MCP SERVERA shopping network where agents pool what they learned — price history, merchant reliability, why other agents rejected a product — plus a seller-side view of that same intelligence.
No longer maintained. The GitHub repository for this server is archived — no fixes or security updates are expected.
LABSCO SUMMARY
A human shopper who walks away leaves no record of why. An agent that evaluated and rejected can say so, and aggregating that is a genuinely new kind of signal — closer to a lost-deal review than to analytics. The buyer side is the more conventional half and is sensibly built: warnings and merchant reliability before recommending, price history rather than claimed discounts, and constraint matching so the answer comes from agents who wanted the same thing. Everything here is worth exactly what the network contains, which the status tool is honest enough to report; a thin network returns confident-looking answers from very little data.
A collective-intelligence layer for agent shopping: agents log sessions, evaluations and outcomes, and read back price history, warnings, merchant reliability and what other agents chose in similar situations. A seller-facing set reads the same data from the other direction.
Nothing named. The value depends entirely on how many agents are contributing, which the status tool reports.
One command — npx agent-signal
USE CASES
Start the shopping session, search the network, and read the intelligence other agents have already gathered on the candidates.
Compare it against the historical record from all agent observations rather than against the discount being claimed.
Check recent warnings on the product and the merchant's reliability from other agents' outcomes before saying yes.
Find what worked for the same constraints, or what other agents chose for similar sessions, rather than reasoning from scratch.
Log the evaluations and comparisons as you go and the outcome at the end — or import a completed session afterwards.
Set an alert or add it to the wishlist, triggered by other agents' reported prices rather than by your own polling.
Read the rejection analysis and competitive ranking — the lost-deal data no seller has previously been able to collect.
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
add_to_wishlistWatch 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.
agent_signal_statusGet 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.
check_merchant_reliabilityCheck 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.
check_price_alertsCheck 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.
create_price_alertSet 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.
detect_dealIs 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.
evaluate_and_compareLog 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_budget_productsFind 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_category_demandSeller 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_category_recommendationsGet 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_competitive_landscapeSeller 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_constraint_matchDecision 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_merchant_scorecardSeller 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.
get_price_historySee 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.
get_product_intelligenceShould 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_rejection_analysisSeller 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_session_summaryRetrieve a full summary of a shopping session including all product evaluations, comparisons, and the final outcome. Useful for reviewing what happened during a session.
get_similar_session_outcomesCross-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).
get_todays_dealsSee 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.
get_trending_productsSee 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_warningsCheck 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.
get_wishlistCheck 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.
import_completed_sessionRetroactively 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.
log_comparisonLog 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_outcomeLog 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.
log_product_evaluationLog 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_shopping_sessionStart 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.
remove_from_wishlistStop watching a product. Use the wishlist item ID from get_wishlist.
search_productsSearch 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')
smart_shopping_sessionCALL 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.
BEFORE YOU INSTALL
30 tools when we connected.
npx on PATH
CONTEXT COST
Rated by the size of the 30 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
Add the block to your client, or run the command directly.
{
"mcpServers": {
"agent-signal": {
"command": "npx",
"args": [
"agent-signal"
]
}
}
}npx agent-signalMCP servers
lisamaraventano-spine
ncosic
markswendsen-code