Buyary PlatformThe Evidence Layer
Instant, source-backed product intelligence that improves discovery, recommendations, trust, and conversion across commerce and AI experiences. Composite scoresEvidence-backed claimsSource-level consensus Agentic commerce needs an evidence layer.
get_product_intelligence("AirPods Pro 3") 01Why Buyary exists
People increasingly discover products through AI, agents, retailers, creators, and conversational experiences — instead of traditional search alone.
What hasn’t changed is that people still want confidence before they buy.
They look for experts. They compare opinions. They read reviews. They seek evidence. They want to understand tradeoffs — not just receive a recommendation.
Buyary brings that collective product knowledge together, evaluates it with our multi-step reasoning engine, and transforms it into structured product intelligence that powers retailers, brands, developers, applications, and AI agents through a single platform.
AI Commerce
Explain recommendations with evidence and confidence.
AI Agents
Structured product intelligence instead of fragmented web pages.
Retailers
Increase shopper confidence directly on PDPs.
Brands
Understand perception and improve merchandising.
Developers
Integrate product intelligence through one API and MCP.
Consumers
Better decisions based on independent evidence rather than popularity alone.
02One tool call
Everything below comes back from a single call — live, from the published catalog.
get_product_intelligence("AirPods Pro 3") Composite scores
Pricing
List $249 $199.99 · 10 live offers
＋ Verified strengths
－ Reported issues
Per-source consensus · 5 of 34
＋ written verdicts for all 34 sources, per-dimension summaries, and the full analysis. See the live evaluation ↗
03The scoring model
Every score is dynamically calculated from synthesized consensus — not static metadata.
AirPods Pro 3 — representative criterion per dimension
04Benchmarked
Query: "Should I buy the iPhone 17 Pro?"
faster than a frontier chat model — delivering structured data instead of unstructured prose.
Median of 5 runs · API-measured · 2026-06-12
05The Catalog Preview
Live from the published catalog · as of 2026-07-12
06Ship it
The same keys. The same source-backed answers. Byte-identical responses.
MCP server
For agents
Agent-native deployment. Drop directly into Claude Desktop or Claude Code, enter your API key, and your agent can immediately call get_product_intelligence.
{
"mcpServers": {
"buyary": {
"type": "http",
"url": "https://services.buyary.com/mcp/",
"headers": {
"Authorization": "Bearer YOUR_BUYARY_KEY"
}
}
}
}REST API
For apps & backends
The same service over plain HTTP. For scripts, custom backends, mobile app architectures, and non-MCP stacks.
curl -s "https://services.buyary.com/v1/product-intelligence?product_name=iPhone%2017%20Pro%20Max" \
-H "Authorization: Bearer YOUR_BUYARY_KEY"07Monetize
Every recommendation is an opportunity.
Applications built on Buyary’s API and MCP can participate in affiliate revenue sharing for eligible purchases. Focus on building exceptional user experiences while Buyary provides structured product intelligence, purchasing links, and monetization infrastructure.
08Knowledge Center
How Buyary turns product evidence into structured intelligence for agents, applications and commerce teams.
Buyary Platform is product-intelligence infrastructure for AI agents, applications and commerce systems. It turns product evidence from many sources into structured, source-backed answers: a composite Buyary Score, expert and user signals, verified claims, strengths and considerations, and per-source consensus.
Instead of asking a language model to rebuild product understanding from scattered webpages on every question, Buyary provides intelligence that has already been organized for buying decisions. Agents connect through MCP; applications and backends use the REST API. Both are interfaces to the same service and the same keys.
RelatedGetting started MCP REST API Data model
Buyary returns structured buying intelligence for a confidently matched catalog product: composite and component scores, source-backed strengths and considerations, verified claims, per-source consensus, pricing context and source attribution — as documented in the data model.
The response is designed to be used by software. An agent can reason over the structured fields; an application can present selected elements in its own interface. Integrations should rely on the documented schema, not on parsing the homepage demo.
Buyary removes the repeated work of finding, reconciling and explaining product evidence. A general-purpose model can search the web, but it still has to pick sources, separate claims from opinions, weigh conflicting findings and produce a stable answer — slowly and inconsistently.
Buyary performs that synthesis ahead of the user's question and exposes the result as structured intelligence, giving agents and applications a fast, consistent starting point for explaining where a product is strong, where it falls short, and how sources agree or disagree.
RelatedData model AI Agents Docs
Buyary gives an AI shopping agent a dedicated product-intelligence tool: the agent calls Buyary with a product name and receives a structured, source-backed response instead of synthesizing every review from scratch — faster, more consistent, easier to explain.
