de en es ja ko ru zh

agent-signal

Name: agent-signal
Author: dan24ou-cpu

by dan24ou-cpu

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

AgentSignal

npm version GitHub stars License: MIT MCP Tools

AI 购物智能体的集体智慧层。

每一个接入的智能体都会让其他智能体变得更聪明。目前已涵盖 1,200 多次购物会话、95 种产品、50 个商家、10 个类别，且数量还在不断增长。

存在意义： 当 AI 智能体为用户购物时，每个智能体都是从零开始。AgentSignal 汇集了所有智能体的决策信号，让每一次会话都能受益于其他智能体已经学到的经验——包括选择率、拒绝模式、价格情报、商家可靠性以及经过验证的约束匹配。

快速入门 (30 秒)

远程模式——无需安装，即刻获取智能：

{
  "mcpServers": {
    "agent-signal": {
      "url": "https://agent-signal-production.up.railway.app/mcp"
    }
  }
}

本地模式 (通过 npx)：

npx agent-signal

Claude Desktop / Claude Code：

{
  "mcpServers": {
    "agent-signal": {
      "command": "npx",
      "args": ["agent-signal"]
    }
  }
}

Related MCP server: agent-commerce-mcp-server

一次调用，开启更智能的购物体验

smart_shopping_session 工具可以在单次调用中记录您的会话并返回所有可用的情报：

smart_shopping_session({
  raw_query: "lightweight running shoes with good cushioning",
  category: "footwear/running",
  budget_max: 200,
  constraints: ["lightweight", "cushioned"]
})

返回内容：

用于后续记录的会话 ID
该类别中其他智能体的热门选择
最重要的约束条件和影响因素
类似会话的最终结果（购买 vs 放弃）
全网统计数据

23 个 MCP 工具

智能组合工具 (推荐)

工具	功能
`smart_shopping_session`	开始会话 + 获取类别情报 + 类似会话结果 — 一次调用全部完成
`evaluate_and_compare`	记录产品评估 + 获取产品情报 + 交易结论 — 一次调用全部完成

买家情报 — 更智能地购物

工具	提供的信息
`get_product_intelligence`	选择率、拒绝原因、哪些竞争对手胜出及其原因
`get_category_recommendations`	热门选择、决策因素、常见需求、平均预算
`check_merchant_reliability`	库存准确性、选择率、按商家划分的购买结果
`get_similar_session_outcomes`	具有相似约束条件的智能体最终选择了什么
`detect_deal`	基于历史数据的价格结论 — 从 best_price_ever 到 above_average
`get_warnings`	库存问题、高拒绝率、放弃信号
`get_constraint_match`	完全符合您约束条件的产品 — 跳过搜索过程

卖家情报 — 了解您的市场

工具	提供的信息
`get_competitive_landscape`	类别排名、直接竞争胜率、谁击败了您及其原因、价格定位
`get_rejection_analysis`	智能体拒绝您产品的原因、每周趋势、他们选择了什么替代品
`get_category_demand`	智能体在搜索什么、未满足的需求、预算分布、市场空白
`get_merchant_scorecard`	完整的商家报告 — 库存可靠性、价格竞争力、各类别选择率

发现与监控

工具	提供的信息
`get_budget_products`	特定预算内的最佳产品 — 按智能体选择排名，包含商家库存情况
`get_trending_products`	趋势上升或下降的产品 — 比较当前与上一周期的选择率
`create_price_alert`	设置价格提醒 — 当智能体发现产品价格达到或低于您的目标时触发
`check_price_alerts`	查看哪些提醒已被最近的智能体活动触发

写入工具 — 回馈数据

工具	捕获的内容
`log_shopping_session`	购物意图、约束条件、预算、排除项
`log_product_evaluation`	考虑中的产品、匹配得分、处置方式 + 拒绝原因
`log_comparison`	比较的产品、维度、胜出者、决定性因素
`log_outcome`	最终结果 — 已购买、已推荐、已放弃或已推迟
`import_completed_session`	批量导入已完成的会话（追溯）
`get_session_summary`	获取完整的会话详情

示例：完整的智能体工作流

# 1. Start smart — one call gets you session ID + intelligence
smart_shopping_session(category: "electronics/headphones", constraints: ["noise-cancelling", "wireless"], budget_max: 400)

# 2. Evaluate products — get intel as you log
evaluate_and_compare(session_id: "...", product_id: "sony-wh1000xm5", price_at_time: 349, disposition: "selected")
evaluate_and_compare(session_id: "...", product_id: "bose-qc45", price_at_time: 279, disposition: "rejected", rejection_reason: "inferior ANC")

# 3. Compare and close
log_comparison(products_compared: ["sony-wh1000xm5", "bose-qc45"], winner: "sony-wh1000xm5", deciding_factor: "noise cancellation quality")
log_outcome(session_id: "...", outcome_type: "purchased", product_chosen_id: "sony-wh1000xm5")

