MCP server

Genomic Intelligence Official

ai.genomicintelligence/genomic-intelligence

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Hosted DNA language models: promoter, splice, enhancer, chromatin, expression, annotation

Website

At a glance

Type

MCP server

Publisher

genomicintelligence.ai (verified by the MCP Registry)

Status

Working at last check

Tools

15

Token cost

About 6,698 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http genomic-intelligence https://mcp.genomicintelligence.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.genomic-intelligence]
url = "https://mcp.genomicintelligence.ai/mcp"

Gemini CLI

{"mcpServers":{"genomic-intelligence":{"httpUrl":"https://mcp.genomicintelligence.ai/mcp"}}}

Cursor

{"mcpServers":{"genomic-intelligence":{"url":"https://mcp.genomicintelligence.ai/mcp"}}}

VS Code

{"servers":{"genomic-intelligence":{"type":"http","url":"https://mcp.genomicintelligence.ai/mcp"}}}

Antigravity

agy mcp add genomic-intelligence https://mcp.genomicintelligence.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"genomic-intelligence","server_url":"https://mcp.genomicintelligence.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"genomic-intelligence","server_url":"https://mcp.genomicintelligence.ai/mcp"}

Tools

• fetch_ensembl_sequence ~242 tokens
  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 prepares the TSS-centred windo
• fetch_gene_for_expression ~418 tokens
  Fetch a gene's sequence prepared for expression prediction. Resolves the gene's canonical-transcript TSS via Ensembl and stores a gene-sense sequence centred on it, as a handle to pass to predict_expression(sequence_ref=...). Longer TSS-centred sequence gives better
• fetch_region ~385 tokens
  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 to find_genes / predict_* — the bas
• find_genes ~813 tokens
  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 the sequence around one TSS rather
• find_genes_and_predict_expression ~720 tokens
  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 experimental context. This is the ri
• get_job ~126 tokens
  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.
• list_jobs ~92 tokens
  List the caller's recent async jobs (also available as gi://jobs/recent).
• list_models ~408 tokens
  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: [...]} — not the
• load_demo_sequence ~236 tokens
  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 window for predict_expression.
• predict_chromatin ~363 tokens
  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.
• predict_enhancer ~412 tokens
  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 scored against a padded windo
• predict_expression ~1159 tokens
  Predict a gene's expression from the sequence around its TSS. 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 input rule is the same for every expression mod
• predict_promoter ~461 tokens
  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 bp context window, so a sho
• predict_splice ~472 tokens
  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, and the wrong strand fails silently a
• store_inline_sequence ~391 tokens
  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_* call. Note that the full seque

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