MCP 服务器
Genomic Intelligence
ai.genomicintelligence/genomic-intelligence
此 MCP 可以做什么
Provides hosted DNA language models for analyzing promoters, splice sites, enhancers, chromatin, expression, and genomic annotations.
功能
工具
15
工具
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 â the bases stay server-side. For
expression, use fetch_gene_for_expression instead (it prepares
the TSS-centred window that model needs).
可访问外部资源
输入模式
{'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.'}}}
输出模式
{'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.
可访问外部资源
输入模式
{'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.'}}}
输出模式
{'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_* â 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.
可访问外部资源
输入模式
{'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.'}}}
输出模式
{'type': 'object', 'title': 'fetch_regionDictOutput', 'additionalProperties': True}
find_genes
Find genes (transcript intervals) in a genomic region (async, ~8-25s).
Takes 1,000â500,000 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 â 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 â 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} â 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 â poll it with get_job.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'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 â 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
1,000â500,000 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} â it never returns a job_id on this path. With wait=False
(detached), returns {data: {job_id, status: 'submitted'}} immediately â
poll it with get_job. Because it ends in expression, `description`
(cell type / assay context) is REQUIRED.
只读
可访问外部资源
输入模式
{'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.'}}}
输出模式
{'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.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'type': 'object', 'title': 'get_jobDictOutput', 'additionalProperties': True}
list_jobs
List the caller's recent async jobs (also available as gi://jobs/recent).
只读
可访问外部资源
输入模式
{'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.'}}}
输出模式
{'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 â {task, default_model, models: [...]} â 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 â 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.
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'list_modelsArguments', 'required': ['task'], 'properties': {'task': {'type': 'string', 'title': 'Task', 'description': 'Task name. One of: promoter, splice, enhancer, chromatin, expression, annotation.'}}}
输出模式
{'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) â 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.
输入模式
{'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."}}}
输出模式
{'type': 'object', 'title': 'load_demo_sequenceDictOutput', 'additionalProperties': True}
predict_chromatin
Chromatin annotation across 919 features (G0 DeepSEA). 200â500,000 bp.
The model reads a 1,000 bp context window; 200â999 bp is accepted and
scored against a padded window.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'type': 'object', 'title': 'predict_chromatinDictOutput', 'additionalProperties': True}
predict_enhancer
Predict enhancer activity (G0 DeepSTARR). 50â500,000 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 50â248 bp is accepted and scored against a padded
window. For a meaningful call, submit at least the 249 bp context.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'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 â 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 â the midpoint is the only legal TSS.
- A longer locus, 9,198â500,000 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) â
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 â
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.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'type': 'object', 'title': 'predict_expressionDictOutput', 'additionalProperties': True}
predict_promoter
Predict promoter regions (G0). 300â500,000 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 â¥300 bp) sequence is still scored â 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.
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'type': 'object', 'title': 'predict_promoterDictOutput', 'additionalProperties': True}
predict_splice
Predict splice donor/acceptor sites (G0 BigBird). 100â500,000 bp.
The model reads a 15,000 bp context window, so anything shorter is
scored against a padded window â feed a whole transcript locus when you
can. It is also strand-specific, and the wrong strand fails silently and
plausibly â 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`).
只读
可访问外部资源
输入模式
{'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).'}}}
输出模式
{'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 â 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.)
输入模式
{'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.'}}}
输出模式
{'type': 'object', 'title': 'store_inline_sequenceDictOutput', 'additionalProperties': True}
历史
近期工具变更
已添加
list_jobs
2026年9月17日 07:56
已添加
get_job
2026年9月17日 07:56
已添加
find_genes_and_predict_expression
2026年9月17日 07:56
已添加
find_genes
2026年9月17日 07:56
已添加
predict_expression
2026年9月17日 07:56
已添加
predict_chromatin
2026年9月17日 07:56
已添加
predict_enhancer
2026年9月17日 07:56
已添加
predict_splice
2026年9月17日 07:56
已添加
predict_promoter
2026年9月17日 07:56
已添加
store_inline_sequence
2026年9月17日 07:56
已添加
load_demo_sequence
2026年9月17日 07:56
已添加
fetch_gene_for_expression
2026年9月17日 07:56
已添加
fetch_region
2026年9月17日 07:56
已添加
fetch_ensembl_sequence
2026年9月17日 07:56
已添加
list_models
2026年9月17日 07:56
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