FishBase MCP for AI. Query Species Names and Database Schemas
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
FishBase connects your AI agent to global marine biology databases, giving you access to the world's largest collection of fish species data and documentation.
Query common names for thousands of species, track database versions over time, or check the API health status—all from one place.
It handles both FishBase and SeaLifeBase schemas so you don't have to worry about which source you need.
What AI agents can do with FishBase Automation
List comnames
Accesses common names data for fishes, supporting filtering and pagination across species.
Get docs by table
Pulls specific technical documentation and schema details for a single, named database table.
Get docs
Retrieves general metadata about the entire FishBase API documentation set.
It searches the databases and lists regional, common names associated with a given scientific species name.
It runs a simple check to confirm that the entire database connection is currently online and operational.
It lists all historical or current versions of the underlying FishBase/SeaLifeBase databases for tracking purposes.
It pulls metadata, explaining exactly what fields and columns exist within a specific database table.
Ask an AI about this
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What AI agents can do with FishBase MCP: 5 Specialized Tools
These five tools give your agent granular control over the data process, letting you pull documentation metadata, current status, version history, or common names.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using FishBase on VinkiusList Comnames
Accesses common names data for fishes, supporting filtering and pagination across species.
Get Docs By Table
Pulls specific technical documentation and schema details for a single, named...
Get Docs
Retrieves general metadata about the entire FishBase API documentation set.
Get Heartbeat
Checks the real-time operational status of the FishBase API to confirm connectivity.
Get Versions
Lists all available database versions, allowing you to track historical data records...
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Make Your AI Do More
Start with FishBase, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FishBase. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Tracking marine life means managing messy metadata.
Today, getting comprehensive data on fish requires jumping between specialized databases and reading pages of technical documentation. You’re constantly copying column names or comparing versions across different academic sites just to confirm a single detail—a huge waste of time.
With this MCP, your agent handles the complexity. Instead of manually navigating schemas, you ask for what you need—be it common name lists via `list_comnames` or documentation on a specific table using `get_docs_by_table`. You get clean data results without touching an API key or worrying about which database system is active.
Getting Status and Versions with the FishBase MCP
Before running any major analysis, you have to manually check if the source data is live or if it’s an outdated snapshot. You might spend minutes checking API status or figuring out what versions are even available.
Now, your agent runs `get_heartbeat` for immediate uptime confirmation and `get_versions` to list all historical database points instantly. It's reliable, fast, and keeps you working on science, not infrastructure.
What your AI can actually do with this
This connection lets your AI agent pull deep scientific data directly from two major global fish databases: FishBase and SeaLifeBase. Instead of opening multiple web tabs or running complex database queries, you can simply ask your agent for information using natural language. You can get lists of common species names across different regions, verify which version of the data is most current, or pull the technical documentation required to understand a specific table's structure.
It’s built to handle both primary databases through one interface. If you use Vinkius as your catalog, your agent connects once and gains immediate access to all this ichthyological knowledge. It means researchers can quickly verify species records or data scientists can map out schemas without ever leaving their workflow.
019e5d19-d70d-725c-a3ac-df62fe56d913 Here's how it actually works
The bottom line is you talk to your agent, and it handles all the database connections and data retrieval steps automatically.
Subscribe to this MCP on Vinkius. You'll need your FishBase API Key if the provider requires it.
Your AI agent connects and confirms the connection status using one of the specialized tools.
You then prompt your agent with a query, like listing common names or asking for documentation on a specific table.
Who is this actually for?
Marine biologists and academic researchers who need verifiable species data. If you're a developer building scientific tools or an educator creating lesson plans for taxonomy, this connection saves hours of manual data verification.
Verifying common names and taxonomic details across different regions to ensure species identification is accurate.
Mapping out the schema of large biological datasets, using tools like get_docs_by_table to understand data structure before analysis.
Accessing reliable, peer-reviewed species information for academic projects and educational materials.
What Changes When You Connect
Stop guessing which data source to use. The connection handles both FishBase and SeaLifeBase databases, so you only need one query regardless of the underlying schema.
Verify species names quickly without juggling multiple sites. Use list_comnames to fetch common regional names for any given scientific identifier.
Understand your dataset fully before analyzing it. Pull specific documentation using get_docs_by_table to map out a table's exact columns and metadata.
Track data history with confidence. The get_versions tool lets you know exactly which version of the database you’re working with, crucial for longitudinal studies.
