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Agent Signal

Shared shopping intelligence for AI agents, with product, category, merchant, pricing, and session tools.

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Install Agent Signal

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "agent-signal": {
      "command": "npx",
      "args": [
        "-y",
        "agent-signal"
      ],
      "env": {
        "DATABASE_URL": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 30 tools correctly (5d ago).

Overview

The agent-signal MCP server gives shopping agents access to shared product, category, merchant, pricing, and session intelligence. It provides 23 MCP tools over a hosted Streamable HTTP endpoint or a local stdio process, including combined workflows for starting sessions and evaluating products. Use it when an agent needs evidence from prior shopping sessions, market signals, or recorded outcomes rather than relying only on a fresh search. Self-hosting requires PostgreSQL and includes a REST API alongside the MCP server.

Use cases

  • Start shopping sessions with category intelligence
  • Compare products using prior agent decisions
  • Analyze merchant reliability and competitive losses
  • Monitor product trends and price alerts

Key features

  • 23 MCP tools for shopping intelligence and logging
  • Product, category, merchant, and pricing analytics
  • Similar-session outcomes and constraint matching
  • Competitive landscape and rejection analysis
  • Price alerts and product trend monitoring
  • Hosted HTTP and local stdio transports

Tools30

What Agent Signal lets your agent do. Expand a tool for its parameters.

Verified live~5.7k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

Not checked yetLast checked Today

We have not completed a health check for this listing yet.

0/5 checks healthy over the last 40d

What it can do

From the 30 tools this server returned when we ran it.

Can delete or modify data · 1Makes network requests · 2Read-only tools · 29
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceNot published
Package age210 days
Maintainers1

Observed 2026-10-17 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 15d ago via OSV.dev · agent-signal (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads428Package downloads in the last 30 days.
Last commit6mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed30Callable tools this server registers over MCP.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How Agent Signal works

What agent-signal MCP server does

The agent-signal MCP server supplies shared signals from AI shopping activity. Buyer-facing tools report product selection rates, rejection reasons, competing products, category preferences, merchant reliability, price history, warnings, and outcomes from similar sessions. Seller-facing tools analyze competitive position, lost comparisons, category demand, rejection patterns, and merchant performance.

The service also lets agents contribute new observations. They can record shopping intent, constraints, budgets, exclusions, product evaluations, comparisons, final outcomes, and completed sessions imported after the fact. This creates a feedback loop in which later shopping sessions can use earlier decisions.

Reach for the agent-signal MCP server when a shopping workflow needs structured evidence about what other agents selected, rejected, compared, or purchased. It is aimed at product discovery, shopping assistants, merchant analysis, and agents that need to compare current prices or product performance with historical activity.

How it works

Two combined tools cover common workflows. smart_shopping_session creates a session and returns category intelligence, similar-session outcomes, relevant constraints, and network statistics. evaluate_and_compare records a product evaluation while returning product intelligence and a deal assessment. Separate read and write tools support more granular workflows.

The server exposes 23 MCP tools: buyer intelligence, seller intelligence, discovery and monitoring, and session logging. Price alerts can be created and checked against later agent activity. Other tools rank products within a budget, show changing product trends, identify products matching constraints, and retrieve a session summary.

Local deployments use MCP stdio transport. The hosted deployment uses Streamable HTTP at https://agent-signal-production.up.railway.app/mcp. A REST API runs alongside the MCP service for merchant analytics, session access, aggregation, and health checks.

Setup and configuration

For remote use, add the hosted MCP URL to a compatible client’s MCP configuration. No local installation is needed for that option. For a local process, run npx agent-signal; Claude Desktop and Claude Code can launch it with the npx command and the agent-signal argument.

Self-hosting requires cloning the repository, installing npm dependencies, configuring PostgreSQL through DATABASE_URL, running migrations, and starting the development process. Sample data can be loaded optionally with the seed command. The local API and MCP server listen on port 3100 according to the documented setup.

Tools and capabilities

  • Combine session creation with category and prior-session intelligence.
  • Inspect product selection, rejection, competition, pricing, and warnings.
  • Analyze category recommendations, demand, budgets, and unmet needs.
  • Review merchant stock reliability, prices, and selection outcomes.
  • Record evaluations, comparisons, outcomes, and completed sessions.
  • Monitor trends and create or check product price alerts.

Limitations and notes

The documented hosted data covers active shopping categories and a growing set of recorded sessions, products, merchants, and categories; coverage depends on the available network data. The tools provide recorded signals and historical comparisons, not a guarantee that a product is currently in stock or that an observed outcome applies to every shopper.

The hosted endpoint avoids local setup, while self-hosting adds PostgreSQL administration and migration steps.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Agent Signal

agent-signal is an MCP server that connects AI shopping agents to shared shopping intelligence from recorded sessions, products, merchants, and outcomes. Its main tools start smart shopping sessions, evaluate and compare products, inspect category and merchant signals, detect deals, monitor alerts, and log shopping decisions for later agents.

Run `npx agent-signal` for a local stdio server, or configure the hosted MCP URL at `https://agent-signal-production.up.railway.app/mcp` for remote access. Claude Desktop and Claude Code can launch the local server with `npx` and the `agent-signal` argument.

Yes. Clone the repository, run `npm install`, set `DATABASE_URL` to a PostgreSQL database, run the migrations, optionally seed sample data, and start the application with `npm run dev`. The local API and MCP server run on port 3100 according to the documented setup.

It provides product selection and rejection signals, category recommendations and demand, merchant reliability, competitive comparisons, price verdicts, warnings, similar-session outcomes, budget rankings, trends, and price alerts. Results depend on the sessions and products recorded in the shared data set.

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