Birdnet Go MCP

birdnet-go-mcp

A high-performance Model Context Protocol (MCP) server & CLI for BirdNET-Go bioacoustic observatories.

Connect Claude, Antigravity, OpenClaw, and local LLMs to your backyard bird monitoring station.

---

Highlights

• Native BirdNET-Go Support: Interfaces directly with BirdNET-Go's v2 REST API over LAN or localhost. No raw database locking or unmaintained Python dependencies.
• Instant npx Run: Launch immediately with npx -y birdnet-go-mcp — zero Go toolchain required.
• Zero-Dependency Static Binary: Single Go binary (CGO_ENABLED=0) compiled for Linux, macOS, and Windows.
• Dual-Mode (CLI + MCP): Human-friendly CLI for quick terminal health checks (birdnet-mcp status), plus full stdio & SSE MCP server for AI agents.
• Context-Protected (8KB Envelope): Hard-capped output preventing multi-hundred detection queries from overflowing model context windows.
• Read-Only & Parallel-Safe: Omits all mutating/destructive endpoints. Annotates all tools with readOnlyHint and idempotentHint for fast parallel agent calls.
• Audio & Clip Access: Resolves LAN Caddy/Nginx .wav clip URLs, with built-in base64 audio streaming for multimodal models.

---

Quick Start

1. Instant Run via NPX (Recommended for Claude Desktop & Node users)

No Go installation needed. Downloads the native binary for your platform automatically:

# Check station health in your terminal
npx -y birdnet-go-mcp status

# Or set target host
BIRDNET_BASE_URL="http://192.0.2.10:8080" npx -y birdnet-go-mcp recent

2. Install via Go

go install github.com/zax0rz/birdnet-go-mcp/cmd/birdnet-mcp@latest

3. Pre-Compiled Binaries

Download the latest static binary for your architecture from GitHub Releases:

• darwin-arm64 (Apple Silicon M1/M2/M3/M4)
• darwin-amd64 (Intel Mac)
• linux-amd64 (x86_64 servers, Proxmox LXC, Docker)
• linux-arm64 (Raspberry Pi 4 / 5)
• linux-armv7 (Raspberry Pi 2 / 3 / Zero 2 W)
• windows-amd64

---

Client & Harness Setup

Claude Desktop & Cursor

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS), %APPDATA%\Claude\claude_desktop_config.json (Windows), or add under Cursor Settings ➔ Features ➔ MCP:

Option A: Using npx (Easiest)

{
  "mcpServers": {
    "birdnet": {
      "command": "npx",
      "args": ["-y", "birdnet-go-mcp", "serve"],
      "env": {
        "BIRDNET_BASE_URL": "http://192.0.2.10:8080",
        "CLIPS_BASE_URL": "http://192.0.2.10:8091"
      }
    }
  }
}

Option B: Using Native Binary

{
  "mcpServers": {
    "birdnet": {
      "command": "/usr/local/bin/birdnet-mcp",
      "args": ["serve"],
      "env": {
        "BIRDNET_BASE_URL": "http://192.0.2.10:8080",
        "CLIPS_BASE_URL": "http://192.0.2.10:8091"
      }
    }
  }
}

---

Claude Code

Add directly via CLI:

claude mcp add birdnet -- npx -y birdnet-go-mcp serve

---

Google Antigravity

In your Antigravity MCP configuration (~/.gemini/antigravity/mcp/ or project settings):

{
  "mcpServers": {
    "birdnet": {
      "command": "birdnet-mcp",
      "args": ["serve"],
      "env": {
        "BIRDNET_BASE_URL": "http://192.0.2.10:8080",
        "CLIPS_BASE_URL": "http://192.0.2.10:8091"
      }
    }
  }
}

---

OpenClaw

In ~/.openclaw/openclaw.json:

{
  "mcp": {
    "servers": {
      "birdnet": {
        "command": "/Users/zach/.openclaw/mcp-servers/birdnet-go-mcp/bin/birdnet-mcp",
        "args": [],
        "env": {
          "BIRDNET_BASE_URL": "http://192.0.2.10:8080",
          "CLIPS_BASE_URL": "http://192.0.2.10:8091"
        },
        "toolFilter": {
          "include": ["*"]
        }
      }
    }
  }
}

