ai.genomicintelligence/genomic-intelligence · v1.0.0 · productivity
Genomic Intelligence is healthy: it completed an MCP handshake in 2625 ms and exposes 15 tools.
Hosted DNA language models: promoter, splice, enhancer, chromatin, expression, annotation
Endpoint: https://mcp.genomicintelligence.ai/mcp (streamable-http)
No repository listed · Website · JSON · Add to my inventory
| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 15 |
| Latency | 2625 ms |
| Last checked (UTC) | 2026-10-14T09:00:18.901Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-14T09:00:18.901Z | Healthy | 2625 |
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: [...]} — not the |
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 windo |
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 bas |
fetch_gene_for_expression | 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 |
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. |
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 seque |
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 bp context window, so a sho |
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, and the wrong strand fails silently a |
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 scored against a padded windo |
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. |
predict_expression | 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 |
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 the sequence around one TSS rather |
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 ri |
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. |
list_jobs | List the caller's recent async jobs (also available as gi://jobs/recent). |