每一步都为网络提供反馈。下一个购买耳机的智能体将受益于您的数据。

示例：卖家情报工作流

# 1. How is my product performing vs competitors?
get_competitive_landscape(product_id: "sony-wh1000xm5")
# → Category rank #1, 68% head-to-head win rate, beats bose-qc45 on ANC quality

# 2. Why are agents rejecting my product?
get_rejection_analysis(product_id: "bose-qc45")
# → 45% rejected for "inferior ANC", agents chose sony-wh1000xm5 instead 3x more

# 3. What do agents want in my category?
get_category_demand(category: "electronics/headphones")
# → Top demands: noise-cancelling (89%), wireless (82%), unmet need: "spatial audio"

# 4. How does my store perform?
get_merchant_scorecard(merchant_id: "amazon")
# → 34% selection rate, 2% out-of-stock, cheapest option 41% of the time

拥有活跃情报的类别

类别	会话数
footwear/running	150+
electronics/headphones	140+
gaming/accessories	130+
electronics/tablets	130+
home/furniture/desks	120+
fitness/wearables	118+
electronics/phones	115+
home/smart-home	107+
kitchen/appliances	105+
electronics/laptops	98+

智能体框架示例

可在 /examples 中找到可直接运行的示例：

框架	文件	描述
LangChain	`langchain-shopping-agent.py`	带有 LangGraph + MCP 适配器的 ReAct 智能体
CrewAI	`crewai-shopping-crew.py`	双智能体团队（研究员 + 购物者）
AutoGen	`autogen-shopping-agent.py`	带有 MCP 工具的 AutoGen 智能体
OpenAI Agents	`openai-agents-shopping.py`	带有 Streamable HTTP 的 OpenAI Agents SDK
Claude	`claude-system-prompt.md`	针对 Claude Desktop/Code 优化的系统提示词

所有示例均连接到托管的 MCP 端点 — 除了 pip install 外无需其他设置。

REST API

面向商家的分析接口位于 https://agent-signal-production.up.railway.app/api：

端点	描述
`GET /api/products/:id/insights`	产品分析 — 考虑率、拒绝原因
`GET /api/categories/:category/trends`	类别趋势 — 热门因素、预算、属性
`GET /api/competitive/lost-to?product_id=X`	竞争损失 — X 输给了谁及其原因
`GET /api/sessions`	最近的会话（分页）
`GET /api/sessions/:id`	完整的会话详情
`POST /api/admin/aggregate`	触发情报计算
`GET /api/health`	健康检查

自托管

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

架构

MCP Server — Stdio 传输（本地）+ Streamable HTTP（远程）
REST API — 同一端口上的 Express
Database — PostgreSQL (兼容 Neon)
23 个 MCP 工具 — 17 个读取（买家 + 卖家 + 发现）+ 6 个写入

许可证

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Related Servers

Related MCP Connectors

marketbasketanalysis-mcp A
io.github.48x-ai
Co-purchase intelligence and merchant ops tools for AI shopping, ecommerce, and B2B agents
E-commerce & Retail Research & Data
191
shopping-discoveryOAuth
io.github.Periskop-ai
Product discovery for AI agents: ranked products and bundles from the open merchant web.
E-commerce & Retail Search
2
agent-commerce
ai.petraport
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
E-commerce & Retail Search
2
OpenShopGraph A
com.openshopgraph
Open, verified shop database for AI agents: products, offers, price comparison, trust and coupons.
E-commerce & Retail Search Open Data
81

Related MCP Servers

Godaloofficial
E-commerce & Retail Search
Godalo-ai
A
license
Not graded
quality
D
maintenance
Affiliate product search for AI agents. Indexes structured merchant feeds — real prices, live stock, affiliate links built in. Works with any MCP client.
Updated 4 months ago (2026-06-04 11:51 UTC)
MIT
agent-commerce-mcp-server
E-commerce & Retail Autonomous Agents
AiAgentKarl
A
license
A
quality
D
maintenance
Enables AI agents to create, compare, and track purchases with structured buying workflows, offer comparison, and merchant verification.
Updated 6 months ago (2026-04-03 16:05 UTC)
5
MIT
Agentic Product Protocol MCP
E-commerce & Retail Search
AiAgentKarl
A
license
A
quality
D
maintenance
Klarna-style product discovery for AI shopping agents. Makes product catalogs machine-readable so AI agents can search, compare, and purchase products programmatically.
Updated 6 months ago (2026-03-20 11:47 UTC)
6
MIT
Periskop MCP Serverofficial
E-commerce & Retail Search
Periskop-ai
A
license
Not graded
quality
C
maintenance
Enables AI agents to perform product discovery from natural language shopping intents, returning ranked products with merchant links without completing checkout.
Updated 3 months ago (2026-07-01 11:42 UTC)
MIT