In your agent's tools.allow list:

"tools": {
  "allow": [
    "birdnet__get_recent_detections",
    "birdnet__search_detections",
    "birdnet__get_detection_detail",
    "birdnet__get_today_summary",
    "birdnet__get_new_arrivals",
    "birdnet__get_station_health",
    "birdnet__get_audio_clip",
    "birdnet__get_audio_clip_base64"
  ]
}

---

Remote / Headless Agents (SSE HTTP Mode)

If your agent runs on another machine or in the cloud without access to local stdio:

# Start background SSE server on port 8092
birdnet-mcp serve --sse --port 8092

Point your agent to: http://<your-server-ip>:8092/sse

---

Interactive CLI Commands

birdnet-mcp is a full CLI tool for humans as well as an MCP server for agents.

Check Station & RTSP Mic Health

$ birdnet-mcp status

🔍 Connecting to BirdNET-Go at http://192.0.2.10:8080...

=== AUDIO STREAMS ===
NAME          TYPE   HEALTH      STATE     THROUGHPUT   LAST RECEIVED
birdz0rz-pi   rtsp   🟢 HEALTHY   running   74.8 KB/s    2026-09-04T20:38:53-04:00

=== DETECTOR HOST ===
Host:         birdz0rz (Debian Linux, x86_64)
CPU:          AMD Ryzen 5 PRO 2400G (2 cores)
Uptime:       15d 3h 6m (Host) | 6d 4h 47m (BirdNET-Go)
Kernel:       7.0.14-12-pve
Environment:  LXC

View Recent Sightings

$ birdnet-mcp recent --limit 5 --min-conf 0.80

ID     TIME                  SPECIES   COMMON NAME        CONF   NEW?   CLIP URL
#129   2026-09-04 19:59:35   easblu    Eastern Bluebird   84%    -      http://192.0.2.10:8091/2026/09/sialia_sialis_84p_20260904T195937Z.wav
#128   2026-09-04 19:58:17   easblu    Eastern Bluebird   95%    -      http://192.0.2.10:8091/2026/09/sialia_sialis_95p_20260904T195819Z.wav
#127   2026-09-04 19:05:23   blujay    Blue Jay           84%    -      http://192.0.2.10:8091/2026/09/cyanocitta_cristata_84p_20260904T190525Z.wav
#126   2026-09-04 18:46:16   carwre    Carolina Wren      96%    -      http://192.0.2.10:8091/2026/09/thryothorus_ludovicianus_96p_20260904T184618Z.wav
#124   2026-09-04 18:35:34   houfin    House Finch        90%    -      http://192.0.2.10:8091/2026/09/haemorhous_mexicanus_90p_20260904T183536Z.wav

Download Audio Recording

$ birdnet-mcp clip sialia_sialis_95p_20260904T195819Z.wav --download --out bluebird.wav
✅ Saved 1440044 bytes to bluebird.wav

---

MCP Tool Reference

min_confidence (0.0-1.0)
species_code (string) | Fetches the most recent bird acoustic detections with confidence scores, timestamps, and audio clip URLs. | | search_detections | date (YYYY-MM-DD)
species (name/code)
min_confidence (0.0-1.0)
limit (int) | Historical search through detection records stored in SQLite. | | get_detection_detail | id (int, required) | Inspects a single detection: weather conditions at detection time, confidence breakdown, and audio clip info. | | get_today_summary | (none) | Aggregate summary of today's observatory run: total count, species diversity, and top visitors. | | get_new_arrivals | (none) | Identifies species heard for the first time ever or new this season/year (vital for tracking migration). | | get_station_health | (none) | Real-time diagnostic telemetry: RTSP mic stream state, ingest bit rate, dropped packets, and host uptime. | | get_audio_clip | clip_name (string)
detection_date (string) | Resolves the accessible LAN URL on Caddy/Nginx (:8091) for Discord/web embedding. | | get_audio_clip_base64 | clip_name (string)
detection_date (string) | Downloads and base64-encodes the raw .wav audio clip for multimodal models with direct audio input capabilities.
⚠️ Token budget warning: A 15-second WAV clip is ~1.4–1.9MB base64 (~350,000–500,000 tokens into Gemini/Claude multimodal models). Use selectively for verification; never put this in automated high-frequency briefing crons! |

