Estimote MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Estimote through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Estimote "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Estimote?"
)
print(result.data)
asyncio.run(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.
Pydantic AI validates every Estimote tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Estimote MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Estimote with type-safe schemas
Why Use Pydantic AI with the Estimote MCP Server
Pydantic AI provides unique advantages when paired with Estimote through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Estimote integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Estimote connection logic from agent behavior for testable, maintainable code
Estimote + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Estimote MCP Server delivers measurable value.
Type-safe data pipelines: query Estimote with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Estimote tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Estimote and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Estimote responses and write comprehensive agent tests
Estimote MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Estimote to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Estimote to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiEstimote + Pydantic AI FAQ
Common questions about integrating Estimote MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.