What can you do with this server?

ShopGraph is a product data extraction API that turns product URLs or raw HTML into structured, confidence-scored JSON. Here's what you can do with it: * Full product extraction (enrich_product): Submit a URL to extract name, price, brand, images, availability, categories, colors, materials, and dimensions using a multi-tier pipeline (Schema.org → LLM inference → headless browser). Costs $0.02/call. * Basic product extraction (enrich_basic): Submit a URL for faster, lighter extraction of core fields (name, price, brand, availability) using Schema.org only — no LLM. Costs $0.01/call beyond the free tier. * Raw HTML extraction (enrich_html): Submit pre-fetched HTML directly for extraction. * Per-field confidence scoring & provenance: Every field includes a confidence score (0–1) and provenance metadata indicating which method produced it. Use strict_confidence_threshold to automatically filter out low-confidence fields. * UCP format output: Optionally receive results in Universal Commerce Protocol format for standardized commerce data. * Flexible billing: Free tier includes 50 calls/month. Additional usage is available via subscription or pay-per-call through Stripe (payment_method_id parameter). Cached results (within 24 hours) are always free. * AI agent integration: Compatible with Claude, Windsurf, CrewAI, AutoGen, and other frameworks via MCP configuration. Self-hosting is also supported.

Which integrations are available for this server?

Leverages Google's Gemini LLM to extract and enrich structured product data from URLs when schema.org data is unavailable. Integrates Stripe's Machine Payments Protocol (MPP) for payment-gated tool access, enabling automated billing and authentication for product data enrichment requests.

ShopGraph

by laundromatic

Overview Schema Related Servers Score Discussions

TypeScript

Remote

ShopGraph

The extraction API that shows its work. Send a URL or raw HTML, get structured JSON with per-field confidence scoring and extraction provenance — every field shows which method produced it (Schema.org, LLM inference, or headless browser) and how confident the system is. Set strict_confidence_threshold and uncertain fields are removed server-side before they reach your agent. 50 free calls/month.

Website: https://shopgraph.dev | API: https://shopgraph.dev/api/enrich/basic | MCP: https://shopgraph.dev/mcp

UCP output validated with ucp-schema v1.1.0 — the official Universal Commerce Protocol schema validator.

Quick Start

# Free — no API key, no signup
curl -X POST https://shopgraph.dev/api/enrich/basic \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.allbirds.com/products/mens-tree-runners"}'

Returns structured JSON with per-field confidence scores:

{
  "product": {
    "product_name": "Men's Tree Runners",
    "brand": "Allbirds",
    "price": { "amount": 100, "currency": "USD" },
    "availability": "in_stock",
    "categories": ["Shoes", "Running"],
    "confidence": { "overall": 0.95 },
    "_shopgraph": {
      "field_confidence": {
        "product_name": 0.97,
        "brand": 0.95,
        "price": 0.98,
        "availability": 0.90
      }
    }
  },
  "free_tier": { "used": 1, "limit": 50 }
}

Related MCP server: AgentShare — Solana DeFi Intelligence MCP

Tools / Endpoints

Tool	REST Endpoint	Price	What It Does
`enrich_basic`	`POST /api/enrich/basic`	Free (shared quota)	Schema.org extraction only. Fast, zero LLM cost.
`enrich_product`	`POST /api/enrich`	Free 50/mo, then subscription or $0.02/call	Full pipeline with per-field confidence scoring and extraction provenance.
`enrich_html`	`POST /api/enrich/html`	Subscription or $0.02/call	Bring your own HTML. Works with Bright Data, Firecrawl, or any fetch/proxy tool.

Pricing: Free (50/mo) | Starter $99/mo (10K calls) | Growth $299/mo (50K calls) | Enterprise (custom). Pay-per-call via Stripe MPP still available for agents. Cached results (24h) are free. No charge for failed extractions.

How It Works

Your agent sends a URL (or raw HTML)
  → Tier 1: Schema.org/JSON-LD parsing (0.93 baseline confidence, instant)
  → Tier 2: LLM extracts from page text when structured data is absent (0.70 baseline)
  → Tier 3: Headless Playwright renders JavaScript, then extracts (additional inference step)
  → Returns ProductData with per-field confidence scores and extraction provenance
    (which tier produced each field) in _shopgraph.field_confidence
  → Set strict_confidence_threshold to remove low-confidence fields server-side
    before they reach your agent
  → Add format=ucp for Universal Commerce Protocol output

Authentication: API key (sg_live_ keys) for subscription tiers, or Stripe MPP for pay-per-call agents.

