Genomic Intelligence is an MCP server with 15 tools an agent can call, among them fetch_ensembl_sequence, fetch_gene_for_expression, fetch_region. It answers at https://mcp.genomicintelligence.ai/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 10 of 10 times since 4 September 2026; the last check, on 17 October 2026, succeeded.
Hosted DNA language models: promoter, splice, enhancer, chromatin, expression, annotation
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
fetch_ensembl_sequence | Fetch a gene's reference sequence from Ensembl and store it. Returns a handle ({ref, name, length, preview, ...}). Pass the `ref` to predict_* tools — the bases stay server-side. For expression, use fetch_gene_for_expression instead (it pre Inputs: gene, species, flank_bp |
fetch_gene_for_expression | Fetch a gene's sequence prepared for expression prediction. Resolves the gene's TSS via Ensembl and returns the exact TSS-centred 9,198 bp window the expression model scores, as a handle to pass to predict_expression(sequence_ref=...). Beca Inputs: gene, species |
fetch_region | Fetch a genomic region by coordinates from Ensembl and store it. For "find the genes in chr8:127,680,000-127,800,000"-style requests: resolves a coordinate range to reference sequence and returns a handle ({ref, name, length, ...}) to pass Inputs: region, species, strand, flank_bp |
find_genes | Find genes (transcript intervals) in a genomic region (async, ~8-25s). Takes 1,000–500,000 bp. The floor is the strictest of the scanning tasks: gene finding needs a region, not a site. (Only expression's 9,198 bp is higher, and that is a f Inputs: sequence, sequence_ref, sequence_name, model, wait |
find_genes_and_predict_expression | Find genes in a sequence, then predict each gene's expression (composite). Server-side chaining in ONE call: finds genes (transcript intervals, with their TSS) in the sequence, then predicts expression off each discovered TSS in the given e Inputs: sequence, sequence_ref, description, sequence_name, wait |
get_job | Poll an async job once. Returns the {data, meta} result if complete, a progress envelope if still running, or an error envelope if it failed. Inputs: job_id |
list_jobs | List the caller's recent async jobs (also available as gi://jobs/recent). Inputs: limit |
list_models | List available models for a task. Use to discover model ids before passing one as the `model` argument to a predict tool. The same catalog is also available as the resource `gi://models`. Returns a FLAT object — {task, default_model, models Inputs: task |
load_demo_sequence | Load a bundled demo reference sequence and return a handle. The server ships one curated, task-correct positive control per task (list them via the gi://sequences resource) — e.g. `expression_hbb_k562` is a ready-to-use K562 expression wind Inputs: name |
predict_chromatin | Chromatin annotation across 919 features (G0 DeepSEA). 200–500,000 bp. The model reads a 1,000 bp context window; 200–999 bp is accepted and scored against a padded window. Inputs: sequence, sequence_ref, sequence_name, model |
predict_enhancer | Predict enhancer activity (G0 DeepSTARR). 50–500,000 bp. 50 bp is the task's admission floor (the API 422s below it), not a statement about what the model reads: enhancer models score a 249 bp context window, so 50–248 bp is accepted and sc Inputs: sequence, sequence_ref, sequence_name, model |
predict_expression | Predict a gene's expression from a TSS-centred window. Expression is cell-type-specific, so `description` (cell type / assay context, e.g. 'K562 cell line') is REQUIRED — the API rejects requests without it. The model scores exactly 9,198 b Inputs: sequence, sequence_ref, description, tss_index, sequence_name, model |
predict_promoter | Predict promoter regions (G0). 300–500,000 bp. Returns the {data, meta} envelope: data.regions lists predicted promoters with start/end/score. 300 bp is the task floor for every promoter model. The default g0-promoter-2000bp scans a 2,000 b Inputs: sequence, sequence_ref, sequence_name, model |
predict_splice | Predict splice donor/acceptor sites (G0 BigBird). 100–500,000 bp. The model reads a 15,000 bp context window, so anything shorter is scored against a padded window — feed a whole transcript locus when you can. It is also strand-specific, an Inputs: sequence, sequence_ref, sequence_name, model |
store_inline_sequence | Store a human-pasted sequence and return a handle to re-use it. For a sequence you've already pasted into the conversation, this gives back a short handle so you can run several tasks on it without re-pasting the bases in each predict_* cal Inputs: sequence, name |
Every block below points at https://mcp.genomicintelligence.ai/mcp. Pick your client and copy.
claude mcp add --transport http genomic-intelligence https://mcp.genomicintelligence.ai/mcp
{
"mcpServers": {
"genomic-intelligence": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://mcp.genomicintelligence.ai/mcp"]
}
}
}{
"mcpServers": {
"genomic-intelligence": {
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"servers": {
"genomic-intelligence": {
"type": "http",
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"context_servers": {
"genomic-intelligence": {
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"mcpServers": {
"genomic-intelligence": {
"serverUrl": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"mcpServers": {
"genomic-intelligence": {
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}name: Genomic Intelligence
version: 0.0.1
schema: v1
mcpServers:
- name: Genomic Intelligence
type: streamable-http
url: https://mcp.genomicintelligence.ai/mcp{
"mcpServers": {
"genomic-intelligence": {
"httpUrl": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"mcpServers": {
"genomic-intelligence": {
"type": "http",
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}{
"mcpServers": {
"genomic-intelligence": {
"url": "https://mcp.genomicintelligence.ai/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fmcp.genomicintelligence.ai%2Fmcp&type=streamable_http&id=genomic-intelligence&name=Genomic%20Intelligence
It answered our last check on 17 October 2026 in 536 ms. Over 10 checks since 4 September 2026 it answered 10. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by genomicintelligence.ai and served from mcp.genomicintelligence.ai, the vendor's own domain: https://mcp.genomicintelligence.ai/mcp.
15, as returned by the server's own tools/list on 7 September 2026: fetch_ensembl_sequence, fetch_gene_for_expression, fetch_region, find_genes, find_genes_and_predict_expression, get_job, list_jobs, list_models, and 7 more.
It answered our last check on 17 October 2026 in 536 ms. Over 10 checks since 4 September 2026 it answered 10.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://mcp.genomicintelligence.ai/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: ai.genomicintelligence. Endpoint host: mcp.genomicintelligence.ai. ARD identifier: urn:air:ai.genomicintelligence:mcp:genomic-intelligence.
First indexed 31 August 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.