precise field names.

This is different from designing an API capability taxonomy. A taxonomy decides the stable name and boundary of a job. Search metadata gives retrieval systems enough language to route varied requests to that job.

Write one retrieval document with operational fields

A useful listing should read like a small execution contract. It needs enough prose to match intent and enough structure to reject an impossible call before payment. At minimum, publish:

• an action and object, such as verify one VAT number or extract fields from one invoice;
• required inputs with their exact names, types, formats, and accepted identifiers;
• optional inputs that narrow geography, language, or output scope;
• the successful response boundary, including the main returned fields;
• explicit exclusions for adjacent work the endpoint does not perform;
• the charging unit and the payment terms the live gateway returns;
• a stable capability name, route, and provider identity.

The action and object carry semantic meaning. The input schema supplies hard evidence. A description that says “find company information” may match many queries, but `domain` as a required input tells the buyer that a company name alone is insufficient. An exclusion such as “does not return personal contact records” prevents a nearby but wrong intent from entering the candidate set.

Keep these fields consistent across the discovery document, catalogue record, OpenAPI description, and MCP tool definition. The provider discovery checklist covers the machine-readable surfaces that may cache them. Contradictory descriptions do not create more reach; they create different answers depending on which surface the crawler read.

Use query variants as tests, not copy

You do not need a paragraph of comma-separated synonyms. Write a canonical description of the job, then maintain a small test set of requests that express the same intent from different roles and workflow stages.

For a domain-to-company endpoint, test requests might ask to identify the business behind a website, resolve a domain to a legal entity, or enrich a company record from its homepage. Each phrasing should reach the same capability because the input and expected output are unchanged. A request to find employee email addresses should not reach it if that output is outside the contract.

Run the variants through discovery and inspect the complete result, not just whether your listing appears first. Check whether:

• the intended capability is returned for each supported phrasing;
• required input clues keep incompatible requests out;
• nearby capabilities remain distinguishable;
• the result identifies one bounded unit of delivery;
• a wording change affects discovery without changing the API contract.

This testing is more useful than counting keywords. It reveals missing task language and false positives with the same fixture set. It also complements capability-based API discovery, which tests whether the providers retrieved under one job are genuinely substitutable.

Do not make semantic similarity carry eligibility

A strong semantic match means the listing may address the task. It does not prove that the endpoint accepts the available inputs, meets a price or latency ceiling, uses an acceptable settlement path, or has measured reliability.

Those requirements need structured checks. In Apiosk, shared parameters such as `max_price`, `max_latency_ms`, `min_reliability`, `settlement`, and `require_all_inputs` can be passed through discovery, comparison, and decision. The search phrase finds a plausible capability; the requirements remove candidates that cannot satisfy the actual request.

The same boundary applies to broader web discovery. `GET /v1/discover?q=…` also sweeps external x402 sources, but those hits appear in a separate `external_candidates` block. They have no stable `candidate_id`, are unreviewed and unmeasured, and cannot chain into `/v1/compare` or `/v1/decide`. Semantic similarity does not turn an external result into a reviewed or settleable offer.

Measure retrieval separately from provider ranking

Search quality and provider ranking answer different questions. Retrieval asks whether the right capability entered the candidate set. Ranking asks which eligible provider should receive this call under the selected optimization rule.

Track retrieval tests by query, expected capability, returned capability, and false-positive capability. Do not use the comparison score as a search metric. Apiosk's 0–100 score is relative only to the candidates in one comparison. Every response includes the weights and each candidate's contribution per dimension. Dimensions not measured for a candidate are dropped from weighting and named, not scored zero.

Price, settlement rail, input compatibility, and sufficiently observed latency and success rate can contribute to comparison. Result quality and provider terms are not measured anywhere. A provider should not insert those claims into semantic metadata as if search relevance made them verified.

Separating the stages makes fixes smaller. A missed query calls for better capability language or aliases. An incompatible candidate calls for a schema or classification correction. A low rank calls for examining the measurements and selected optimization rule, not adding more search phrases.

Where Apiosk fits

Apiosk turns semantic discovery into a bounded buying path. `GET /v1/discover?q=…` accepts task language and returns reviewed candidates with stable IDs. `GET /v1/capabilities` exposes the catalogue's jobs, while `/v1/capabilities/{slug}` returns providers for one job, cheapest first. Over the Apiosk MCP server, the same chain is available as `apiosk_discover`, `apiosk_compare`, and `apiosk_decide`.

Once candidates are known, comparison returns their measurable differences and decision returns one provider, the rule that selected it, every rejected candidate with its failed constraint, and ordered runners-up. Search supplies the set; structured requirements and evidence govern what happens next.

For a provider, the immediate task is to publish one honest retrieval document: state the job in buyer language, expose exact inputs and outputs, name adjacent work you do not perform, and test natural query variants. When that record is ready, you can list your API on Apiosk so a semantic match can lead to a reviewed candidate rather than ending at an unverified search result.