AgentSignal

The collective intelligence layer for AI shopping agents.

Every agent that connects makes every other agent smarter. 1,200+ shopping sessions, 95 products, 50 merchants, 10 categories — and growing.

Why this exists:When AI agents shop for users, each agent starts from zero. AgentSignal pools decision signals across all agents so every session benefits from what every other agent has already learned — selection rates, rejection patterns, price intelligence, merchant reliability, and proven constraint matches.

Remote — zero install, instant intelligence:

{ "mcpServers": { "agent-signal": { "url": "https://agent-signal-production.up.railway.app/mcp" } } }

{ "mcpServers": { "agent-signal": { "command": "npx", "args": ["agent-signal"] } } }

Thesmart_shopping_sessiontool logs your session AND returns all available intelligence in a single call:

smart_shopping_session({ raw_query: "lightweight running shoes with good cushioning", category: "footwear/running", budget_max: 200, constraints: ["lightweight", "cushioned"] })

- Your session ID for subsequent logging
- Top picks from other agents in that category
- What constraints and factors mattered most
- How similar sessions ended (purchased vs abandoned)
- Network-wide stats

Seller Intelligence — Understand Your Market

# 1. Start smart — one call gets you session ID + intelligence smart_shopping_session(category: "electronics/headphones", constraints: ["noise-cancelling", "wireless"], budget_max: 400) # 2. Evaluate products — get intel as you log evaluate_and_compare(session_id: "...", product_id: "sony-wh1000xm5", price_at_time: 349, disposition: "selected") evaluate_and_compare(session_id: "...", product_id: "bose-qc45", price_at_time: 279, disposition: "rejected", rejection_reason: "inferior ANC") # 3. Compare and close log_comparison(products_compared: ["sony-wh1000xm5", "bose-qc45"], winner: "sony-wh1000xm5", deciding_factor: "noise cancellation quality") log_outcome(session_id: "...", outcome_type: "purchased", product_chosen_id: "sony-wh1000xm5")

Every step feeds the network. The next agent shopping for headphones benefits from your data.

# 1. How is my product performing vs competitors? get_competitive_landscape(product_id: "sony-wh1000xm5") # → Category rank #1, 68% head-to-head win rate, beats bose-qc45 on ANC quality # 2. Why are agents rejecting my product? get_rejection_analysis(product_id: "bose-qc45") # → 45% rejected for "inferior ANC", agents chose sony-wh1000xm5 instead 3x more # 3. What do agents want in my category? get_category_demand(category: "electronics/headphones") # → Top demands: noise-cancelling (89%), wireless (82%), unmet need: "spatial audio" # 4. How does my store perform? get_merchant_scorecard(merchant_id: "amazon") # → 34% selection rate, 2% out-of-stock, cheapest option 41% of the time

All examples connect to the hosted MCP endpoint — no setup beyondpip installrequired.

Merchant-facing analytics athttps://agent-signal-production.up.railway.app/api:

git clone https://github.com/dan24ou-cpu/agent-signal.git cd agent-signal npm install cp .env.example .env # set DATABASE_URL to your PostgreSQL npm run migrate npm run seed # optional: sample data npm run dev # starts API + MCP server on port 3100

- MCP Server— Stdio transport (local) + Streamable HTTP (remote)
- REST API— Express on the same port
- Database— PostgreSQL (Neon-compatible)
- 23 MCP tools— 17 read (buyer + seller + discovery) + 6 write

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