MCP Radar
MCP Server

Genomic Intelligence

ai.genomicintelligence/genomic-intelligence
Science & Engineering Public & reachable MCP 2025-11-25

What this MCP does

Provides hosted DNA language models for analyzing promoters, splice sites, enhancers, chromatin, expression, and genomic annotations.

Capabilities

Tools

15 tools
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).
Open world
Input schema 
{'type': 'object', 'title': 'fetch_ensembl_sequenceArguments', 'required': ['gene'], 'properties': {'gene': {'type': 'string', 'title': 'Gene', 'description': "Gene symbol (e.g. 'TP53') or Ensembl ID."}, 'species': {'type': 'string', 'title': 'Species', 'default': 'human', 'description': "Species name, e.g. 'human', 'mouse'."}, 'flank_bp': {'type': 'integer', 'title': 'Flank Bp', 'default': 0, 'minimum': 0, 'description': 'Extra bp added on each side of the gene body.'}}}
Output schema 
{'type': 'object', 'title': 'fetch_ensembl_sequenceDictOutput', 'additionalProperties': True}
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 9,198 bp window the expression model scores, as a handle to pass to predict_expression(sequence_ref=...). Because the window is exactly 9,198 bp, no `tss_index` is needed on that call.
Open world
Input schema 
{'type': 'object', 'title': 'fetch_gene_for_expressionArguments', 'required': ['gene'], 'properties': {'gene': {'type': 'string', 'title': 'Gene', 'description': "Gene symbol (e.g. 'HBB')."}, 'species': {'type': 'string', 'title': 'Species', 'default': 'human', 'description': 'Species name.'}}}
Output schema 
{'type': 'object', 'title': 'fetch_gene_for_expressionDictOutput', 'additionalProperties': True}
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.
Open world
Input schema 
{'type': 'object', 'title': 'fetch_regionArguments', 'required': ['region'], 'properties': {'region': {'type': 'string', 'title': 'Region', 'description': "Genomic coordinates, e.g. 'chr8:127,680,000-127,800,000'. Commas, en/em dashes and '..' are accepted; the 'chr' prefix is optional."}, 'strand': {'type': 'integer', 'title': 'Strand', 'default': 1, 'description': '1 = plus (default), -1 = minus. find_genes (gene finding) is plus-oriented â\x80\x94 keep 1 for annotation; use -1 only for a strand-sensitive task on a known minus-strand locus.'}, 'species': {'type': 'string', 'title': 'Species', 'default': 'human', 'description': "Species name, e.g. 'human', 'mouse'."}, 'flank_bp': {'type': 'integer', 'title': 'Flank Bp', 'default': 0, 'minimum': 0, 'description': 'Extra bp added on each side of the region.'}}}
Output schema 
{'type': 'object', 'title': 'fetch_regionDictOutput', 'additionalProperties': True}
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 a fixed window rather than a minimum region size.) 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.
Read only Open world
Input schema 
{'type': 'object', 'title': 'find_genesArguments', 'properties': {'wait': {'type': 'boolean', 'title': 'Wait', 'default': True, 'description': 'Default True: block and stream progress until the result is ready. Set False for detached mode â\x80\x94 returns a job_id immediately to poll with get_job.'}, 'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'find_genesDictOutput', 'additionalProperties': True}
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". predict_expression scores ONE TSS window and needs you to know where that TSS is (either a pre-centred 9,198 bp window or a `tss_index`); this tool discovers every gene's TSS itself. It has no 9,198 bp floor and no tss_index; it starts with gene finding, so it takes 1,000–500,000 bp. 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.
Read only Open world
Input schema 
{'type': 'object', 'title': 'find_genes_and_predict_expressionArguments', 'properties': {'wait': {'type': 'boolean', 'title': 'Wait', 'default': True, 'description': 'Default True: block and stream progress until the result is ready. Set False for detached mode â\x80\x94 returns a job_id immediately to poll with get_job.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases, 1,000-500,000 bp (line breaks ignored). Mutually exclusive with sequence_ref.'}, 'description': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Description', 'default': None, 'description': "REQUIRED experimental context â\x80\x94 cell type / assay / conditions (e.g. 'K562 cell line'), applied to every found gene. The workflow ends in expression, which the API rejects without it."}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Stored sequence handle. Mutually exclusive with sequence.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back.'}}}