Build reliable workflows that check status automatically. Use get_heartbeat to ensure your agent doesn't fail due to an unexpected API outage.
See it in action
Identifying a new species name for a paper
A marine biologist needs common names for Gadus morhua. Instead of visiting multiple sites, they ask their agent. The agent uses list_comnames and returns 'Cod', 'Morue', and 'Bacalao' immediately, saving research time.
Comparing schema across two projects
A data scientist needs to know the structure of tables in both FishBase and SeaLifeBase. They use get_docs_by_table twice, getting side-by-side documentation for 'species' metadata from both systems.
Debugging a historical dataset discrepancy
A researcher suspects data integrity issues in an old report. They first use get_versions to confirm the exact date range of the available records, then check the status with get_heartbeat before proceeding.
Building a reliable scientific application
A developer building a web app needs constant uptime assurance. They integrate get_heartbeat into their system's startup routine, ensuring the AI agent knows instantly if the primary data source is offline.
The honest tradeoffs
Treating documentation as a single block of text
Asking the agent vaguely for 'all fish information' without specifying what metadata or table you need. This leads to massive, unusable dumps.
Be specific. If you need schema details, use get_docs_by_table and name the exact table (e.g., 'species'). For general API info, start with get_docs.
Assuming all data is always current
Relying on a single dataset without verifying its age or version. This risks basing critical findings on outdated records.
Always check the available versions first using get_versions. If you need current status, run get_heartbeat before anything else.
Ignoring the dual database support
Treating FishBase and SeaLifeBase as two separate APIs that require entirely different connection methods or keys.
The MCP handles both. Your agent can switch between them using a simple parameter toggle within the query, keeping your workflow clean.
When It Fits, When It Doesn't
Use this MCP if your core task involves verifying scientific names, comparing taxonomy across different global databases (FishBase and SeaLifeBase), or mapping out complex biological data schemas. Specifically, use list_comnames when you only need common names for identification. Use get_docs_by_table when you know the table name but need to confirm its field structure. Don't use this if your goal is general search; you must specify what kind of data (e.g., versions, documentation) and which species or database you are querying. If you just want a quick status check, run get_heartbeat first.
Questions you might have
How do I check if the FishBase API is online using get_heartbeat? +
Run get_heartbeat. It immediately confirms the API's operational status. A successful heartbeat signal means your agent can proceed with data queries.
What is the difference between get_docs and get_docs_by_table? +
Use get_docs for overall metadata about the entire FishBase API structure. Use get_docs_by_table when you want specific, field-level documentation for one single database table.
Can list_comnames find common names for a species? +
Yes, list_comnames is designed to access and list regional common names associated with a scientific species name. It supports filtering and pagination too.
Which tool should I use if I want to know the data version? +
Use get_versions. This tool lists all available database versions, giving you control over which historical dataset your analysis references.
When using list_comnames, how do I ensure I'm pulling data from a specific source like SeaLifeBase? +
The query system handles switching between FishBase and SeaLifeBase sources. You don't need separate calls; you just specify your desired database context in the request parameters for list_comnames. This lets you verify species common names without needing to know which underlying source holds the most accurate record.
If I run list_comnames and get thousands of results, how do I handle filtering or pagination? +
You must use the provided parameters for chunking large result sets. The tool supports explicit pagination controls, letting you request data in manageable batches. You can also apply specific field filters to narrow down your common names list efficiently.
When I'm building a new analysis and need to understand the whole system structure, should I use get_docs or get_docs_by_table? +
Use get_docs first if you want an overview of the entire API's metadata. If you know exactly which table you need—like 'species'—you must run get_docs_by_table. The former provides context; the latter delivers specific schema details for one place.
What should I do if my initial attempt with any tool fails, and I suspect it’s a rate limit issue? +
Check the API status first using get_heartbeat. If that passes but subsequent calls fail, you've likely hit a usage cap. In that case, wait for your key refresh cycle or check documentation regarding bulk request limits.
Can I switch between FishBase and SeaLifeBase data? +
Yes. Most tools include a use_sealifebase parameter. Set it to true to query the SeaLifeBase database instead of the default FishBase.
How do I find the common names for a specific fish species? +
Use the list_comnames tool and provide the species name in the species parameter. You can also limit the results or filter specific fields.
Where can I find documentation for the database tables? +
You can use get_docs for general metadata or get_docs_by_table to get detailed documentation for a specific table name.
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