From 8d7333b31a8d3c9cf47b00f860b7787f63a44b9e Mon Sep 17 00:00:00 2001 From: Kunitsyn Oleg Date: Tue, 28 Apr 2026 11:01:17 +0200 Subject: [PATCH 1/3] [ODM-13248] Add mcp server build --- Earthfile | 28 + mcp-server/.gitignore | 2 + mcp-server/.python-version | 1 + mcp-server/pyproject.toml | 11 + mcp-server/src/main.py | 370 ++++++++++++ mcp-server/uv.lock | 1168 ++++++++++++++++++++++++++++++++++++ 6 files changed, 1580 insertions(+) create mode 100644 mcp-server/.gitignore create mode 100644 mcp-server/.python-version create mode 100644 mcp-server/pyproject.toml create mode 100644 mcp-server/src/main.py create mode 100644 mcp-server/uv.lock diff --git a/Earthfile b/Earthfile index f4a1978c..ac051494 100644 --- a/Earthfile +++ b/Earthfile @@ -177,9 +177,37 @@ stoplight: SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/stoplight:${OPENAPI_VERSION} SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/stoplight:latest +openapi-mcp-server: + FROM astral/uv:0.10.7-python3.13-trixie-slim + + RUN groupadd --system --gid 999 nonroot \ + && useradd --system --gid 999 --uid 999 --create-home nonroot + USER nonroot + + WORKDIR /app + + ENV PYTHONUNBUFFERED=1 + ENV PYTHONDONTWRITEBYTECODE=1 + ENV UV_SYSTEM_PYTHON=1 + ENV UV_LINK_MODE=copy + + COPY mcp-server/pyproject.toml mcp-server/uv.lock . + RUN uv sync --frozen --no-cache --no-dev + + COPY mcp-server/src /app/src + + # Run the application using uv + ENTRYPOINT ["uv"] + CMD ["run", "src/main.py"] + + ARG --required OPENAPI_VERSION + SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/openapi-mcp-server:${OPENAPI_VERSION} + SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/openapi-mcp-server:latest + main: BUILD +swagger BUILD +stoplight BUILD +docs BUILD +r-api-client BUILD +python-api-client + BUILD +openapi-mcp-server diff --git a/mcp-server/.gitignore b/mcp-server/.gitignore new file mode 100644 index 00000000..8d193625 --- /dev/null +++ b/mcp-server/.gitignore @@ -0,0 +1,2 @@ +.venv +.env diff --git a/mcp-server/.python-version b/mcp-server/.python-version new file mode 100644 index 00000000..0104088a --- /dev/null +++ b/mcp-server/.python-version @@ -0,0 +1 @@ +3.14.4 diff --git a/mcp-server/pyproject.toml b/mcp-server/pyproject.toml new file mode 100644 index 00000000..584300ad --- /dev/null +++ b/mcp-server/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "mcp-server" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "asyncio>=4.0.0", + "fastmcp>=3.1.0", + "httpx>=0.28.1", +] diff --git a/mcp-server/src/main.py b/mcp-server/src/main.py new file mode 100644 index 00000000..ed4e282e --- /dev/null +++ b/mcp-server/src/main.py @@ -0,0 +1,370 @@ +import asyncio +import base64 +import os +from typing import Optional +import httpx +from fastmcp import FastMCP, Client +from pydantic import BaseModel, Field + +odm_url = os.environ.get("ODM_URL", "https://develop-oak.dev.gs.team").rstrip("/") +odm_token = os.environ.get("API_TOKEN", "tknRoot") + +server_host = os.environ.get("SERVER_HOST", "0.0.0.0") +server_port = int(os.environ.get("SERVER_PORT", 8080)) + +mcp = FastMCP(name="ODM EDA MCP") + +_headers = {"Genestack-API-Token": odm_token} + +PAGE_LIMIT = 50 +SEARCH_STUDIES_PATH = "/api/v1/as-user/integration/fulltext/search/studies" +SYSTEM_FILTER_GROUPS = {"GS_VALIDITY_FLAG", "GS_Access"} + + +class StudiesSearchParams(BaseModel): + full_text: Optional[str] = Field( + default=None, + description=( + "Free-text search across study metadata (STRICT match). " + 'Examples: "TCGA", "covid", "breast cancer".' + ), + ) + data_classes: Optional[list[str]] = Field( + default=None, + description=( + "Filter by measurement data classes. Multiple values are AND-combined " + "(study must have all). Closed list: " + "Bulk transcriptomics, DNA methylation, Gene panel data, Gene variant (VCF), " + "Proteomics, Single-cell transcriptomics, Flow Cytometry (FACS), Other. " + 'Example: ["Bulk transcriptomics", "Gene variant (VCF)"].' + ), + ) + metadata: Optional[dict[str, list[str]]] = Field( + default=None, + description=( + "Filter by metadata fields. Keys are field names (spaces or underscores accepted), " + "values are lists of option names. Multiple values for the same key are AND-combined. " + "Common queryable fields: Therapeutic Area, Study