Agent Signal
This is a collective intelligence layer that pools shopping decisions across AI agents. It exposes 23 MCP tools split between buyer operations (get_product_intelligence, get_category_recommendations, detect_deal) and seller analytics (get_competitive_landscape, get_rejection_analysis, get_category_demand). The smart_shopping_session tool starts a session and returns category intel in one call, while evaluate_and_compare logs product evaluations and fetches product intelligence simultaneously. Every agent that logs selections, rejections, and comparisons feeds the network, so subsequent agents get selection rates, rejection patterns, price verdicts, and constraint matches drawn from 1,200+ prior sessions. Works via remote Railway endpoint or local npx, with examples for LangChain, CrewAI, AutoGen, and OpenAI Agents.
- tools
- 23
- transport
- STDIO, HTTP
- last updated
- Mar 20, 2026
Tools
23 toolsPublic tool metadata for what this MCP can expose to an agent.
smart_shopping_sessionStart session + get category intelligence + similar session outcomes — all in one call
Start session + get category intelligence + similar session outcomes — all in one call
No parameter schema in public metadata yet.
evaluate_and_compareLog product evaluation + get product intelligence + deal verdict — all in one call
Log product evaluation + get product intelligence + deal verdict — all in one call
No parameter schema in public metadata yet.
get_product_intelligenceSelection rate, rejection reasons, which competitors beat it and why
Selection rate, rejection reasons, which competitors beat it and why
No parameter schema in public metadata yet.
get_category_recommendationsTop picks, decision factors, common requirements, average budgets
Top picks, decision factors, common requirements, average budgets
No parameter schema in public metadata yet.
check_merchant_reliabilityStock accuracy, selection rate, purchase outcomes by merchant
Stock accuracy, selection rate, purchase outcomes by merchant
No parameter schema in public metadata yet.
get_similar_session_outcomesWhat agents with similar constraints ended up choosing
What agents with similar constraints ended up choosing
No parameter schema in public metadata yet.
detect_dealPrice verdict against historical data — best_price_ever to above_average
Price verdict against historical data — best_price_ever to above_average
No parameter schema in public metadata yet.
get_warningsStock issues, high rejection rates, abandonment signals
Stock issues, high rejection rates, abandonment signals
No parameter schema in public metadata yet.
get_constraint_matchProducts that exactly match your constraints — skip the search
Products that exactly match your constraints — skip the search
No parameter schema in public metadata yet.
get_competitive_landscapeCategory rank, head-to-head win rate, who beats you and why, price positioning
Category rank, head-to-head win rate, who beats you and why, price positioning
No parameter schema in public metadata yet.
get_rejection_analysisWhy agents reject your product, weekly trends, what they chose instead
Why agents reject your product, weekly trends, what they chose instead
No parameter schema in public metadata yet.
get_category_demandWhat agents are searching for, unmet needs, budget distribution, market gaps
What agents are searching for, unmet needs, budget distribution, market gaps
No parameter schema in public metadata yet.
get_merchant_scorecardFull merchant report — stock reliability, price competitiveness, selection rates by category
Full merchant report — stock reliability, price competitiveness, selection rates by category
No parameter schema in public metadata yet.
get_budget_productsBest products within a specific budget — ranked by agent selections, with merchant availability
Best products within a specific budget — ranked by agent selections, with merchant availability
No parameter schema in public metadata yet.
get_trending_productsProducts trending up or down — compares current vs previous period selection rates
Products trending up or down — compares current vs previous period selection rates
No parameter schema in public metadata yet.
create_price_alertSet a price alert — triggers when agents spot the product at or below your target
Set a price alert — triggers when agents spot the product at or below your target
No parameter schema in public metadata yet.
check_price_alertsCheck which alerts have been triggered by recent agent activity
Check which alerts have been triggered by recent agent activity
No parameter schema in public metadata yet.
log_shopping_sessionShopping intent, constraints, budget, exclusions
Shopping intent, constraints, budget, exclusions
No parameter schema in public metadata yet.
log_product_evaluationProduct considered, match score, disposition + rejection reason
Product considered, match score, disposition + rejection reason
No parameter schema in public metadata yet.
log_comparisonProducts compared, dimensions, winner, deciding factor
Products compared, dimensions, winner, deciding factor
No parameter schema in public metadata yet.
log_outcomeFinal result — purchased, recommended, abandoned, or deferred
Final result — purchased, recommended, abandoned, or deferred
No parameter schema in public metadata yet.
import_completed_sessionBulk import a completed session retroactively
Bulk import a completed session retroactively
No parameter schema in public metadata yet.
get_session_summaryRetrieve full session details
Retrieve full session details
No parameter schema in public metadata yet.
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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"
}
}
}
Details
- Repository
- github.com/dan24ou-cpu/agent-signal
- Category
- AI & LLM Tools
- Package
agent-signal- Transport
- STDIO, HTTP
- Registry
- MCP registry, active
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