An agent answering “Should I buy this phone?” can use Buyary's scores, verified claims, consensus and considerations as evidence, then apply the user's priorities in its own reasoning. Buyary supplies the product understanding; the agent stays responsible for the conversation and the final answer.
Yes. Buyary exposes its product-intelligence service through an MCP server for compatible agents and clients. Add the server URL and a Buyary bearer key to the client configuration, and the Buyary tool can be discovered and called by the agent.
MCP is the most direct path when the experience is agent-native; conventional applications, services and scripts can use the REST API instead. The same key works across both protocols — they are two interfaces to the same underlying answers.
RelatedMCP Authentication & keys Quickstart
Web search retrieves pages; Buyary returns pre-structured product intelligence. Even a web-enabled model must choose sources, reconcile contradictions, extract claims and rebuild an answer on every query — slowly, and with results that are hard to reproduce.
Buyary provides a stable product representation built for buying questions: scores, claims, consensus, attribution. Web search remains useful for breaking news, availability or products outside the catalog; Buyary is the purpose-built evidence layer when a supported match exists.
RelatedAI Agents Data model Errors & limits
Retrieval-augmented generation retrieves documents to give a model context; Buyary provides a product-specific intelligence object that has already been synthesized from many sources. They solve different layers of the problem and can work together.
A RAG pipeline may retrieve raw reviews and leave the model to reconcile them at question time. Buyary supplies organized scores, claims, considerations and per-source consensus the agent can use directly — and an application can still retrieve original sources when the user wants more detail.
RelatedData model MCP REST API
Buyary helps ground agent output by returning structured, source-attributed evidence and an explicit unmatched-product state. When the service cannot confidently match a product, it says so rather than guessing — and the application should do the same.
No tool can guarantee every generated statement is correct. Ground product claims in the Buyary response, preserve qualifiers and uncertainty, cite sources where appropriate, and never convert missing fields into confident statements. Buyary strengthens the evidence layer; responsible output still depends on the surrounding prompt, model and application.
RelatedErrors & limits Data model MCP
Buyary refuses to pretend an uncertain catalog match is correct. When no product confidently matches the request, the tool returns found:false with “did you mean” candidates instead of manufacturing intelligence for the wrong item.
Treat this as a normal, useful state: show the suggestions, ask the user to clarify the model or variant, or fall back to another research workflow. Never hide the mismatch or present a suggestion as though it were the confirmed result.
Product intelligence is a structured understanding of a product derived from multiple forms of evidence. In Buyary it includes a composite score, component signals, verified claims, strengths, considerations, per-source consensus and source attribution.
The goal is not just “is this product good.” Product intelligence lets software explain what a product does well, where it falls short, how strong the evidence is, and why sources may disagree — reusable across agents, product pages, comparisons and assistants.
The Buyary Score is a 0–100 composite that summarizes the available body of product evidence — expert reviews, user voices, empirical information and category-relevant criteria — rather than a simple average of star ratings. The methodology is documented on buyary.com.
Treat the score as a compact entry point, not a substitute for the analysis. Pair it with the response's claims, strengths, considerations and per-source consensus so users can see what produced the overall evaluation.
Verified claims are structured product statements that Buyary has associated with supporting evidence in its analysis — concrete facts and findings an agent or application can present with more confidence than an unsupported generated assertion.
Show claims in context: manufacturer statements, measured findings and source observations are different kinds of evidence, and the response preserves qualifiers where they matter. Don't strip attribution or turn a bounded claim into a universal promise.
RelatedData model get_product_intelligence Buyary.com Methodology
Per-source consensus shows how individual sources align with the overall product assessment — whether named sources broadly agree, diverge, or reach a distinct evaluation — so an application can reveal the shape of the evidence, not just a single summary.
Consensus is not unanimity, and disagreement is not an error: reviewers test different conditions and value different qualities. Use consensus to help users inspect the evidence rather than implying the most common opinion is automatically correct.
Buyary's response separates verified strengths from reported considerations and can expose feature-level performance, so an application can explain tradeoffs instead of flattening every product into one number.
A device may pair excellent performance and display quality with a premium price and weak repairability — which one matters depends on the user. Buyary supplies the structured evidence; the agent or application connects it to the user's priorities.
Request access, sign in, and create a key from the Buyary dashboard — the secret is shown exactly once, so store it securely. Then pick your protocol: connect the MCP server for agent-native work, or call the REST API from a backend, script or service.
The quickstart walks through the first product-intelligence call. A successful response contains a matched product and structured intelligence; an ambiguous request may return found:false with suggested matches instead.
Buyary uses bearer-key authentication, and the same key works for both MCP and REST. Keys are created in the dashboard; the plaintext secret is shown exactly once, so capture it in a secret manager at creation time.