ShopGraph is a structuring layer, not a fetcher. It's complementary to Bright Data, Firecrawl, and other fetch/proxy tools. They handle retrieval. ShopGraph handles extraction provenance and per-field confidence scoring.

REST API

`POST /api/enrich/basic` (Free tier)

curl -X POST https://shopgraph.dev/api/enrich/basic \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com/product"}'

Schema.org only. Shares the free-tier quota with /api/enrich. No signup needed.

`POST /api/enrich` (Full extraction)

# With API key (subscription)
curl -X POST https://shopgraph.dev/api/enrich \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sg_live_..." \
  -d '{"url": "https://example.com/product", "strict_confidence_threshold": 0.8, "format": "ucp"}'

# With Stripe MPP (pay-per-call)
curl -X POST https://shopgraph.dev/api/enrich \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com/product", "payment_method_id": "pm_..."}'

Full pipeline: Schema.org → LLM inference → headless browser. 50 free calls/month. Authenticate with API key (sg_live_) or Stripe MPP for higher limits.

`POST /api/enrich/html` (Bring your own HTML)

curl -X POST https://shopgraph.dev/api/enrich/html \
  -H "Content-Type: application/json" \
  -d '{"html": "<html>...</html>", "url": "https://example.com/product", "payment_method_id": "pm_..."}'

Already fetched the page? Pipe the HTML to ShopGraph for structuring.

MCP Configuration

{
  "mcpServers": {
    "shopgraph": {
      "type": "url",
      "url": "https://shopgraph.dev/mcp"
    }
  }
}

Works with Claude, Claude Code, Cursor, Windsurf, CrewAI, LangGraph, AutoGen, and any MCP client.

Extracted Data

Every response includes:

Field	Description
`product_name`	Product title
`brand`	Manufacturer or brand
`price`	Amount + currency + sale price
`availability`	`in_stock`, `out_of_stock`, `preorder`, `unknown`
`categories`	Product taxonomy
`image_urls`	Product images (enrich_product/enrich_html only)
`color`	Available colors
`material`	Materials/fabrics
`dimensions`	Size/weight info
`confidence`	Overall + per-field scores (0-1)
`_shopgraph.field_confidence`	Per-field confidence with field-type modifiers

Self-Hosted Setup

git clone https://github.com/laundromatic/shopgraph.git
cd shopgraph
npm install

Required .env:

Variable	Purpose
`STRIPE_TEST_SECRET_KEY`	Stripe secret key (test or live)
`GOOGLE_API_KEY`	Gemini API key for Tier 2 (LLM) inference
`UPSTASH_REDIS_REST_URL`	Upstash Redis for stats/monitoring (optional)
`UPSTASH_REDIS_REST_TOKEN`	Upstash Redis token (optional)

npm run build          # Compile TypeScript
npm start              # Run MCP server (stdio)
npm run start:http     # Run HTTP server
npm run dev            # Dev mode (no build needed)
npm run test:run       # Run 118 tests

Monitoring

ShopGraph runs 118 automated tests across 22 product verticals. Self-healing pipeline with circuit breaker, URL verification, and health alerts.

Health: https://shopgraph.dev/health
Stats: https://shopgraph.dev/api/stats
Dashboard: Live on shopgraph.dev homepage

License

Apache 2.0

Built By

Krishna Brown | Los Angeles, CA

Available Tools

2 tools

enrich_basicAInspect

Extract basic product attributes from a URL (name, price, brand, availability). Faster and cheaper than enrich_product. Costs $0.01 per call (cached results are free).

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	Product page URL to extract data from
`payment_method_id`	No	Stripe payment method ID for MPP payment

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, description carries full disclosure burden and successfully reveals cost model ($0.01 per call), caching behavior ('cached results are free'), and performance characteristics ('Faster'). Missing minor details like rate limits or error modes, but covers critical behavioral economics.

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

Conciseness5/5

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

Three tightly constructed sentences: capability definition, sibling differentiation, and economic model. Every sentence delivers unique value with zero redundancy. Logical flow moves from what it does to how it compares to alternatives to operational costs.

