Genomic Intelligence

Hosted DNA language models: promoter, splice, enhancer, chromatin, expression, annotation

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质量与安全性

A
描述质量
100%
模式完整度
88%
命名质量
91%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

发现(1)

  • LOWTool 'find_genes_and_predict_expression' name length outside 3-30 range在 find_genes_and_predict_expression 中

基于对工具定义和协议合规性的自动分析。

上下文开销

~5,743token 数(工具定义)
~2.4 KB典型响应大小
对注意力有显著影响(占 128k 上下文窗口的 4.49%)

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高,可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中:

{
  "mcpServers": {
    "genomic-intelligence": {
      "url": "https://mcp.genomicintelligence.ai/mcp"
    }
  }
}

远程端点

https://mcp.genomicintelligence.ai/mcpstreamable-http

它能做什么

工具清单

工具(15)

🟢 只读🟡 写入🔴 删除⚪ 未知
🟢list_models(task)

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.

输入模式

{
  "type": "object",
  "properties": {
    "task": {
      "description": "Task name. One of: promoter, splice, enhancer, chromatin, expression, annotation.",
      "title": "Task",
      "type": "string"
    }
  },
  "required": [
    "task"
  ],
  "title": "list_modelsArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "list_modelsDictOutput"
}
🟢fetch_ensembl_sequence(gene, species, flank_bp)

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).

输入模式

{
  "type": "object",
  "properties": {
    "gene": {
      "description": "Gene symbol (e.g. 'TP53') or Ensembl ID.",
      "title": "Gene",
      "type": "string"
    },
    "species": {
      "default": "human",
      "description": "Species name, e.g. 'human', 'mouse'.",
      "title": "Species",
      "type": "string"
    },
    "flank_bp": {
      "default": 0,
      "description": "Extra bp added on each side of the gene body.",
      "minimum": 0,
      "title": "Flank Bp",
      "type": "integer"
    }
  },
  "required": [
    "gene"
  ],
  "title": "fetch_ensembl_sequenceArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "fetch_ensembl_sequenceDictOutput"
}
🟢fetch_region(region, species, strand, flank_bp)

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.

输入模式

{
  "type": "object",
  "properties": {
    "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.",
      "title": "Region",
      "type": "string"
    },
    "species": {
      "default": "human",
      "description": "Species name, e.g. 'human', 'mouse'.",
      "title": "Species",
      "type": "string"
    },
    "strand": {
      "default": 1,
      "description": "1 = plus (default), -1 = minus. find_genes (gene finding) is plus-oriented — keep 1 for annotation; use -1 only for a strand-sensitive task on a known minus-strand locus.",
      "title": "Strand",
      "type": "integer"
    },
    "flank_bp": {
      "default": 0,
      "description": "Extra bp added on each side of the region.",
      "minimum": 0,
      "title": "Flank Bp",
      "type": "integer"
    }
  },
  "required": [
    "region"
  ],
  "title": "fetch_regionArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "fetch_regionDictOutput"
}
🟢fetch_gene_for_expression(gene, species)

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.

输入模式

{
  "type": "object",
  "properties": {
    "gene": {
      "description": "Gene symbol (e.g. 'HBB').",
      "title": "Gene",
      "type": "string"
    },
    "species": {
      "default": "human",
      "description": "Species name.",
      "title": "Species",
      "type": "string"
    }
  },
  "required": [
    "gene"
  ],
  "title": "fetch_gene_for_expressionArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "fetch_gene_for_expressionDictOutput"
}
⚪load_demo_sequence(name)

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.

输入模式

{
  "type": "object",
  "properties": {
    "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.",
      "title": "Name",
      "type": "string"
    }
  },
  "required": [
    "name"
  ],
  "title": "load_demo_sequenceArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "load_demo_sequenceDictOutput"
}
🟡store_inline_sequence(sequence, name)

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.)