Store keys as secrets, send them only over secure connections, and never embed them in browser-delivered code or commit them to a repository. The authentication docs cover creation, rotation and 401 semantics.
Use MCP when an AI agent or MCP-compatible client should discover and call Buyary as a tool. Use REST when a backend, script, workflow or application needs direct HTTP access to the same product-intelligence service.
The decision is about integration architecture, not answer quality — both protocols use the same keys and return the same underlying answers. Many teams run both: MCP for an internal agent, REST for a customer-facing application.
RelatedMCP REST API Quickstart
get_product_intelligence is the core operation for retrieving structured intelligence about one product: the caller provides a product name, Buyary matches it against the catalog, and returns the documented intelligence response.
Pass enough identifying information to distinguish variants, inspect the found state, and use the returned product identity rather than assuming the query string matched exactly. The endpoint reference and data model define the production contract.
Yes — get_product_intelligence_batch retrieves product intelligence for multiple products in one call, which is the right tool for comparison, catalog enrichment and workflows that would otherwise issue many individual requests.
A batch response can contain a mixture of matched, unmatched and partially usable items, so handle each result independently rather than treating the batch as all-or-nothing. The batch reference documents limits and error behavior.
Implement the documented error and limit behavior before shipping: distinguish authentication failures, invalid requests, rate or usage limits, service errors, and a legitimate found:false catalog result — each needs a different user experience and retry policy.
Use bounded retries with backoff only for retryable failures, log request context without exposing secrets, and show a useful fallback when intelligence is unavailable. Never retry an unmatched product as though it were a transient server failure.
RelatedErrors & limits Authentication & keys Playground Schema Changelog
Commerce applications use Buyary to add source-backed product understanding to discovery, product pages, comparison, assistants and other decision moments — retrieving structured scores, claims, strengths, considerations and consensus, then presenting the pieces that fit the experience.
Treat Buyary as an intelligence layer, not a front-end template: a conversational assistant may use the response to answer a question, while a product page surfaces a score, an evidence summary and expandable source detail.
RelatedREST API Data model Contact
Yes. Retailers and marketplaces use Buyary for product-intelligence enrichment, shopping assistance and catalog-scale workflows — the batch operation and REST interface for catalog work, MCP for agent experiences, and Trust Widgets for on-page trust.
For larger builds or data-licensing discussions, use the contact path. Scale, service-level and pricing specifics are agreed per engagement rather than promised generically.
RelatedBatch reference Contact REST API
Buyary's Editorial Policy and Methodology establish that commercial relationships do not determine Buyary Scores and that sponsored placements are disclosed. Brands choose where Buyary appears; they do not determine scores, evidence or recommendations.
That separation is the product: the intelligence is only valuable if users and applications can trust that it reflects evidence rather than the preferences of a paying brand or retailer. The methodology and disclosure pages carry the authoritative explanation.
Yes — source attribution and per-source consensus let an application show which sources contributed to the analysis and how each individual evaluation relates to the overall result.
Preserve source names, links and qualifiers where the response provides them, and don't imply Buyary is affiliated with or endorsed by a source merely because that source was analyzed. The data model defines the citation fields.
Buyary supports more accurate product answers by grounding responses in structured, source-backed evidence and representing uncertain catalog matches explicitly with found:false and suggested candidates rather than a silent guess.
Accuracy is never absolute: sources disagree, products change, and any synthesis system can contain errors. The best integrations expose evidence, freshness and uncertainty, let users inspect sources, and avoid presenting every field with identical confidence.
Yes — organizations building larger integrations or needing a data-licensing arrangement can contact the team. Enterprise scope covers custom catalog deployments, commercial terms, integration planning and dedicated support appropriate to the use case.
Private deployment, SLAs, compliance and security specifics are agreed per engagement; the contact path is the route for requirements beyond the documented self-serve offering.
RelatedEnterprise Contact Pricing
Buyary is priced per product line: Trust Widgets by products deployed, the Intelligence API by monthly lookups, MCP access included in platform plans, Insights by research scope, and enterprise catalog deployments custom-quoted. Pilots are scoped and priced individually.
The pricing page carries the current model, and pilots are deliberately small — up to 25 products with real measurement — so the first step is an easy yes. Revenue Sharing is a separate commercial model where partners earn rather than pay.
RelatedPricing Contact Request access
Request access through the platform's request-access form; once approved, sign in and create an API key from the dashboard. The key's secret is displayed exactly once, then follow the MCP or REST quickstart for your first call.
For larger deployments, catalog-scale needs or data licensing, contact the team directly — the sales-assisted path exists alongside self-serve access.
RelatedRequest access Dashboard Quickstart Contact
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Built for agents & apps
Building something bigger, or need a data-licensing arrangement? Contact us