Source, Disease, Organism, " + "Tissue, Sample Type, Study Type, Sex, Cell Type, Platform Type, " + "Tumor Histological Type, Sample Source, Genome Version, " + "Library Preparation Protocol, Project ID, Experimental Platform, Data Species, " + "Data Processing Method, Normalization Method, Study ID, Condition, " + "Primary Tumor Anatomical Site Subdivision, Attached file attribute, Crop, Group, " + "Tissue Type, Assay Type, Contact Institute, Organization name. " + "Other metadata fields may also be queryable; the exact field/value names " + "available for the current result set appear in `available_filters` of any " + "prior response. " + 'Example: {"Therapeutic Area": ["Oncology"], "Disease": ["breast cancer"]}.' + ), + ) + + +class FacetOption(BaseModel): + name: str + study_count: int + + +class FacetGroup(BaseModel): + options: list[FacetOption] + has_more: bool + + +class AvailableFilters(BaseModel): + data_classes: FacetGroup = Field( + description="Available GS_DATA_CLASS values to narrow on. Mirrors input `data_classes`." + ) + metadata: dict[str, FacetGroup] = Field( + description="Available metadata-field options. Keys mirror input `metadata` keys." + ) + + +class DataClassMatrixRow(BaseModel): + data_classes: list[str] + study_count: int + sample_count: int + + +class DataClassTotal(BaseModel): + study_count: int + sample_count: int + + +class DataClassMatrix(BaseModel): + columns: list[str] = Field(description="Data classes seen in returned studies, frequency desc.") + rows: list[DataClassMatrixRow] = Field( + description="Studies grouped by exact data-class signature; study_count desc." + ) + totals_per_class: dict[str, DataClassTotal] = Field( + description="Per-data-class totals across returned studies (partial when truncated)." + ) + + +class StudyEntry(BaseModel): + accession: str + name: str + sample_count: int + data_classes: list[str] + summary: dict[str, list[str]] + + +class ExploreStudiesSummary(BaseModel): + studies_found: int = Field(description="Total studies matching filters (exact).") + studies_returned: int = Field(description="Studies included in this response (≤50).") + truncated: bool = Field(description="True when studies_found > studies_returned.") + samples_in_returned_studies: int = Field( + description="Sum of sample counts across returned studies (partial when truncated)." + ) + + +class ExploreStudiesResult(BaseModel): + summary: ExploreStudiesSummary + data_class_matrix: DataClassMatrix + studies: list[StudyEntry] = Field(description="Per-study details, sample_count desc, capped at 50.") + available_filters: AvailableFilters + + +def _encode_filter_option_id(filter_id: str, name: str) -> str: + return base64.b64encode(f"{filter_id}:{name}".encode("utf-8")).decode("ascii") + + +def _build_filters(params: StudiesSearchParams) -> list[dict]: + filters: list[dict] = [] + if params.full_text: + filters.append({"type": "FULL_TEXT", "match": params.full_text, "mode": "STRICT"}) + for name in params.data_classes or []: + filters.append({ + "type": "SELECT", + "filterOptionId": _encode_filter_option_id("GS_DATA_CLASS", name), + }) + for raw_key, values in (params.metadata or {}).items(): + filter_id = f"METADATA_{raw_key.replace(' ', '_')}" + for value in values: + filters.append({ + "type": "SELECT", + "filterOptionId": _encode_filter_option_id(filter_id, value), + }) + return filters + + +def _build_studies(content: list[dict]) -> list[StudyEntry]: + studies = [ + StudyEntry( + accession=s["accession"], + name=s["name"], + sample_count=s.get("size", 0), + data_classes=[dc["title"] for dc in s.get("dataClasses", [])], + summary={entry["key"]: entry.get("values", []) for entry in s.get("summary", [])}, + ) + for s in content + ] + studies.sort(key=lambda x: x.sample_count, reverse=True) + return studies + + +def _build_matrix(studies: list[StudyEntry]) -> DataClassMatrix: + class_studies: dict[str, int] = {} + class_samples: dict[str, int] = {} + for s in studies: + for c in s.data_classes: + class_studies[c] = class_studies.get(c, 0) + 1 + class_samples[c] = class_samples.get(c, 0) + s.sample_count + + columns = sorted(class_studies, key=lambda c: class_studies[c], reverse=True) + column_index = {c: i for i, c in enumerate(columns)} + totals_per_class = { + c: DataClassTotal(study_count=class_studies[c], sample_count=class_samples[c]) + for