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "description": "DNA bases to store and get a handle for. Line breaks are fine — whitespace is stripped, so the handle holds bases.",
      "title": "Sequence",
      "type": "string"
    },
    "name": {
      "default": "sequence",
      "description": "Label for this sequence.",
      "title": "Name",
      "type": "string"
    }
  },
  "required": [
    "sequence"
  ],
  "title": "store_inline_sequenceArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "store_inline_sequenceDictOutput"
}
🟢predict_promoter(sequence, sequence_ref, sequence_name, model)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    }
  },
  "title": "predict_promoterArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "predict_promoterDictOutput"
}
🟢predict_splice(sequence, sequence_ref, sequence_name, model)

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`).

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    }
  },
  "title": "predict_spliceArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "predict_spliceDictOutput"
}
🟢predict_enhancer(sequence, sequence_ref, sequence_name, model)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    }
  },
  "title": "predict_enhancerArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "predict_enhancerDictOutput"
}
🟢predict_chromatin(sequence, sequence_ref, sequence_name, model)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    }
  },
  "title": "predict_chromatinArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "predict_chromatinDictOutput"
}
🟢predict_expression(sequence, sequence_ref, description, tss_index, sequence_name, ...)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "description": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "REQUIRED experimental context — cell type / assay / conditions (e.g. 'K562 cell line', 'liver tissue'). Expression is cell-type-specific; the API rejects requests without it.",
      "title": "Description"
    },
    "tss_index": {
      "anyOf": [
        {
          "minimum": 0,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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 — check it: a wrong-but-in-range offset scores the wrong window silently.",
      "title": "Tss Index"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    }
  },
  "title": "predict_expressionArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "predict_expressionDictOutput"
}
🟢find_genes(sequence, sequence_ref, sequence_name, model, wait)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "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`.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Handle (seq_…) 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`.",
      "title": "Sequence Ref"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back in the response (ignored when `sequence_ref` is used).",
      "title": "Sequence Name",
      "type": "string"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional model id; omit for the task default. See list_models.",
      "title": "Model"
    },
    "wait": {
      "default": true,
      "description": "Default True: block and stream progress until the result is ready. Set False for detached mode — returns a job_id immediately to poll with get_job.",
      "title": "Wait",
      "type": "boolean"
    }
  },
  "title": "find_genesArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "find_genesDictOutput"
}
🟢find_genes_and_predict_expression(sequence, sequence_ref, description, sequence_name, wait)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "DNA bases, 1,000-500,000 bp (line breaks ignored). Mutually exclusive with sequence_ref.",
      "title": "Sequence"
    },
    "sequence_ref": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Stored sequence handle. Mutually exclusive with sequence.",
      "title": "Sequence Ref"
    },
    "description": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "REQUIRED experimental context — 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.",
      "title": "Description"
    },
    "sequence_name": {
      "default": "sequence",
      "description": "Label echoed back.",
      "title": "Sequence Name",
      "type": "string"
    },
    "wait": {
      "default": true,
      "description": "Default True: block and stream progress until the result is ready. Set False for detached mode — returns a job_id immediately to poll with get_job.",
      "title": "Wait",
      "type": "boolean"
    }
  },
  "title": "find_genes_and_predict_expressionArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "find_genes_and_predict_expressionDictOutput"
}
🟢get_job(job_id)

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.

输入模式

{
  "type": "object",
  "properties": {
    "job_id": {
      "description": "Job id from an async tool (find_genes, find_genes_and_predict_expression).",
      "title": "Job Id",
      "type": "string"
    }
  },
  "required": [
    "job_id"
  ],
  "title": "get_jobArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "get_jobDictOutput"
}
🟢list_jobs(limit)

List the caller's recent async jobs (also available as gi://jobs/recent).

输入模式

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 20,
      "description": "Max number of recent jobs to return.",
      "maximum": 100,
      "minimum": 1,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "list_jobsArguments"
}

输出模式

{
  "type": "object",
  "additionalProperties": true,
  "title": "list_jobsDictOutput"
}

推荐提示词

search_research
Search for information about [topic] using Genomic Intelligence
预期工具: find_genes
find_specific
Find [specific item] using Genomic Intelligence
预期工具: find_genes
retrieve_data
Get details about [item] from Genomic Intelligence
预期工具: fetch_ensembl_sequence
fetch_info
Fetch [information type] using Genomic Intelligence
预期工具: fetch_ensembl_sequence
list_items
List all [items] available in Genomic Intelligence
预期工具: list_models

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