Synthesize Bio MCP Integration for AI Agents

Returns the structured-metadata schema used to turn a natural-language experiment description into sample groups. Response fields include `group_schema` (the JSON shape of one sample group), the `fields` and `perturbation_fields` inventory for the active metadata version, and `instructions` for building the `groups` array. The resulting `groups` array is the input to resolve_sample_metadata. This tool makes no AI calls and consumes no usage budget.

Deterministically harmonizes already-structured sample `groups` to Synthesize Bio's controlled ontology vocabulary. Does not accept a natural-language prompt and makes no AI calls. Input: `groups` — a JSON array of sample-group objects matching get_metadata_schema's `group_schema`. Returns a resolution_id and a per-group breakdown including tissue, disease, cell type/line (with resolved ontology ids), sex, age, and full perturbation details (type, label/id, gene mechanism and mechanism type, dose, dose count, timepoint). Each resolved field also carries ranked candidate options for disambiguation. If a value resolves to the wrong id, correcting the term in `groups` and calling again produces a new resolution. The `warnings` array flags issues such as a described drug that did not match the ontology. When status is `resolving`, the same resolution_id identifies the pending request and can be passed again to poll. A completed resolution_id is required by analyze_gene_expression. Optional workspace_id selects which workspace the resolution is stored under. When omitted, a single-workspace account is assigned automatically. When the account has more than one workspace and workspace_id is omitted, the call is rejected with failure_kind `workspace_selection_required` and a `workspaces` list of `{ name, workspace_id }`; no metadata resolution runs until the user picks and the tool is retried with workspace_id. Quota and monthly-limit errors include a request-higher-limits URL and indicate remaining samples/cells when available.

Starts a differential gene expression analysis using Synthesize Bio's AI platform. Requires the resolution_id returned by resolve_sample_metadata; raw natural-language prompts are not accepted. Requires `user_confirmed_metadata: true`. When the flag is missing or false, the call is rejected with failure_kind `user_confirmation_required`. Optional workspace_id selects which workspace owns the generated dataset. When omitted, the workspace from resolve_sample_metadata is used automatically. When the account has more than one workspace and the resolution has no workspace, the call is rejected with failure_kind `workspace_selection_required` and a `workspaces` list of `{ name, workspace_id }`. Returns a job_id immediately; get_analysis_results accepts that job_id and returns analysis status or results. The pipeline runs two steps: (1) GEM-1 — Synthesize Bio's Gene Expression Model inference; (2) Differential expression — GPU-accelerated DESeq2 (negative-binomial GLM with Wald test, Cook's outlier filter, and Benjamini-Hochberg padj). All genes are tested; pre-filtering is handled by DESeq2's independent filtering. If the query is unsupported, later polling responses include failure_kind `unsupported_query` and suggested_queries. Quota and monthly-limit errors include a request-higher-limits URL; a previous successful resolve does not grant an extra run when the account is out of budget.

Polls the status of a gene expression analysis. Each call waits server-side for a short bounded window and may return earlier if progress is detected. Responses always include a `structuredContent` object (declared by the tool's `outputSchema`); MCP clients read from `structuredContent` directly rather than re-parsing JSON out of the human-readable text. `structuredContent` always has `status` (one of `running`, `complete`, `failed`), `job_id`, and `steps_completed`. While running, it also has `step` (`gem_model` or `diff_expr`), `message`, and `progress_label`/`progress_percent`/`progress_bar`. Failed responses include `error`, and may also include `failure_kind`, `user_action_required`, and `suggested_queries`. When `status` is `complete`, `structuredContent` carries: `metadata` (prompt, modality, groups, plus summary counts such as `significant_genes`, `significant_up`, `significant_down`, `total_genes_tested`); `results` — up to 1000 differential expression rows (each with `gene_id`, `gene_symbol`, `log2FoldChange`, `pvalue`, `padj`, `neg_log10_padj` (pre-computed `-log10(padj)`, clamped to 300 if padj underflows), `direction`, `significant`) suitable for downstream analysis or visualization (e.g. a volcano plot with x = `log2FoldChange`, y = `neg_log10_padj`); `plot_results` — the top ~200 most significant rows (same per-row shape, pre-sorted most-significant-first), pre-sliced for charting; the full `results` array is better suited to tables, summaries, and analysis; `results_returned` and `results_total` for truncation accounting; `plot_results_returned` for the plotted subset size; `dataset_link` — `{ dataset_id, title, url }` for the Synthesize Bio platform dataset (or `null`). The accompanying `content[0].text` is a human-readable Markdown summary of the same data. For hosts that do not surface `structuredContent` (e.g. claude.ai), it inlines only the top ~200 most significant rows as an array of objects under a top-level `results` key — same per-row schema as `structuredContent.results`, including the pre-computed `neg_log10_padj` field — so chart-widget code can use those rows directly. The full result set remains available via `structuredContent.results` and the dataset link when present.

Returns a presigned S3 URL to download the raw gene expression counts data (JSON) produced by a completed or in-progress analysis job. Supported only in Claude Code or Codex with control of local terminal sessions; chat clients and hosted/browser-based code sandboxes are unsupported, even with Python or shell tools. The data is typically large (20,000+ genes by N samples) and is intended for local processing. Raw counts are optional for custom computations; DE results and contrast labels are available separately from get_analysis_results. The JSON has the following structure: { gene_order: string[] (Ensembl IDs), outputs: [{ counts: number[], metadata: object }], model_version: string }. Each entry in 'outputs' corresponds to one sample; 'counts' is aligned with 'gene_order'. The response also returns a second presigned URL to a small (~500 KB) Parquet file mapping every Ensembl `gene_id` in `gene_order` to its HGNC gene_name — both files join on gene_id to label genes by symbol. Both URLs expire after 1 hour; fresh responses contain fresh URLs. Available after analyze_gene_expression has completed the GEM-1 step.