Adding Initial MCP Customer Employee Example

modules/genai-ecosystem/pages/mcp-employee-customer.adoc

@@ -0,0 +1,109 @@
+= Multi-Domain Agent with MCP & Neo4j
+include::_graphacademy_llm.adoc[]
+:slug: mcp-employee-customer
+:author: Zach Blumenfeld
+:category: genai-tutorials
+:tags:
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: mcp-employee-customer
+
+image::mcp-customer-employee.png[align=center]
+This is a context engineering example demonstrating how to create intelligent agents across multiple business domains with graph databases and model context protocol (MCP).  Graph Databases represent context & associated metadata in an optimal matter, enabling agents to better decompose tasks and use mcp servers to execute intelligent workflows across multiple data sources.
+GraphRAG is involved in this process - as tools focus on graph retrieval, but think of graph context as a sort of meta-GraphRAG process. We aren't just telling agents how to retrieve data, we are guiding them over how to reason about what context to use.
+
+In this specific example, we work with a knowledge assistant that can help with
+
+1. **Employees & talent:** Skills analysis, talent search, and team formation
+2. **Customer experience:** Analyze & understand customer purchasing behavior & journeys. Specifically for a retail store in this example.
+3. **Identifying risks & making recommendations:** Putting the above together to understanding opportunities, costs, and make project and resourcing recommendations to improve ROI, reduce churn, etc.
+
+== Graph & MCP Server Setup
+
+The Setup for this example is split into 2 steps:
+
+1. Graph & MCP Server Setup
+2. MCP Client Configuration
+
+=== Graph & MCP Server Setup
+For this example, you will need to setup 2 knowledge graphs, and we will use the below two separate projects for the respective graphs:
+
+
+1. https://github.com/neo4j-product-examples/neo4j-employee-graph/tree/mcp-toolbox[Employee Graph]
+2. https://github.com/neo4j-product-examples/demo-cx/tree/mcp-example/mcp-tools-subgraph[Customer Graph]
+
+
+For each graph you will need a respective Neo4j database.  You can create Neo4j AuraDB instances at https://console.neo4j.io/. You can use the Aura Pro free trial for the customer graph and Aura free for the employee graph. During setup, you'll download a credentials file needed for later configuration. **Remember to make 2 databases**.
+
+After this you can do  the following:
+
+1. For the Employee graph, run the 4 module notebooks in order (modules 1, 2, 3 & 4) from https://github.com/neo4j-product-examples/neo4j-employee-graph/tree/mcp-toolbox[this GitHub repo branch]. See the directions for deploying MCP Toolbox at the end of the module 4 notebook as well.
+
+2. For the Customer graph, see the readme in https://github.com/neo4j-product-examples/demo-cx/tree/mcp-example/mcp-tools-subgraph[this GitHub repo branch] for instructions.  You will run a notebook and some similar scripts to get MCP Toolbox servers deployed.
+
+
+=== MCP Client Configuration
+You will leverage 4 MCP servers for this example. You can use any https://modelcontextprotocol.io/clients[MCP client], but here we will assume [https://claude.ai/downloadClaude Desktop] for which you would use the below configuration. See https://modelcontextprotocol.io/quickstart/user#installing-the-filesystem-server[MCP Desktop instructions] for more details on how to set this up. Other client configurations will vary, please see their associated docs.
+[source,json]
+----
+{
+  "mcpServers": {
+    "employee-cypher": {  <1>
+      "command": "uvx",
+      "args": [
+        "mcp-neo4j-cypher"
+      ],
+      "env": {
+        "NEO4J_URI":"neo4j+s://xxxxxxxx.databases.neo4j.io",
+        "NEO4J_USERNAME":"neo4j",
+        "NEO4J_PASSWORD": "xxxxxxxxxxxx"
+      }
+    },
+    "employee-tools": {  <2>
+      "command": "npx",
+      "args": [
+        "mcp-remote",
+        "https://employee-toolbox-xxxxxxxxxxxx.us-central1.run.app/mcp"
+      ]
+    },
+    "customer-cypher": { <3>
+      "command": "uvx",
+      "args": [
+        "mcp-neo4j-cypher"
+      ],
+      "env": {
+        "NEO4J_URI":"neo4j+s://xxxxxxxx.databases.neo4j.io",
+        "NEO4J_USERNAME":"neo4j",
+        "NEO4J_PASSWORD": "xxxxxxxxxxxx"
+      }
+    },
+    "customer-tools": { <4>
+      "command": "npx",
+      "args": [
+        "mcp-remote",
+        "https://customer-toolbox-xxxxxxxxxxxx.us-central1.run.app/mcp"
+      ]
+    }
+  }
+}
+----
+<1> `employee-cypher`: Enables the client (AI agent) to get the schema and execute its own Cypher (graph queries) against the employee graph
+<2> `employee-tools`: Provides specific Cypher templates to the AI agent. Think of these as expert crafted tools for finding similarities and summaries of employees and talent which can accept arguments/parameters from the agent (such as employee names, skill types, domains, etc. )
+<3> `customer-cypher`: Like `employee-cypher` enables the client (AI agent) to get the schema and execute its own Cypher (graph queries) against the customer graph.
+<4> `customer-tools`: Similar to `employee-tools` provides specific Cypher templates to the AI agent but to access the customer graph.
+
+
+the `*-cypher` servers use the already available https://github.com/neo4j-contrib/mcp-neo4j/tree/main/servers/mcp-neo4j-cypher[`mcp-neo4j-cypher`].
+
+The `*-tool` servers use Google MCP Toolbox to create servers with parameterized Cypher templates. The code for doing so is located in the respective repositories below
+
+
+== Sample Questions
+
+Below are some sample questions you can ask via Claude Desktop or your chosen client
+
+1. Can you summarize my technical talent and skills distribution?  Feel free to include simple graphics.
+
+2. Let's switch focus to our customers.  Can you tell me about my customer purchase patterns and segments? What does my churn risk look like? Feel free to include simple graphics.
+
+3. Who from our team can I put on this to figure out the right type of outreach? Can you write a brief project proposal?