Your agent needs product data — not HTML to parse. Covala gives agents structured tools through MCP and REST, with token-optimized responses designed for context windows.
Add product search, lookup, and knowledge tools to any MCP-compatible agent. One config line — Claude, GPT, or custom agents.
Request format=ai for ~300-token summaries instead of ~2,000-token full JSON. Fit more products in your context window.
9 knowledge types per product category — maintenance schedules, error codes, buying guides. Structured data, not web scraping results.
Point your MCP-compatible agent at the Covala server. Your agent gets access to product search, lookup, knowledge queries, and comparison — no custom integration code.
{
"mcpServers": {
"covala": {
"command": "npx",
"args": ["@covala/mcp-server"],
"env": {
"COVALA_API_KEY": "sk_cvl_live_..."
}
}
}
}Add format=ai to any product request. Your agent gets a condensed summary with key specs, pricing, and knowledge — optimized for context windows.
GET /v2/products/{id}?format=ai
Authorization: Bearer sk_cvl_...{
"summary": "Samsung DW80B7070US
Dishwasher — 42 dBA,
AutoRelease Door, WiFi",
"price_range": "$649–$899",
"retailers": [
"Best Buy", "Home Depot",
"Lowe's"
],
"key_specs": {
"noise_level": "42 dBA",
"capacity": "16 place settings"
},
"knowledge": {
"avg_lifespan": "10 years",
"annual_energy_cost": "$35"
}
}Building product data infrastructure for an AI agent means scraping, parsing, deduplicating, and maintaining it yourself. With Covala, that's one API key.
Fragile selectors that break on every redesign. Anti-bot measures that block your scraper. Stale data from cached pages.
Extracting noise levels, dimensions, and capacities from paragraphs of marketing copy. Getting it wrong means your agent gives wrong answers.
Relationships between products, brands, categories, variants, and accessories. Months of engineering before your agent can answer a single question.
Different rate limits, authentication methods, and response formats for every retailer. One breaks, your whole pipeline stops.
One retailer says "42 dBA," another says "42 decibels," a third puts it in a PDF. Your agent needs one consistent schema.
Structured JSON gets your agent started. These features make it genuinely useful.
The same product matched across 20 retailers automatically. Your agent queries one product identity instead of deduplicating results from multiple sources.
Color, size, and configuration variants collapsed into one entity. Your agent knows the stainless steel and black versions are the same dishwasher.
1,400+ brand profiles with quality tiers, warranty info, and cross-category coverage. Your agent can recommend brands, not just products.
9 structured data types per product category — maintenance schedules, error codes, buying guides, lifespan data, cost of ownership, and more.
The Covala MCP server exposes product search, lookup, comparison, and knowledge tools through the Model Context Protocol. Any MCP-compatible agent — Claude, GPT, or custom agents — can call these tools natively. Add it with a single config block pointing at https://mcp.covala.com/sse with your API key. No custom integration code, no SDK to install.
Add format=ai to any product request. Instead of the full ~2,000-token JSON response with every specification and offer, you get a condensed ~300-token summary with the product name, price range, key specs, retailer availability, and knowledge highlights. This lets your agent fit 6-7x more products into a single context window.
Yes. The knowledge endpoint returns 9 structured data types per product category: maintenance schedules, error codes, buying guides, lifespan data, cost of ownership, installation requirements, common failure modes, energy efficiency data, and warranty information. All structured as typed JSON, not paragraphs to parse.
Entity resolution matches the same product across different retailers into a single identity. A Samsung dishwasher listed under three different names at Best Buy, Home Depot, and Lowe’s resolves to one product with offers from each store. Your agent queries one product ID instead of deduplicating results from multiple sources.
Two options. For MCP-compatible agents, add the Covala server to your MCP config — one JSON block with the server URL and your API key. For everything else, use the REST API directly: GET /v2/products for search, GET /v2/products/{id} for lookup, and GET /v2/product-types/{slug}/knowledge for structured knowledge data. Both return clean, typed JSON.
Cross-retailer pricing, already matched. Build comparison features without matching infrastructure.
Maintenance schedules, lifespan data, and replacement planning — structured as API data.
Error codes, failure modes, and fix steps — structured as API data, not buried in PDFs.
MCP server, REST API, and AI-optimized responses — everything your agent needs to answer real product questions with real data.
MCP + REST API + format=ai