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Macaulay Library

Macaulay Library MCP for AI. Query millions of wildlife records by species and region.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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How this MCP server connects to your AI agent

Macaulay Library MCP Server connects your AI client directly to Cornell Lab of Ornithology's scientific media database. You can search millions of photos, audio recordings, and videos by species, location, or contributor.

It retrieves deep metadata for specific assets using ML Catalog Numbers, tracks the latest global wildlife uploads, and filters massive datasets for ornithological research.

What AI agents can do with Macaulay Library Automation

Get asset

Retrieves all scientific and technical metadata for one specific media asset using its ML Catalog Number.

Get recent media

Fetches the most recently uploaded wildlife content, optionally filtered by a country or region code.

Search media

Searches the library for assets based on criteria like species name, location, contributor, or media type.

Search for specific assets

You specify criteria—like a species name or region—and the server returns relevant photo, audio, or video assets.

Retrieve detailed asset information

By providing a unique ML Catalog Number, you get all the scientific and technical data associated with that single piece of media.

Track new submissions

The server fetches the most recently uploaded wildlife content, allowing you to monitor global or regional activity feeds.

Included with Plan

Waiting for input…

AI Agent

What AI agents can do with Macaulay Library: 3 Tools for Scientific Media Retrieval

These three tools let your AI client search vast archives, pull specific assets, and track the latest global wildlife uploads.

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Get Asset

Retrieves all scientific and technical metadata for one specific media asset using its ML Catalog Number.

Get Recent Media

Fetches the most recently uploaded wildlife content, optionally filtered by a...

Search Media

Searches the library for assets based on criteria like species name, location...

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Macaulay Library integration is available immediately — no restart needed.

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Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Sifting through scientific archives takes days of manual work., Solved with Vinkius AI Gateway

Today, getting a comprehensive view of bird media means juggling multiple academic databases. You download zip files from one source for species data, then manually check another site for location context, and finally try to cross-reference everything in a spreadsheet. It's slow, error-prone, and you always miss something.

With the Macaulay Library MCP Server, your agent runs `search_media` across millions of records instantly. You specify 'Species X' and 'Region Y,' and it compiles all relevant assets—photos, audio, video—into one structured output. It saves you days of tedious API calling.

The Macaulay Library MCP Server: Get validated media insights.

Manual processes require you to track down the unique ML Catalog Number for every asset, then use a separate tool just to check its metadata. You're bottlenecked by retrieving one single piece of information at a time, slowing your research dramatically.

Now, you pass the ID to `get_asset` and instantly pull all associated data points—technical specs, recording details, scientific context—in one go. The process is immediate, reliable, and structured.

What your AI can actually do with this

Macaulay Library MCP Server connects your AI client straight into Cornell Lab of Ornithology's massive scientific media archive. You're looking at millions of photos, audio recordings, and videos covering global wildlife—from birds to other creatures. This isn't just a search index; it's deep, structured scientific data that lets you do some serious research work with your agent.

Searching the Archive: search_media

Need to pull assets based on specific criteria? You tell the server what you need—it handles the filtering. You can search across species names (using taxon codes), pinpoint locations, track a particular contributor, or filter by media type like photo, audio, or video. If your project requires tracking all footage of one bird family from South America over the last decade, this tool is how you pull that massive dataset together.

Getting Deep Details: get_asset

When you find an asset—a perfect recording or a crucial photo—you don't just want to know it exists; you need all its technical and scientific background. By providing the unique ML Catalog Number, this tool retrieves every piece of metadata attached to that single media file. You get deep details on how it was captured, what taxonomists analyzed it, and any specific technical measurements associated with it.

It’s your way to verifying the provenance and context for critical research assets.

Tracking Global Changes: get_recent_media

Monitoring global wildlife activity is a whole other ballgame. This tool lets you pull the absolute newest content that's been uploaded into the archive. You can track this feed globally, or you can narrow it down to just one country or specific region code if your research is hyper-focused. You get real-time insight into what scientists are submitting right now, letting you monitor global or regional activity feeds as they happen.


How These Tools Work Together

Your AI client uses these tools to make sense of the sheer volume of data. If you're working on a project comparing migratory patterns, your agent might first use search_media to find all relevant videos from a specific region and species over a ten-year span. Then, for each promising video found that meets those criteria, it uses get_asset to pull the detailed scientific metadata—checking out everything from recording equipment specs to associated research notes.

If you suspect there's new data coming in on a particular coast, your agent can use get_recent_media, filtering by the appropriate country code, pulling the latest uploads for immediate review.

This setup lets you build complex query chains. You don't just get a list of files; you get structured, actionable intelligence about wildlife media. It’s built for serious ornithological research, giving your agent access to one of the most comprehensive scientific archives in the world. Don't settle for simple keyword searches—you're getting full-stack metadata retrieval across millions of assets.

Built · Hosted · Managed by Vinkius Macaulay Library MCP Server - Wildlife Media Archive
Server ID 019e5d32-2909-7235-bbe3-a70c79aeb876
Vinkius Inspector
Compliance Grade D
Score 59.84/100
Vinkius Inspector Badge — Score 59.84/100

Questions you might have

How do I find media for a specific bird species using search_media? +

You use search_media and provide the species' taxon code as one of the required parameters. This is much more accurate than searching by common name alone.

What does get_asset need to run? +

It requires a single input: the ML Catalog Number. Once you have this unique ID, it pulls all available metadata for that specific piece of media.

Can I check new uploads from Brazil using get_recent_media? +

Yes. You pass 'BR' (or the region code) to get_recent_media. This filters the latest global feed down to just what was recently uploaded in that country.

Is search_media better than get_asset? +

search_media is for discovery—when you don't know the ID yet. get_asset is for verification and detail—when you already have the specific ML Catalog Number.

Before I run `search_media`, what kind of API token do I need to connect? +

You must use an eBird API Token. This token authenticates your request and grants your agent read access to the live Macaulay data feed, ensuring you can query all three tools successfully.

If I run `get_asset` with a Catalog Number that doesn't exist, what error should I expect? +

You will receive an HTTP 404 or similar 'Asset Not Found' status. This specific error means the ID is incorrect or the data was removed; it confirms your connection to the server works fine.

Can I use `search_media` to filter results by both species and a specific date range? +

Yes, you pass multiple filters into the search query. You specify the taxon code and provide start/end dates in the parameters, narrowing down the assets significantly for targeted research.

When I use `get_recent_media`, how do I get all results if there are thousands of uploads? +

The function supports pagination using a cursor or offset parameter. You must pass this identifier with subsequent calls to fetch the next chunk of media until the API returns an empty set.

Can I filter my media search to only show audio recordings for a specific species? +

Yes! Use the search_media tool and set the mediaType parameter to 'a' (audio) along with the taxonCode for the species you are interested in.

How do I find the most recent media uploads from a specific country like Canada? +

You can use the get_recent_media tool and provide the regionCode (e.g., 'CA' for Canada) to fetch the latest submissions from that area.

What information can I get if I have a specific ML Catalog Number? +

By using the get_asset tool with the mlCatalogNumber, you will receive detailed metadata including species identification, location, date, contributor details, and technical specifications of the media.

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