Provides a custom node for n8n workflows that enables access to FishBase marine biology data, allowing users to search for species information, get ecological and morphological data, and validate species names within their automation workflows.
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Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP FishBase Serverwhat's the scientific name for Atlantic cod?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP FishBase Server
A Model Context Protocol (MCP) server that provides access to FishBase marine biology data, with n8n integration.
Features
Access to FishBase species data
Ecological information
Distribution/occurrence data
Morphological data
Species name validation
Common name to scientific name conversion
Related MCP server: Knowledge Base MCP Server
Installation
MCP Server
cd mcp_fishbase
npm install
npm run build
npm run startn8n Integration
cd n8n-integration
npm install
npm run buildThen install the node package in your n8n instance:
npm install /path/to/n8n-integrationUsage
MCP Tools Available
get_species: Get detailed species informationsearch_species: Search for species by nameget_ecology: Get ecological data for a speciesget_distribution: Get distribution/occurrence dataget_morphology: Get morphological datavalidate_species_name: Validate and suggest corrections for species namescommon_to_scientific: Convert common names to scientific nameslist_tables: List all available FishBase tables
Example Usage in n8n
Add the FishBase node to your workflow
Configure the operation (e.g., "Get Species Info")
Provide the species name (e.g., "Salmo trutta")
Connect to downstream nodes for data processing
Example Species Data
{
"SpecCode": 1,
"Genus": "Salmo",
"Species": "trutta",
"FBname": "Brown trout",
"Length": 100,
"CommonLength": 30,
"Weight": 17000
}Development
The MCP server uses mock data for development. In production, you would integrate with the actual FishBase HuggingFace datasets using a proper Parquet parser.
License
MIT
Resources
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