Ensembl MCP for AI. Analyze any genomic data in plain conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Ensembl MCP gives you direct access to vast genomic data, letting your AI client pull gene trees, alignments, homologies, and cross-references from the Ensembl database in natural conversation.
What your AI can do
Get alignment
Pulls full genomic sequence alignments for a defined region.
Get archive bulk
Finds the newest version of multiple identifiers at once.
Get archive id
Determines the current stable version for a single identifier.
Retrieve complete gene trees and homology data for specific genes or species.
Determine the functional consequences of genetic variants using standardized notation like HGVS.
Convert genomic or cDNA coordinates when switching between different assembly versions.
Link an Ensembl object to external databases using common symbols like BRCA2.
Get genomic alignments or calculate linkage disequilibrium across specific DNA sequences.
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Ensembl: 27 Bioinformatics Tools
These tools allow your agent to perform highly specific genomic tasks, ranging from sequence alignment (`get_alignment`) to finding related genes (`get_homology`).
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Start using Ensembl on VinkiusGet Alignment
Pulls full genomic sequence alignments for a defined region.
Get Archive Bulk
Finds the newest version of multiple identifiers at once.
Get Archive Id
Determines the current stable version for a single identifier.
Get Ga4gh Beacon
Provides allele information using the GA4GH beacon service.
Search Ga4gh Variants
Searches for genetic variants using the standardized GA4GH schema.
Get Genetree
Retrieves a visual gene tree structure for a specific identifier.
Get Homology
Gets information about genes that share common ancestry across different species.
Get Info Assembly
Lists all available chromosome assemblies for a given organism.
Get Info Rest
Shows the current version details of the Ensembl REST API.
Get Info Species
Lists every available species and their associated metadata within the database.
Get Ld
Calculates Linkage Disequilibrium (LD) values for a region of DNA.
Get Lookup Bulk
Performs bulk lookups to find information for many identifiers simultaneously.
Get Lookup Id
Finds the corresponding species and database type for a single identifier.
Get Map Cdna
Translates cDNA sequence coordinates back into genomic coordinates.
Get Map
Converts coordinates from one assembly version to another format.
Get Ontology Id
Searches for a specific term using its ontological identifier.
Get Overlap Region
Identifies features that overlap a defined genomic region.
Ping
Checks if the entire data service is currently operational.
Get Sequence Id
Requests the full DNA sequence based on a stable identifier.
Get Sequence Region
Retrieves a segment of genomic DNA by specifying start and end points.
Get Taxonomy Id
Searches for biological classification terms using either an ID or name.
Get Variation
Fetches details about genetic variants, including population data and genotypes.
Get Vep Bulk
Predicts the functional consequences for many DNA regions at once.
Get Vep Hgvs
Determines variant consequences using standardized HGVS nomenclature.
Get Vep Id
Predicts variant effects when given a recognized ID like an rsID.
Get Xrefs Id
Pulls external reference links for any Ensembl identifier.
Get Xrefs Symbol
Looks up a common gene symbol and returns all linked Ensembl objects.
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Make Your AI Do More
Start with Ensembl, 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 Ensembl. 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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Works with 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 27 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Sifting through different database versions is a nightmare.
If you're working on genomics, you know the pain: an experiment runs fine using coordinates from 'Assembly V2', but when you try to submit that data today, the modern database rejects it because the map changed. You end up spending hours figuring out which coordinate system is correct and manually adjusting every single number in your dataset.
With this MCP, you just ask your agent to convert those old coordinates using `get_map`. The process handles all the complex assembly mapping behind the scenes. You get clean, usable data formatted for today's standards, no manual math required.
Getting gene relationships via homology and trees
Before this MCP, finding orthologs—the genes in different species that share a common ancestor—meant running specialized scripts for every pair of species you cared about. It was slow, error-prone, and required deep coding knowledge just to get a list.
Now, your agent uses `get_homology` or `get_genetree`. You ask the question in plain English: 'What are the orthologs for gene X?' The MCP delivers the full tree structure and related data instantly. It’s that simple.
What your AI can actually do with this
Think of this as bypassing weeks of scripting. Instead of writing custom Python or R code just to compare genes across species, you talk to the MCP. You can ask for everything from finding all orthologs (the genes that evolved from a common ancestor) to checking if two different identifiers refer to the same thing.
It’s like having a senior bioinformatician sitting next to you who knows every corner of the Ensembl database and can pull up any data point instantly, whether it's calculating linkage disequilibrium or just listing available species. When you connect this MCP through Vinkius, your AI client handles all the complex API calls—you just ask what you need.
It’s pure biological querying without the boilerplate code.
019e5d16-5228-723f-8044-258a3ef9aed1 Here's how it actually works
The bottom line is you get complex genomic reports without writing a single script.
First, you connect your preferred AI client to this MCP on the Vinkius Marketplace and supply the necessary API configuration.
Next, you simply ask your agent a natural language question, like 'What are the homologies for gene X in species Y?'
The MCP translates that request into multiple specific database calls, gathers the data (e.g., getting alignments or checking variant features), and hands back the clean result to your agent.
Who is this actually for?
This MCP is built for computational biologists, genetic researchers, and data scientists who spend too much time writing repetitive code just to pull basic biological facts. You're the person frustrated by manually hopping between databases and constantly rewriting boilerplate API calls.
Runs comparative genomics analyses, pulling gene trees or checking for orthologs across multiple species.