Output schema 
{'type': 'object', 'title': 'find_genes_and_predict_expressionDictOutput', 'additionalProperties': True}
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.
Read only Open world
Input schema 
{'type': 'object', 'title': 'get_jobArguments', 'required': ['job_id'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id', 'description': 'Job id from an async tool (find_genes, find_genes_and_predict_expression).'}}}
Output schema 
{'type': 'object', 'title': 'get_jobDictOutput', 'additionalProperties': True}
list_jobs
List the caller's recent async jobs (also available as gi://jobs/recent).
Read only Open world
Input schema 
{'type': 'object', 'title': 'list_jobsArguments', 'properties': {'limit': {'type': 'integer', 'title': 'Limit', 'default': 20, 'maximum': 100, 'minimum': 1, 'description': 'Max number of recent jobs to return.'}}}
Output schema 
{'type': 'object', 'title': 'list_jobsDictOutput', 'additionalProperties': True}
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 {data, meta} envelope the predict tools return. Each model carries a `bio_spec`, whose useful fields are `request_max_bp` (the enforced ceiling, 500,000 everywhere) and `context_window_bp` (what the model reads in one step — compare your sequence length against it: a shorter one is scored against a padded window). `trained_window_bp` is the fixed receptive field where there is no sliding window (9,198 for g0-expression). `request_max_bp` is the only one of the three that is a cap; the window fields describe what the model scores, not what the route accepts.
Read only Open world
Input schema 
{'type': 'object', 'title': 'list_modelsArguments', 'required': ['task'], 'properties': {'task': {'type': 'string', 'title': 'Task', 'description': 'Task name. One of: promoter, splice, enhancer, chromatin, expression, annotation.'}}}
Output schema 
{'type': 'object', 'title': 'list_modelsDictOutput', 'additionalProperties': True}
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.
Input schema 
{'type': 'object', 'title': 'load_demo_sequenceArguments', 'required': ['name'], 'properties': {'name': {'type': 'string', 'title': 'Name', 'description': "Demo name from gi://sequences, e.g. 'expression_hbb_k562', 'promoter_tp53', or 'annotation_hbb_chr11'. A gene token like 'TP53' also resolves."}}}
Output schema 
{'type': 'object', 'title': 'load_demo_sequenceDictOutput', 'additionalProperties': True}
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.
Read only Open world
Input schema 
{'type': 'object', 'title': 'predict_chromatinArguments', 'properties': {'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'predict_chromatinDictOutput', 'additionalProperties': True}
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 window. For a meaningful call, submit at least the 249 bp context.
Read only Open world
Input schema 
{'type': 'object', 'title': 'predict_enhancerArguments', 'properties': {'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'predict_enhancerDictOutput', 'additionalProperties': True}
predict_expression
Predict a gene's expression from a TSS-centred 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. The model scores exactly 9,198 bp centred on the TSS (±4,599). Two ways to supply that: - A sequence of exactly 9,198 bp already centred on the TSS. No `tss_index` needed — the midpoint is the only legal TSS. - A longer locus, 9,198–500,000 bp, plus `tss_index`: the 0-based offset of the TSS into it. The API cuts the window for you (sequence[tss_index-4599 : tss_index+4599]) and never scans for a TSS itself. Anything under 9,198 bp is rejected, here and by the API (422) — there is no padding or truncation fallback. `tss_index` is required for every other length, because a locus with no offset is indistinguishable from a mis-centred window. An offset that is merely WRONG (e.g. counted over a wrapped FASTA's characters, or against a chromosome coordinate instead of an offset into THIS sequence) still succeeds and scores the wrong window — verify meta.task_specific_counts.scored_window in the response. Easiest paths: fetch_gene_for_expression(gene) returns a ready-centred handle, and find_genes_and_predict_expression takes a raw region and finds each TSS for you.
Read only Open world
Input schema 
