mcp-server-birdstats
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Integrations
Allows users to access repository functionality, including cloning the repository and handling issues for feature requests and bug reports.
Facilitates installation and execution of the MCP plugin through NPX commands.
Enables data-driven visualizations using React artifacts for displaying bird detection data and statistics.
mcp-server-birdstats
Cross-reference your BirdNET-Pi data with eBird observations using natural language
Overview
This MCP plugin allows you to analyze BirdNET-Pi/BirdWeather detections by connecting them with eBird's extensive database.
Features
- Natural language interface for querying birdnet-pi / birdweather, and eBird data
- Cross-reference BirdWeather detections with eBird observations
- Data-driven visualizations with React artifacts
Installation
- Clone the repository:
- Install dependencies:
Claude Config
Usage
The plugin processes natural language queries about your bird detection data. Example queries:
- "My BirdNET heard a Swainson's Thrush - is that unusual?"
- "What's my station's rarest detection this week?"
- "Graph my detections by time of day"
- "Are others seeing these warblers on eBird?"
Data Integrity
This plugin strictly adheres to data integrity principles:
- Only uses actual API response data
- Never creates example or estimated data
- Always shows observation counts with percentages
- Clear indication when data is unavailable
Core Logic
The core functionality is contained in a single text file under src/index.txt
which includes:
- BirdWeather API interactions
- eBird API interactions
- Data analysis functions
- Type definitions
- Helper utilities
API Integration
BirdWeather API
- Station statistics
- Species lists
- Detection records
eBird API
- Recent observations
- Notable sightings
- Hotspot data
- Species information
License
MIT
Support
For issues and feature requests, please open an issue on the GitHub repository.
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Cross-reference your BirdNET-Pi data with eBird observations using natural language