Verifies stable ID versions and cross-references symbols directly from their research environment to ensure data integrity.
Automates the retrieval of genomic alignments and metadata for large-scale biological analysis pipelines.
What Changes When You Connect
Stop manually writing scripts for comparative genomics. With get_homology and get_genetree, you simply ask your agent to find gene trees or orthologs, getting the results instantly without needing custom R code.
Save time validating data integrity. Instead of guessing if an identifier is current, use get_archive_id or get_lookup_bulk to reliably check the latest version and species for any ID you encounter.
Handle complex variant analysis efficiently. Need to know what a mutation does? Use get_vep_hgvs, which takes standardized HGVS notation and immediately gives you the predicted biological consequence, like an amino acid change.
Cross-reference data without effort. If you have a gene symbol (e.g., BRCA2), use get_xrefs_symbol to pull all linked Ensembl objects across different databases in one go.
Process massive datasets faster than ever before. Tools like get_vep_bulk let you predict variant effects for multiple regions simultaneously, which is a huge time saver for large-scale analyses.
See it in action
Comparing genes between human and mouse
A researcher needs to find all orthologs for a specific human gene in the mouse genome. They ask their agent, which uses get_homology, and immediately get a list of high-confidence matches, including necessary alignment details.
Mapping coordinates across assembly updates
A data scientist has genomic coordinates from an older dataset but needs to run analysis against the latest build. They use get_map to convert those old coordinates into the current, accurate format for their pipeline.
Investigating a novel variant
A clinician identifies a rare variant using an rsID (e.g., rs12345). They prompt their agent with get_vep_id, and the MCP returns a detailed report on the predicted functional impact of that specific mutation.
Getting a full picture of a gene
A student needs to understand every facet of a gene. They ask their agent, which uses get_xrefs_id, and it pulls not only the sequence but also external links connecting that gene to other databases.
The honest tradeoffs
Writing basic lookup scripts
Manually writing a Python script using multiple API endpoints just to check if an ID exists and what its species is. This takes hours of setup.
Instead, let your agent run get_lookup_id. You ask the question in plain English, and it handles all the necessary lookups, giving you the result immediately.
Handling coordinates manually
Getting frustrated because coordinates used in an old paper don't match the current assembly build, requiring multiple manual conversion steps.
Use get_map or get_map_cdna. You just tell your agent what you need to convert, and it handles the complex mapping between assemblies.
Missing variant context
Running a query on a variant ID but not knowing if the database supports HGVS or rsID format, leading to failed queries.
If you need consequence prediction, use get_vep_hgvs for notation-based analysis, or get_vep_id when given a standard identifier like an rsID.
When It Fits, When It Doesn't
Use this MCP if your research involves comparative biology, sequence mapping, or variant consequence prediction. If you need to know how genes relate across species (homology/gene trees) or map coordinates between different assembly versions, this is the right tool. Don't use it if you only need general biological definitions; those might be better served by a taxonomy lookup service. Likewise, don't try to build an entire pipeline using just one tool—the power comes from letting your agent orchestrate calls across get_homology, get_vep_hgvs, and get_xrefs_symbol seamlessly.
Questions you might have
How do I check if an identifier is current using get_archive_id? +
Run get_archive_id with the specific Ensembl identifier you have. The MCP will return the latest stable version number and confirm its status within the most recent assembly.
What's the difference between get_homology and get_genetree? +
get_homology provides detailed information about related genes (orthologs/paralogs). get_genetree, however, outputs the actual tree structure showing how those genes are related evolutionarily.
Can I find external IDs using get_xrefs_symbol? +
Yes. If you provide a common gene symbol (like BRCA2), get_xrefs_symbol will look up all the linked Ensembl objects and return cross-references to other databases.
How do I predict variant effects with get_vep_hgvs? +
You provide the variant in HGVS notation (e.g., NM_00123:c.123A>G). The MCP uses get_vep_hgvs to run a consequence prediction, telling you if it's a missense mutation or something else.
What is the purpose of get_lookup_bulk? +
get_lookup_bulk lets you check multiple identifiers at once. Instead of running individual lookups for 20 genes, you input all 20 and get a comprehensive report back.
How do I check if the Ensembl API is running correctly using ping? +
The ping tool confirms immediate service availability. It simply sends a health check request to verify that the MCP connection and the underlying Ensembl REST API are online and accepting commands.
What is the difference between getting coordinates with get_map versus using get_map_cdna? +
Use get_map when you need to convert genomic coordinates from one assembly version to a different one. Meanwhile, get_map_cdna specifically handles converting cDNA sequences into their corresponding genomic locations.
How do I get metadata for all available species using get_info_species? +
The get_info_species tool lists every organism supported by the Ensembl database. This allows you to identify which taxa are available before attempting specific analyses like homology lookups.
How can I find orthologs for a specific gene across different species? +
Use the get_homology tool by providing the species name and the Ensembl gene ID. You can filter by type (e.g., 'orthologues') to see related genes in other organisms.
Can I retrieve the evolutionary gene tree for a specific identifier? +
Yes! The get_genetree tool allows you to fetch the gene tree for any stable Ensembl ID, with options for alignment and sequence types (protein or cdna).
How do I map a common gene symbol like 'BRCA2' to its Ensembl ID? +
Use the get_xrefs_symbol tool. Provide the species (e.g., 'human') and the symbol 'BRCA2' to retrieve all linked Ensembl objects and their stable identifiers.
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