MCP Integration for Dynamic Geo-Context Retrieval via Pages Functions

This repository showcases the implementation strategy for leveraging EdgeOne Pages Functions to ascertain the real-time geographical location of end-users, subsequently feeding this context into large language models utilizing the Model Context Protocol (MCP).

Demonstration Showcase

Deployment Shortcut

Explore Additional Blueprints: EdgeOne Pages Templates

Constituent Modules

1. EdgeOne Pages Handler: Geo-Positioning

This dedicated Pages Function is responsible for extracting current user location details:

• Accesses granular location metrics from the EdgeOne request environment.
• Formats the retrieved locational payload into standardized JSON.
• Source file location: functions/get_geo.ts

2. MCP Backend Interface

The core MCP service layer furnishes the necessary endpoint for AI systems to query spatial data:

• Adheres strictly to the Model Context Protocol specifications.
• Exposes a callable instrument named get_geolocation consumable by intelligent agents.
• Delegates the actual data fetching to the aforementioned EdgeOne Pages Function.
• Source file location: mcp-server/index.ts

MCP Provisioning Setup

To successfully wire this geo-context resolver into your AI framework, incorporate the following stanza into your MCP configuration manifest:

{ "mcpServers": { "edgeone-geo-mcp-server": { "command": "tsx", "args": ["path/to/mcp-server/index.ts"] } } }

Further Reference Materials

• EdgeOne Pages Product Overview
• EdgeOne Pages Functions Detailed Documentation
• Model Context Protocol (MCP) Specification - Essential reading for external tool integration with AI agents

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