# Genomic Intelligence MCP server Hosted DNA language models: promoter, splice, enhancer, chromatin, expression, annotation ## Links - Registry page: https://www.getdrio.com/mcp/ai-genomicintelligence-genomic-intelligence - Website: https://genomicintelligence.ai ## Install - Endpoint: https://mcp.genomicintelligence.ai/mcp - Auth: Auth required by registry metadata ## Setup notes - Remote header: Authorization (secret) - The upstream registry signals required auth or secrets. - Remote endpoint: https://mcp.genomicintelligence.ai/mcp - Header: Authorization ## Tools - 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`. Endpoint: https://mcp.genomicintelligence.ai/mcp - 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 prepares the TSS-centred window that model needs). Endpoint: https://mcp.genomicintelligence.ai/mcp - 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 to find_genes / predict_* — the bases stay server-side. Plus strand by default, which is what the gene-finder expects. For a gene by name use fetch_ensembl_sequence; for expression use fetch_gene_for_expression. Endpoint: https://mcp.genomicintelligence.ai/mcp - 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 window the expression model needs, as a handle to pass to predict_expression(sequence_ref=...). Endpoint: https://mcp.genomicintelligence.ai/mcp - 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 window for predict_expression. Stores the demo and returns a handle to pass to a predict_* tool: no Ensembl fetch, no quota. Handy for smoke-testing a prediction end-to-end. Endpoint: https://mcp.genomicintelligence.ai/mcp - 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_* call. Note that the full sequence still passes through the LLM on THIS call — it does not save context on its own. For large sequences, prefer fetch_ensembl_sequence / fetch_gene_for_expression / load_local_fasta, which acquire the bases server-side and never round-trip them. Endpoint: https://mcp.genomicintelligence.ai/mcp - predict_promoter - Predict promoter regions (G0). Up to 500,000 bp. Returns the {data, meta} envelope: data.regions lists predicted promoters with start/end/score. Endpoint: https://mcp.genomicintelligence.ai/mcp - predict_splice - Predict splice donor/acceptor sites (G0 BigBird). Up to 500,000 bp. Endpoint: https://mcp.genomicintelligence.ai/mcp - predict_enhancer - Predict enhancer activity (G0 DeepSTARR). Up to 500,000 bp. Endpoint: https://mcp.genomicintelligence.ai/mcp - predict_chromatin - Chromatin annotation across 919 features (G0 DeepSEA). Up to 500,000 bp. Endpoint: https://mcp.genomicintelligence.ai/mcp - predict_expression - Predict a gene's expression from a TSS-centred input 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. Requires exactly 9,198 bp centred on the TSS; call fetch_gene_for_expression(gene) to get a correctly-prepared handle. For a raw region or whole gene where you don't already have that window, use find_genes_and_predict_expression (it finds the genes for you). Endpoint: https://mcp.genomicintelligence.ai/mcp - find_genes - Find genes (transcript intervals) in a genomic region (async, ~8-25s). Gene-finding: detects transcript boundaries (TSS + PolyA) and returns one interval per predicted transcript — start/end, strand, a confidence score, and predicted TSS/PolyA positions (BED-style feature intervals, not free-text notes). Use this for "what genes are here", "find / locate genes", or "annotate this region". Each transcript also carries its type (mRNA/lnc_RNA) and internal exon/intron/CDS structure in `exons`/`introns`/`cds` arrays, plus a browser-ready GFF3 track in `data.formats.gff3`. To get each gene's *expression* from a raw region, use find_genes_and_predict_expression instead — expression needs a per-gene TSS window, so predict_expression cannot run on a whole region. Submits an async job internally. With wait=True (default), blocks and streams progress, then returns the result {data, meta} — it never returns a job_id on this path. (If a generous block ceiling is exceeded it returns a timeout error, not a job handle.) With wait=False (detached), returns {data: {job_id, status: 'submitted'}} immediately — poll it with get_job. Endpoint: https://mcp.genomicintelligence.ai/mcp - 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 experimental context. This is the right tool whenever you want expression for a raw region or sequence — e.g. "find the genes in chr8:… and predict their expression in K562". You cannot call predict_expression on a whole region, because it needs a single per-gene 9,198 bp TSS window; this tool handles that for you. Runs async internally at every size (the annotate stage is slow even for small inputs), so progress always streams. With wait=True (default), blocks and streams progress, then returns the result {data, meta} — it never returns a job_id on this path. With wait=False (detached), returns {data: {job_id, status: 'submitted'}} immediately — poll it with get_job. Because it ends in expression, `description` (cell type / assay context) is REQUIRED. Endpoint: https://mcp.genomicintelligence.ai/mcp - 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. Endpoint: https://mcp.genomicintelligence.ai/mcp - list_jobs - List the caller's recent async jobs (also available as gi://jobs/recent). Endpoint: https://mcp.genomicintelligence.ai/mcp ## Resources - gi://models - All models across the six tasks, with their bio specs. MIME type: application/json - gi://docs/tasks - What each of the six tasks does, sync/async, and length bounds. MIME type: application/json - gi://openapi.json - Live OpenAPI 3.0 spec for the /v1 API. MIME type: application/json - gi://sequences - Curated reference FASTAs bundled with the server (one positive control per task). Load one into a handle with load_demo_sequence. MIME type: application/json - gi://jobs/recent - The caller's recent async inference jobs and their status. MIME type: application/json - gi://account - Configured backend, health, and the tasks this server exposes. MIME type: application/json ## Prompts - gi-promoter-screen - Fetch a gene and scan it for promoters, reporting strong hits. Arguments: gene, species - gi-expression-screen - Predict expression for a gene from its TSS-centred window. Arguments: gene, species ## Metadata - Owner: ai.genomicintelligence - Version: 1.0.0 - Runtime: Streamable Http - Transports: HTTP - License: Not captured - Language: Not captured - Stars: Not captured - Updated: Jul 27, 2026 - Source: https://registry.modelcontextprotocol.io