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

Completeness4/5

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

For a 2-parameter tool with no output schema, description adequately compensates by listing specific extracted attributes (name, price, brand, availability) and disclosing financial/caching behavior. Sufficiently complete for agent selection, though could benefit from brief error condition mention.

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?

Schema coverage is 100% (both url and payment_method_id fully described in schema), establishing baseline 3. Description mentions URL in context of extraction but adds no validation rules, format constraints, or explicit linkage between payment_method_id and the disclosed $0.01 cost beyond implicit inference.

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?

Description uses specific verb 'Extract' with clear resource scope 'basic product attributes from a URL' and explicitly lists returned fields (name, price, brand, availability). It distinguishes from sibling tool enrich_product by stating it is 'Faster and cheaper,' clearly positioning this as the lightweight alternative.

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

Usage Guidelines4/5

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

Provides explicit comparison to sibling ('Faster and cheaper than enrich_product') which implies selection criteria based on cost and speed requirements. Includes economic context ($0.01 per call, cached free) that helps agents decide when to invoke. Lacks explicit 'when NOT to use' guidance (e.g., when comprehensive data is required).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

enrich_productAInspect

Extract comprehensive product data from a URL including name, price, brand, images, availability, and more. Uses schema.org structured data when available, with LLM fallback. Costs $0.02 per call (cached results are free).

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	Product page URL to extract data from
`payment_method_id`	No	Stripe payment method ID for MPP payment

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full behavioral burden. Excellently discloses cost model (price per call, caching behavior) and extraction methodology (structured data priority with AI fallback). Missing: rate limits, timeout behavior, or failure modes when extraction fails.

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

Conciseness5/5

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

Three sentences with zero waste: (1) Function and data scope, (2) Technical implementation, (3) Cost model. Front-loaded with the core value proposition. Every word serves selection or invocation logic.

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

Completeness4/5

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

Compensates well for missing output schema by enumerating expected return fields (name, price, brand, etc.). Input schema is simple (2 params) and fully documented. Could be improved with error behavior description or return format specification.

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

Parameters4/5

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

Schema coverage is 100% (both parameters documented), establishing baseline 3. Description adds significant value by contextualizing the URL as a product page requiring comprehensive extraction, and the cost disclosure ($0.02) implicitly explains the purpose of payment_method_id without duplicating schema text.

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?

Specific verb ('Extract') plus explicit resource ('product data from a URL') with concrete field enumeration (name, price, brand, images, availability). The term 'comprehensive' effectively distinguishes this from sibling 'enrich_basic', clearly positioning this as the richer alternative.

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

Usage Guidelines4/5

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

Provides crucial usage context through pricing disclosure ('$0.02 per call, cached results are free'), which implicitly guides cost-sensitive decisions. Describes implementation strategy (schema.org with LLM fallback), helping agents understand reliability characteristics. Lacks explicit sibling comparison (e.g., 'use enrich_basic for simpler needs').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

2 tool updatesv1.0.0Mar 24, 2026
- First observedenrich_basic
- First observedenrich_product

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.3/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: enrich_basic extracts minimal attributes quickly and cheaply, while enrich_product provides comprehensive data with advanced methods at higher cost. Their descriptions explicitly differentiate scope, method, and pricing, leaving no ambiguity for an agent to misselect.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'enrich_' prefix, maintaining perfect naming alignment. This predictability helps agents understand they belong to the same functional family for product data extraction.

Tool Count4/5

Two tools are reasonable for a product data extraction server, offering tiered options. However, it feels slightly thin; additional tools like batch processing or schema validation could enhance completeness without overcomplication.

Completeness3/5

The server covers basic and comprehensive extraction well, but lacks tools for related operations like URL validation, batch processing, or data export. This creates minor gaps where agents might need workarounds for full workflows.

Maintenance

ActivityActive

ResponsivenessSyncing

Resources

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ShopGraph

Quick Start

Tools / Endpoints

How It Works

REST API

POST /api/enrich/basic (Free tier)

POST /api/enrich (Full extraction)

POST /api/enrich/html (Bring your own HTML)

MCP Configuration

Extracted Data

Self-Hosted Setup

Monitoring

License

Built By

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Ashra Structured Data

AgentShare — Solana DeFi

Haunt API

Godaloofficial

Appeared in Searches

Latest Blog Posts

MCP directory API

`POST /api/enrich/basic` (Free tier)

`POST /api/enrich` (Full extraction)

`POST /api/enrich/html` (Bring your own HTML)