Skip to main content
Glama

ensembl-mcp-server

by io.github.cyanheads

List Ensembl Species

ensembl_list_species
Read-onlyIdempotent

List species supported by Ensembl with display name, common name, assembly, taxon ID, and division. Required discovery step — species names like homo_sapiens are opaque to non-biologists and are the input format every other Ensembl tool expects. Filter by division to select one; use nameContains to find a species by partial name match. With no division, returns the endpoint default division — the vertebrates (~356 species on the default GRCh38 endpoint); pass a division to list that division.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
divisionNoFilter to a specific Ensembl division. EnsemblVertebrates includes human, mouse, zebrafish, and other vertebrates. EnsemblPlants covers crop and model plant genomes. EnsemblFungi, EnsemblMetazoa, EnsemblProtists cover non-vertebrate model organisms. Omit to return the endpoint default division (vertebrates).
nameContainsNoCase-insensitive substring filter applied locally after fetching. Matches against species name, display name, and common name. Example: "sapiens" matches homo_sapiens; "mouse" matches mus_musculus.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
noticeNoGuidance when the filter matches no species.
speciesNoSpecies matching the filter criteria, sorted by internal name.
totalCountNoTotal number of matching species after local filtering.

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already communicate read-only and idempotent behavior. The description adds genuinely useful behavioral details beyond that: the default division behavior when no division is passed, the division categories, and the fact that nameContains performs a local case-insensitive substring match. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient: three sentences cover purpose, workflow role, and filtering behavior. The only slight redundancy is repeating the default-division behavior already stated in the schema, but the rest of the content earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (2 optional params, 100% schema coverage, output schema present, read-only annotations), the description covers all essential decision points: what the tool returns, why it must be called first, and how to limit results by division or partial name. No meaningful gap remains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the input schema already fully explains both division and nameContains with examples. The description adds workflow context—'input format every other Ensembl tool expects'—which helps an agent understand why the parameters matter, but it does not materially change the semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'List species supported by Ensembl' and enumerates the returned fields (display name, common name, assembly, taxon ID, division). It also clearly separates this discovery/list tool from the sibling get/lookup/query/predict tools, which operate on specific species or regions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong contextual guidance, calling the tool a 'Required discovery step' and explaining that species names like homo_sapiens are the input format all other Ensembl tools expect. It also explains when to use division and nameContains. It does not explicitly name alternatives to avoid, but the discovery-step framing makes the place in the workflow clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ensembl-mcp-server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.5/5.0
Disambiguation5/5

Each tool targets a distinct genomic resource: homology, sequence, xrefs, species metadata, gene lookup, variant prediction, and region query. There is no overlap in their purposes, and the descriptions clearly delineate when to use each.

Naming Consistency5/5

All tool names follow the consistent pattern 'ensembl_' plus an action verb (get, list, lookup, predict, query) and a noun. The naming style is uniformly snake_case and lowercase, making the tool set predictable and easy to navigate.

Tool Count5/5

With 7 tools, the server is well-scoped for its purpose. Each tool addresses a core Ensembl query need without redundancy or bloat, fitting comfortably in the ideal range for a domain-specific toolkit.

Completeness5/5

The tool set covers the primary lifecycle of genomic investigation: discovering species, resolving gene identifiers, fetching sequences, obtaining cross-references, querying regions, finding homologs, and predicting variant consequences. There are no critical gaps for typical use cases.

Resources