Hosted MCP Server

GI hosts a Model Context Protocol server (Streamable HTTP) at:

https://mcp.genomicintelligence.ai/mcp

It works keyless against a capped public demo quota, with no setup. An optional gi_ bearer key (GI_API_KEY) raises the quota. Prefer MCP on agent hosts that support it: the tools use agent-friendly, handle-based schemas so large sequences never enter the context.

The hosted server exposes 15 tools. Verify with tools/list rather than assuming; the list below is a point-in-time snapshot.

The handle-based flow

Acquire a sequence handle (sequence_ref), then predict against it.

1. Acquire (each returns a handle)

Tool	Required	Notes
`fetch_ensembl_sequence`	`gene`	Gene symbol or Ensembl ID (e.g. `"TP53"`). Also `species`, `flank_bp`. Not for coordinates.
`fetch_region`	`region`	Coordinate range, e.g. `"chr8:127,680,000-127,800,000"`. Also `species`, `strand`, `flank_bp`. Plus strand by default, which is what gene finding expects.
`fetch_gene_for_expression`	`gene`	Builds the TSS-centred 9,198 bp window `expression` needs. Also `species`.
`load_demo_sequence`	`name`	`name` is required. Valid names: `promoter_tp53`, `splice_hbb`, `enhancer_eve`, `chromatin_active_promoter_chr19`, `expression_hbb_k562`, `annotation_hbb_chr11`.
`store_inline_sequence`	`sequence`	Store an inline string; optional `name`.

There is no load_local_fasta on the hosted server — it only exists in local deployments. Over REST, read the file yourself.

2. Predict (pass the handle)

predict_promoter, predict_splice, predict_enhancer, predict_chromatin, predict_expression. Each takes sequence_ref or sequence (mutually exclusive), plus optional model and sequence_name.

predict_expression additionally needs description (cell type / assay, e.g. "K562 cells").

3. Gene finding (the annotation task on MCP)

There is no predict_annotation tool. The annotation task is surfaced as find_genes, which takes sequence_ref or sequence — not a region. Acquire a region handle with fetch_region first, then pass the handle.

find_genes runs async internally (~8-25 s). With wait=True (the default) it blocks and returns the result directly, never a job id. With wait=False it returns {data: {job_id, status}} to poll with get_job(job_id).

Composite

find_genes_and_predict_expression takes sequence_ref or sequence plus a required description. It has no region parameter — acquire a handle with fetch_region first. It finds genes in the sequence, then predicts expression off each discovered TSS. Use it whenever you want expression for a whole region: predict_expression cannot run on one, because it needs a single per-gene 9,198 bp window.

Jobs and discovery

get_job(job_id) (required job_id) and list_jobs — poll detached work.
list_models(task) — the model registry for a task. Do not invent model IDs, and do not hardcode a default; omit model and the server resolves it.

Resources

Reference context lives in MCP resources: gi://models, gi://docs/tasks, gi://sequences, gi://account. Read these instead of hardcoding model lists or bounds.

Small sequences

Small sequences may be passed inline via sequence on the predict_* tools, but the handle flow above is preferred to keep context small.

Worked example

# region -> handle -> genes -> expression per gene
h = fetch_region(region="chr11:5,225,000-5,235,000")
find_genes(sequence_ref=h.ref)
find_genes_and_predict_expression(sequence_ref=h.ref, description="K562 cells")

# gene -> handle -> promoter
g = fetch_ensembl_sequence(gene="TP53")
predict_promoter(sequence_ref=g.ref)

# keyless smoke test
d = load_demo_sequence(name="promoter_tp53")
predict_promoter(sequence_ref=d.ref)