This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
A Python-based Model Context Protocol (MCP) server that connects AI assistants to Reltio data through a set of structured, reusable tools.
The server exposes Reltio entity search, entity details, record counts, and relationship retrieval as MCP tools, allowing an MCP-compatible AI client to interact with Reltio data through natural-language requests.
This project demonstrates how to build an MCP server that acts as a bridge between an AI assistant and a Reltio data environment.
Instead of requiring the AI assistant to directly understand and manage Reltio API requests, the MCP server exposes high-level tools such as:
Search contacts
Search organizations
Search locations
Search stations
Search voyagers
Search any supported entity type
Retrieve complete entity details
Count records by entity type
Retrieve entity relationships
The server communicates with Reltio using its REST APIs and exposes the functionality through the Model Context Protocol (MCP).
Related MCP server: ContextFlow MCP Server
AI Assistant / MCP Client
│
│ MCP / stdio
▼
┌──────────────────┐
│ Reltio MCP │
│ Server │
│ (Python) │
└────────┬─────────┘
│
│ REST API
▼
┌──────────────────┐
│ Reltio │
│ Data Platform │
└──────────────────┘Natural Language Request ↓ MCP Client ↓ MCP Tool Call ↓ Python Server ↓ Reltio REST API ↓ Response Processing ↓ Structured JSON ↓ MCP Client
📚 Key Concepts Demonstrated
This project demonstrates practical experience with:
Model Context Protocol (MCP) AI-to-data integration API integration REST APIs Reltio MDM Asynchronous Python HTTP clients Environment-based configuration JSON data transformation Tool-based AI interaction Entity search and relationship traversal
This server cannot be deployed
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