MCP Resources

• birdnet://station/health — Live JSON snapshot of the Pi mic stream and BirdNET-Go engine.
• birdnet://detections/recent — The 10 most recent detections.
• birdnet://species/summary — Aggregated species occurrence summary.

MCP Prompts

• daily_backyard_brief — Morning dispatch workflow template for resident bird agents.
• investigate_detection — Deep-dive template for evaluating anomalous sightings against eBird.

---

Configuration Reference

---

Field-Tested in Production: The Origin Story & Hardening

birdnet-go-mcp wasn't built in a theoretical vacuum — it was forged and verified against a live backyard bioacoustic observatory:

• Mic Station: Raspberry Pi Zero 2 W mounted outdoors with a weather-sealed electret microphone streaming 48kHz mono audio via RTSP (mediamtx) at ~75 KB/s over Wi-Fi.
• Detector Host: BirdNET-Go running inside a Debian 12 Proxmox LXC container (amd64, AMD Ryzen 5 PRO), analyzing audio chunks with the Cornell Lab of Ornithology neural network.
• Clip Web Server: Caddy reverse proxy serving /var/lib/birdnet-go/clips/ over HTTP on port 8091.
• Agent Mesh: OpenClaw on an Apple Silicon Mac Mini running autonomous resident agents:
  • Blenda (Infra agent, GLM-5.3): Monitors server health, manages gateway restarts, and oversees tool permissions.
  • Leopold (Resident Naturalist, Gemini 3.8 Flash): Composes daily backyard wildlife briefings, flags unusual species (Eastern Bluebirds, Carolina Wrens, Pileated Woodpeckers), and inspects audio spectrograms.

Real-World Lessons & "Incident Zero"

1. The Caddy Directory Permission Trap (Incident Zero): BirdNET-Go creates monthly clip directories (/clips/YYYY/MM/) with 750 permissions (drwxr-x---). External web servers (Caddy, Nginx) running as their own system user will hit HTTP 403 Forbidden when serving audio clips to agents or Discord webhooks. Fix: Set permissions to 755 on existing month folders and add your web server user to the birdnet group:

    sudo chmod -R 755 /var/lib/birdnet-go/clips
    sudo usermod -aG birdnet caddy

2. Apple Silicon AMFI & Cross-Compilation: When cross-compiling Go binaries from Linux for macOS (GOOS=darwin GOARCH=arm64) with stripped debug symbols (-ldflags="-s -w"), macOS Apple Mobile File Integrity (AMFI) will immediately terminate the process with SIGKILL (exit code 137). All Darwin ARM64 releases are properly ad-hoc codesigned (codesign -s - --force).
3. Context Window Token Budget Guard: Feeding raw base64 WAV recordings into multimodal LLMs is magical for verifying difficult bird calls, but a single 15-second WAV consumes ~1.9MB (approx. 500,000 tokens). That can consume 50% of a 1M-token context window in one tool invocation. birdnet-go-mcp strictly caps all structured JSON tool responses at an 8KB envelope cap, while keeping get_audio_clip_base64 explicitly exempt so models can call it intentionally without risk of accidental context blowup in daily briefing routines.

---

Contributing & Development

# Clone
git clone https://github.com/zax0rz/birdnet-go-mcp.git
cd birdnet-go-mcp

# Run unit tests
make test

# Build for local OS
make build

# Cross-compile for Darwin ARM64 (Apple Silicon)
make build-mac

---

Credits & License

• Built with mark3labs/mcp-go.
• Designed for tphakala/birdnet-go.
• Bird identification neural network developed by the Cornell Lab of Ornithology and Chemnitz University of Technology.
• Licensed under the MIT License.