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.

Quick Start (30 seconds)

Remote — zero install, instant intelligence:

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

Local via npx:

npx agent-signal

Claude Desktop / Claude Code:

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

One Call to Start Shopping Smarter

The smart_shopping_session tool 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"]
})

Returns:

• 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

19 MCP Tools

Smart Combo Tools (recommended)

| Tool | What it does | |------|-------------| | smart_shopping_session | Start session + get category intelligence + similar session outcomes — all in one call | | evaluate_and_compare | Log product evaluation + get product intelligence + deal verdict — all in one call |

Buyer Intelligence — Shop Smarter

| Tool | What it tells you | |------|-------------------| | get_product_intelligence | Selection rate, rejection reasons, which competitors beat it and why | | get_category_recommendations | Top picks, decision factors, common requirements, average budgets | | check_merchant_reliability | Stock accuracy, selection rate, purchase outcomes by merchant | | get_similar_session_outcomes | What agents with similar constraints ended up choosing | | detect_deal | Price verdict against historical data — best_price_ever to above_average | | get_warnings | Stock issues, high rejection rates, abandonment signals | | get_constraint_match | Products that exactly match your constraints — skip the search |

Seller Intelligence — Understand Your Market

| Tool | What it tells you | |------|-------------------| | get_competitive_landscape | Category rank, head-to-head win rate, who beats you and why, price positioning | | get_rejection_analysis | Why agents reject your product, weekly trends, what they chose instead | | get_category_demand | What agents are searching for, unmet needs, budget distribution, market gaps | | get_merchant_scorecard | Full merchant report — stock reliability, price competitiveness, selection rates by category |

Write Tools — Contribute Back

| Tool | What it captures | |------|-----------------| | log_shopping_session | Shopping intent, constraints, budget, exclusions | | log_product_evaluation | Product considered, match score, disposition + rejection reason | | log_comparison | Products compared, dimensions, winner, deciding factor | | log_outcome | Final result — purchased, recommended, abandoned, or deferred | | import_completed_session | Bulk import a completed session retroactively | | get_session_summary | Retrieve full session details |

Example: Full Agent Workflow

# 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.

Example: Seller Intelligence Workflow

# 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

Categories with Active Intelligence

| Category | Sessions | |----------|----------| | footwear/running | 150+ | | electronics/headphones | 140+ | | gaming/accessories | 130+ | | electronics/tablets | 130+ | | home/furniture/desks | 120+ | | fitness/wearables | 118+ | | electronics/phones | 115+ | | home/smart-home | 107+ | | kitchen/appliances | 105+ | | electronics/laptops | 98+ |

Agent Framework Examples

Ready-to-run examples in /examples:

| Framework | File | Description | |-----------|------|-------------| | LangChain | langchain-shopping-agent.py | ReAct agent with LangGraph + MCP adapter | | CrewAI | crewai-shopping-crew.py | Two-agent crew (researcher + shopper) | | AutoGen | autogen-shopping-agent.py | AutoGen agent with MCP tools | | OpenAI Agents | openai-agents-shopping.py | OpenAI Agents SDK with Streamable HTTP | | Claude | claude-system-prompt.md | Optimized system prompt for Claude Desktop/Code |

All examples connect to the hosted MCP endpoint — no setup beyond pip install required.

REST API

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

| Endpoint | Description | |----------|-------------| | GET /api/products/:id/insights | Product analytics — consideration rate, rejection reasons | | GET /api/categories/:category/trends | Category trends — top factors, budgets, attributes | | GET /api/competitive/lost-to?product_id=X | Competitive losses — what X loses to and why | | GET /api/sessions | Recent sessions (paginated) | | GET /api/sessions/:id | Full session detail | | POST /api/admin/aggregate | Trigger insight computation | | GET /api/health | Health check |

Self-Hosting

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

Architecture

• MCP Server — Stdio transport (local) + Streamable HTTP (remote)
• REST API — Express on the same port
• Database — PostgreSQL (Neon-compatible)
• 19 MCP tools — 13 read (buyer + seller intelligence) + 6 write

License

MIT