{'type': 'object', 'title': 'predict_expressionArguments', 'properties': {'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'tss_index': {'anyOf': [{'type': 'integer', 'minimum': 0}, {'type': 'null'}], 'title': 'Tss Index', 'default': None, 'description': '0-based offset of the transcription start site into the sequence, counted in bases (whitespace is ignored). Required unless the sequence is exactly 9,198 bp; must leave 4,599 bp on each side. The API scores only sequence[tss_index-4599 : tss_index+4599] and reports the slice it used as meta.task_specific_counts.scored_window â\x80\x94 check it: a wrong-but-in-range offset scores the wrong window silently.'}, 'description': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Description', 'default': None, 'description': "REQUIRED experimental context â\x80\x94 cell type / assay / conditions (e.g. 'K562 cell line', 'liver tissue'). Expression is cell-type-specific; the API rejects requests without it."}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'predict_expressionDictOutput', 'additionalProperties': True}
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 shorter (but ≥300 bp) sequence is still scored — against a window padded out to that size. Check the chosen model's bio_spec.context_window_bp via list_models to know whether it saw real sequence or padding.
Read only Open world
Input schema 
{'type': 'object', 'title': 'predict_promoterArguments', 'properties': {'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'predict_promoterDictOutput', 'additionalProperties': True}
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 and plausibly — it returns sites at different positions, often still scoring above 0.9, not the near-zero scores once documented here. Nothing in the response flags it, so submit the transcript's own orientation (fetch_region takes `strand`).
Read only Open world
Input schema 
{'type': 'object', 'title': 'predict_spliceArguments', 'properties': {'model': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Model', 'default': None, 'description': 'Optional model id; omit for the task default. See list_models.'}, 'sequence': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence', 'default': None, 'description': 'DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.'}, 'sequence_ref': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sequence Ref', 'default': None, 'description': 'Handle (seq_â\x80¦) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.'}, 'sequence_name': {'type': 'string', 'title': 'Sequence Name', 'default': 'sequence', 'description': 'Label echoed back in the response (ignored when `sequence_ref` is used).'}}}
Output schema 
{'type': 'object', 'title': 'predict_spliceDictOutput', 'additionalProperties': True}
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. A line-wrapped FASTA *body* may be pasted verbatim: whitespace is stripped before storing, so the handle's `length` counts bases and a later `tss_index` counts into the same string the API measures. (A FASTA `>` header line is not a sequence and is rejected by the API's alphabet check.)
Input schema 
{'type': 'object', 'title': 'store_inline_sequenceArguments', 'required': ['sequence'], 'properties': {'name': {'type': 'string', 'title': 'Name', 'default': 'sequence', 'description': 'Label for this sequence.'}, 'sequence': {'type': 'string', 'title': 'Sequence', 'description': 'DNA bases to store and get a handle for. Line breaks are fine â\x80\x94 whitespace is stripped, so the handle holds bases.'}}}
Output schema 
{'type': 'object', 'title': 'store_inline_sequenceDictOutput', 'additionalProperties': True}
History

Recent tool changes

Added
list_jobs
Sept. 17, 2026, 7:56 a.m.
Added
get_job
Sept. 17, 2026, 7:56 a.m.
Added
find_genes_and_predict_expression
Sept. 17, 2026, 7:56 a.m.
Added
find_genes
Sept. 17, 2026, 7:56 a.m.
Added
predict_expression
Sept. 17, 2026, 7:56 a.m.
Added
predict_chromatin
Sept. 17, 2026, 7:56 a.m.
Added
predict_enhancer
Sept. 17, 2026, 7:56 a.m.
Added
predict_splice
Sept. 17, 2026, 7:56 a.m.
Added
predict_promoter
Sept. 17, 2026, 7:56 a.m.
Added
store_inline_sequence
Sept. 17, 2026, 7:56 a.m.
Added
load_demo_sequence
Sept. 17, 2026, 7:56 a.m.
Added
fetch_gene_for_expression
Sept. 17, 2026, 7:56 a.m.
Added
fetch_region
Sept. 17, 2026, 7:56 a.m.
Added
fetch_ensembl_sequence
Sept. 17, 2026, 7:56 a.m.
Added
list_models
Sept. 17, 2026, 7:56 a.m.
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