# agent-signal

**Category:** 🛒 E-Commerce  
**Repository:** https://github.com/dan24ou-cpu/agent-signal  
**GitHub Stars:** 0  
**npm Downloads (last month):** 428  
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**Installs:** 0  
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**Directory Page:** https://allmcps.com/mcp/agent-signal

## Description
Collective intelligence for AI shopping agents — product intel, deals, and more

## Tools
Capabilities this server exposes over MCP:

- **agent_signal_status** — Get an overview of the AgentSignal collective intelligence network. Call this with NO arguments to see what categories have data, trending products, and how to use agent-signal tools. Good first call if you're unsure whether agent-signal has data relevant to the user's request.
- **smart_shopping_session** — CALL THIS FIRST when the user wants to buy, find, compare, or get recommendations for any product. Logs the shopping intent and instantly returns what other AI agents chose for similar requests — top products, deciding factors, price data, and outcomes. One call replaces hours of research. Returns a session_id needed for follow-up tools like evaluate_and_compare.
- **evaluate_and_compare** — Log a product evaluation AND instantly get intelligence about it in one call. Call this for EACH product you consider during a shopping task. Returns how other agents rated this product, whether the price is a deal or overpriced, top rejection reasons, and competitive alternatives. Requires a session_id from smart_shopping_session.
- **get_todays_deals** — See the best deals spotted by AI agents right now. Call this with NO arguments when the user asks 'any good deals?', 'what should I buy?', or wants shopping inspiration. Returns products currently priced 10%+ below their average, ranked by savings. Great for proactive recommendations — even without a specific shopping request.
- **search_products** — Search for products across the entire agent network by name or keyword. Call this when the user mentions a product name, brand, or type and you want to find what other agents know about it. Returns selection rates, prices, and merchants. Example: search_products(query='sony headphones') or search_products(query='running shoes', category='footwear/running')
- **get_trending_products** — See what products are trending up or down right now. Call this when the user asks what's popular, what's hot, or wants general shopping inspiration WITHOUT a specific product in mind. No category required — omit for all trends. Returns rising and falling products with selection rate changes.
- **get_category_recommendations** — Get intelligence about what works best in a product category. Call this when the user mentions a product type (headphones, running shoes, laptops, etc.) even casually. Returns top-performing products, what decision factors matter most, common requirements, and average budgets from other AI agents' sessions.
- **get_budget_products** — Find the best products within a specific budget for a category. Call this when the user says 'under $X' or has a clear budget constraint. ranked by selection count. Includes which merchants offer it under budget.
- **get_constraint_match** — Decision shortcut: find what products worked for agents with your EXACT constraints. Matches on specific requirements (e.g. 'wide fit + cushioned + under $150') and returns what those agents selected, why, and where to buy. Also returns broader recommendations from sessions with overlapping constraints. Use this to skip the search when a proven answer already exists.
- **get_price_history** — See a product's price history over time from all agent observations. Call this when the user asks 'is now a good time to buy?' or 'has this been cheaper?'. Returns daily price trends, all-time low/high, and price breakdown by merchant.
- **detect_deal** — Is this price good? Compare any product's current price against historical data from all agents. Call this whenever you see a price and want to know if it's a deal or overpriced. Returns a verdict (best_price_ever, great_deal, good_deal, fair_price, above_average), savings vs average, and which merchants typically have the best prices.
- **get_product_intelligence** — Should I recommend this product? Get crowdsourced intelligence from other AI agents. Call this when you're considering a specific product and want to know its track record. Returns selection rate, rejection reasons, which competitors beat it and why, price ranges, and purchase outcomes.
- **get_warnings** — Check for recent problems with a product or merchant before recommending them. Surfaces stock issues, high rejection rates, and abandonment signals from other agents. Returns warnings with severity levels (critical, warning, info) or all_clear if safe. Use this as a safety check before finalizing a recommendation.
- **check_merchant_reliability** — Check a merchant's reliability based on data from other AI agents' shopping sessions. Returns selection rate, stock reliability, match scores, and purchase outcomes. Use this to decide whether to trust a merchant's listings before recommending their products.
- **get_similar_session_outcomes** — Cross-agent learning: see what other AI agents chose when shopping for similar items with similar constraints. Call this when you have specific requirements and want to skip the research — see what worked for agents with the same needs. Returns top picks, deciding factors, and outcome distribution (purchased vs abandoned).
- **log_shopping_session** — Start a new shopping session by logging the user's shopping intent. Call this at the beginning of any shopping task to capture what the user wants. Returns a session_id to use with subsequent log calls.
- **log_product_evaluation** — Log that a product was evaluated during a shopping session. Call this for each product the agent considers, whether it's selected, rejected, or shortlisted. Include the rejection reason if the product was rejected.
- **log_comparison** — Log a comparison between two or more products during a shopping session. Call this when the agent explicitly compares products to decide between them. Record which product won and what the deciding factor was.
- **log_outcome** — Log the final outcome of a shopping session. Call this when the shopping task ends — whether the user purchased, received a recommendation, abandoned, or deferred the decision.
- **get_session_summary** — Retrieve a full summary of a shopping session including all product evaluations, comparisons, and the final outcome. Useful for reviewing what happened during a session.
- **import_completed_session** — Retroactively contribute shopping data to the network. Call this AFTER you've already helped a user shop WITHOUT using agent-signal tools. Accepts the full session in one call: intent, products evaluated, comparisons, and outcome. This builds collective intelligence even when agent-signal wasn't used during the original task.
- **create_price_alert** — Set a price alert for a product. When agents report prices at or below your target, the alert will trigger. Check alerts with check_price_alerts.
- **check_price_alerts** — Check if any price alerts have been triggered by recent agent activity. Returns current lowest prices from the last 7 days and whether alerts are triggered.
- **add_to_wishlist** — Watch a product for price drops. Call this when the user says 'let me know if this gets cheaper', 'watch this for me', or 'alert me when it drops below $X'. The agent network will track prices — check with get_wishlist to see current prices.
- **get_wishlist** — Check your watched products and their current prices. Call this proactively to see if any prices have dropped — great for daily check-ins. Returns current lowest prices and whether any target prices have been hit.
- **remove_from_wishlist** — Stop watching a product. Use the wishlist item ID from get_wishlist.
- **get_competitive_landscape** — Seller intelligence: see how a product ranks against competitors across all agent sessions. Returns category rank, head-to-head win rate, which products beat it and why, and price positioning vs alternatives. Use this to understand competitive positioning.
- **get_rejection_analysis** — Seller intelligence: deep dive into why AI agents are rejecting a product. Returns rejection reasons ranked by frequency, weekly rejection trends, which products agents chose instead, and rejection rates by merchant. Use this to identify and fix product or listing issues.
- **get_category_demand** — Seller intelligence: see what AI agents are searching for in a product category. Returns top demanded features/constraints, unmet needs (from abandoned sessions), budget distribution, outcome rates, and top products. Use this to identify market gaps and product opportunities.
- **get_merchant_scorecard** — Seller intelligence: comprehensive merchant performance report across all agent sessions. Returns selection rate, stock reliability, price competitiveness, performance by category, and top products. Use this for merchant self-assessment or to compare merchant quality.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "agent-signal": {
    "command": "npx",
    "args": ["-y","agent-signal"],
    "env": {
      "DATABASE_URL": ""
    }
  }
}
```

**Requires environment variables:** `DATABASE_URL` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## 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.

_Full upstream README: https://allmcps.com/mcp/agent-signal/readme_