Estimote MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Estimote through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Estimote, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Estimote MCP Server
Connect your Estimote Cloud account to any AI agent and take full control of your beacon fleet management and proximity data workflows through natural conversation.
The Vercel AI SDK gives every Estimote tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 10 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Fleet Orchestration — List all Estimote beacons including Proximity, Location, and Stickers, returning identifiers, hardware types, and current battery levels natively
- Device Shadow Management — Retrieve detailed configurations and status for specific beacons and update broadcasting parameters or transmission power through the shadow system
- Proximity Analytics — Pull detection counts, unique visitor estimates, and dwell time distributions over specified periods to measure real-world engagement
- Real-time Telemetry — Access live sensor data including temperature readings, ambient light levels, motion detection, and barometric pressure from supported hardware
- Physical Location Auditing — Register and manage venues, buildings, or stores, providing geographic coordinates for beacon organization and analytics grouping
- Taxonomy & Tagging — List fleet tags and assign organizational labels to devices for logical grouping and proximity campaign targeting
- Decommissioning Oversight — Permanently remove beacon devices from your cloud account while maintaining physical broadcasting for legacy integrations
The Estimote MCP Server exposes 10 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Estimote to Vercel AI SDK via MCP
Follow these steps to integrate the Estimote MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 10 tools from Estimote and passes them to the LLM
Why Use Vercel AI SDK with the Estimote MCP Server
Vercel AI SDK provides unique advantages when paired with Estimote through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Estimote integration everywhere
Built-in streaming UI primitives let you display Estimote tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Estimote + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Estimote MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Estimote in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Estimote tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Estimote capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Estimote through natural language queries
Estimote MCP Tools for Vercel AI SDK (10)
These 10 tools become available when you connect Estimote to Vercel AI SDK via MCP:
assign_tag_to_beacon
If the tag does not exist, it is created automatically. A device can have multiple tags. Use to organize beacons by floor, zone, store section, or campaign. Tags persist in the cloud and do not require physical beacon access. Assign an organizational tag to a specific Estimote beacon device, adding it to a logical group for fleet management, analytics filtering, and proximity campaign targeting
create_physical_location
After creating a location, assign beacon devices to it for organized fleet management and location-scoped analytics. Use when deploying beacons at a new site. Register a new physical location (store, office, venue) in Estimote Cloud, providing the site name, street address, and geographic coordinates for beacon fleet organization and analytics grouping
get_beacon_details
The identifier is the beacon MAC address or Estimote Cloud ID. Returns the full device shadow including pending settings changes. Use to diagnose beacon configuration issues or verify firmware update status. Retrieve detailed configuration and status for a specific Estimote beacon device, including its current broadcasting power, advertising interval, sensor readings, firmware version, and physical location assignment
get_beacon_telemetry
Returns the most recent sensor readings from the beacon. Not all sensors are available on all hardware models. Estimote Proximity Beacons support temperature and motion; Location Beacons add light and pressure sensors. Use for environmental monitoring and occupancy detection. Retrieve real-time sensor telemetry data from a specific Estimote beacon, including temperature readings, ambient light levels, accelerometer motion detection, magnetometer orientation, and barometric pressure where supported by hardware
get_device_analytics
Supports query parameters for date range (from, to), device identifier, and tag filtering. Returns aggregated metrics showing how many mobile devices detected each beacon. Use for foot traffic analysis, retail engagement measurement, and space utilization studies. Retrieve proximity analytics data for Estimote beacon devices, including detection counts, unique visitor estimates, dwell time distributions, and engagement metrics over a specified time period
list_beacon_devices
estimote.com. Returns a paginated array of beacon objects. Each beacon includes its MAC address (the most reliable identifier), iBeacon UUID/Major/Minor, Eddystone namespace/instance, and shadow settings. Use to inventory your deployed beacon fleet. List all Estimote beacon devices registered in your Estimote Cloud account, returning device identifiers, hardware types (Proximity/Location/Sticker), battery levels, firmware versions, and current configuration status
list_fleet_tags
Returns an array of tag objects with names and associated device counts. Tags are the primary organizational mechanism in Estimote Cloud. Use to understand your current fleet taxonomy before assigning or filtering devices. List all organizational tags defined in your Estimote Cloud account, which are used to group and categorize beacon devices by location, use case, department, or any custom classification scheme
list_physical_locations
Returns an array of location objects. Locations serve as containers for organizing beacons by physical site. Each location can have multiple beacon devices assigned to it. Use to audit your deployment footprint across multiple sites. List all physical locations (venues/buildings/stores) registered in your Estimote Cloud account, returning location names, addresses, geographic coordinates, and the number of beacons deployed at each site
remove_beacon_device
WARNING: This permanently removes the device from your fleet. The beacon will continue broadcasting but will no longer be managed by Estimote Cloud. Only use when decommissioning hardware. The device can be re-added later via the Estimote app. Permanently remove an Estimote beacon device from your Cloud account, deleting all associated configuration, analytics history, and location assignments. This action is irreversible
update_beacon_settings
Changes are queued in the cloud shadow and synchronized to the physical beacon when a device running the Estimote SDK connects to it. Common updates include name, tags, broadcasting power (dBm), and advertising interval (ms). Update the configuration of a specific Estimote beacon device by modifying its broadcasting parameters, advertising interval, transmission power, or attached metadata tags through the Estimote Cloud shadow system
Example Prompts for Estimote in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Estimote immediately.
"List all my beacons and their current battery status"
"What is the current temperature at 'Beacon-XYZ'?"
"Show me visitor analytics for the 'Main Store' tag from last month"
Troubleshooting Estimote MCP Server with Vercel AI SDK
Common issues when connecting Estimote to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpEstimote + Vercel AI SDK FAQ
Common questions about integrating Estimote MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.Connect Estimote with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Estimote to Vercel AI SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.