AgentSignal

Collective intelligence for AI shopping agents. Every agent that connects makes every other agent smarter.

Quick Start (30 seconds)

Remote — zero install:

{
  "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"]
    }
  }
}

What It Does

When AI agents shop on behalf of users, each agent starts from scratch. AgentSignal pools decision signals across all agents so every session benefits from collective intelligence.

Read Tools — Make Better Decisions

Tool	What it tells you
`get_product_intelligence`	Selection rate, rejection reasons, which competitors beat it and why
`get_category_recommendations`	Top picks in a category, what decision factors matter, common requirements
`check_merchant_reliability`	Stock reliability, selection rate, purchase outcomes by merchant
`get_similar_session_outcomes`	What agents with similar constraints ended up choosing
`detect_deal`	Is this price good? Compares against historical prices from all agents
`get_warnings`	Stock issues, high rejection rates, abandonment signals
`get_constraint_match`	Exact match on your constraints — skip the search if a proven answer exists

Write Tools — Contribute Back

Tool	What it captures
`log_shopping_session`	Shopping intent, constraints, budget, exclusions
`log_product_evaluation`	Product considered, match score, selected/rejected/shortlisted + why
`log_comparison`	Products compared, dimensions, winner, deciding factor
`log_outcome`	Final result — purchased, recommended, abandoned, or deferred
`get_session_summary`	Retrieve full session details

How Agents Use It

At the start of a shopping task:

1. get_category_recommendations("footwear/running")
2. get_constraint_match("footwear/running", ["cushioned", "wide fit"], 150)
3. log_shopping_session(...)

While evaluating products:

4. get_product_intelligence("hoka-clifton-9")
5. detect_deal("hoka-clifton-9", 129.99)
6. get_warnings(product_id: "hoka-clifton-9", merchant_id: "amazon")
7. log_product_evaluation(...)

Before recommending:

8. check_merchant_reliability("amazon")
9. log_comparison(...)
10. log_outcome(...)

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, competitors
`GET /api/categories/:category/trends`	Category trends — top factors, budgets, trending 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)
12 MCP tools — 7 read + 5 write

License

MIT

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How are these scores calculated?

Resources

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