Genomic Intelligence MCP server

• Registry nameai.genomicintelligence/genomic-intelligenceverified in the MCP registry

The registry name sits under genomicintelligence.ai. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.

• Stateanswerschecked 2026-10-20
• Tools15
• Server calls itselfgi-mcp
• Server version0.1.0a26
• Protocol version2025-06-18

On 2026-10-20 the server answered initialize and tools/list without a sign-in. First answer in 101 ms.

• Whether its tools do the product's job has not been judged yet.
• Checked on 2 of the 7 days it takes to judge reliability.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

• INITE MCP scoreafter a weeka score needs 7 daily checks: 2 so far
• Answeredmeasuring2 of the 7 daily checks it takes to print a share
• Median first answer1543 ms
• Space in an agent's context≈ 5,891 tokensthe definitions of all its tools (15), which an agent carries on every turn
• Tool definitionsA · 100 of 100
• Typed results15 of 15 toolsdeclared outputSchema: results come typed, read once

• Every tool passes all six checks.

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (30%), doing the product's job (25%), definitions (20%), context (10%) and use (15%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

• 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…

• 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…

• 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…

• 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…

• 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…

• 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_*…

• 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…

• 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…

• 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…

• 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…

• 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…

• 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…

• 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).