c in columns + } + + groups: dict[frozenset, list[StudyEntry]] = {} + for s in studies: + groups.setdefault(frozenset(s.data_classes), []).append(s) + + rows = [ + DataClassMatrixRow( + data_classes=sorted(key, key=lambda c: column_index.get(c, len(columns))), + study_count=len(group), + sample_count=sum(s.sample_count for s in group), + ) + for key, group in groups.items() + ] + rows.sort(key=lambda r: r.study_count, reverse=True) + return DataClassMatrix(columns=columns, rows=rows, totals_per_class=totals_per_class) + + +def _build_available_filters(filter_option_groups: list[dict]) -> AvailableFilters: + data_classes_group = FacetGroup(options=[], has_more=False) + metadata_groups: dict[str, FacetGroup] = {} + + for group in filter_option_groups: + filter_id = group.get("filterId", "") + if filter_id in SYSTEM_FILTER_GROUPS: + continue + options = [ + FacetOption(name=o["name"], study_count=o["count"]) + for o in group.get("options", []) + if o.get("count") is not None + ] + facet = FacetGroup(options=options, has_more=bool(group.get("hasMoreOptions", False))) + if filter_id == "GS_DATA_CLASS": + data_classes_group = facet + elif filter_id.startswith("METADATA_"): + label = filter_id[len("METADATA_"):].replace("_", " ") + metadata_groups[label] = facet + + return AvailableFilters(data_classes=data_classes_group, metadata=metadata_groups) + + +@mcp.tool +def explore_studies(search_params: StudiesSearchParams) -> ExploreStudiesResult: + """Iteratively define a study cohort for a planned bioinformatics experiment. + + Each call returns: + - summary stats (studies & samples found), + - a data-class availability matrix grouping studies by their exact set of + measurement types, + - per-study details (capped at 50; check `summary.truncated`), + - `available_filters` showing which filter options will narrow further. + + Strategy: + - If you don't know where to start, call with no filters first. The response + surfaces the most populated facets — pick one to narrow on. + - Then add filters and call again to refine. Backtrack by removing filters. + - When `summary.truncated` is true (studies_found > 50), narrow further before + trusting the matrix; per-data-class sample counts are partial in that case. + + Inputs: + - full_text: free-text search (STRICT match). E.g. "TCGA", "covid". + - data_classes: closed list of measurement types to require, e.g. + ["Bulk transcriptomics", "Gene variant (VCF)"]. AND-combined. + - metadata: dict of metadata field → list of allowed values, e.g. + {"Therapeutic Area": ["Oncology"], "Disease": ["breast cancer"]}. + Values for the same key are AND-combined. + + The exact field/value names you can use appear in `available_filters` on every + response — copy them directly into the next call. + """ + body = { + "filters": _build_filters(search_params), + "page": {"offset": 0, "limit": PAGE_LIMIT}, + } + response = httpx.post( + f"{odm_url}{SEARCH_STUDIES_PATH}", + headers={**_headers, "Content-Type": "application/json", "accept": "application/json"}, + json=body, + ) + response.raise_for_status() + data = response.json() + + page = data.get("page", {}) + filtered_count = page.get("filteredCount", 0) + studies = _build_studies(page.get("content", [])) + matrix = _build_matrix(studies) + available = _build_available_filters(data.get("filterOptionGroups", [])) + + return ExploreStudiesResult( + summary=ExploreStudiesSummary( + studies_found=filtered_count, + studies_returned=len(studies), + truncated=filtered_count > len(studies), + samples_in_returned_studies=sum(s.sample_count for s in studies), + ), + data_class_matrix=matrix, + studies=studies, + available_filters=available, + ) + + +# class SamplesSearchParams(BaseModel): +# pass +# +# +# @mcp.tool +# def explore_samples(search_params: SamplesSearchParams) -> dict: +# """Find what samples is available +# """ +# pass +# +# +# @mcp.tool +# def preview_samples_cohort(search_params: SamplesSearchParams) -> dict: +# """When ready to explore in details, reuse query to +# Fetches up to 2000 samples from study with metadata, returns column names, values distributions, across all intersecting columns. +# Doesn't include all the data, but marks if there were more than 2000 samples. +# """ + + +# @mcp.tool +# def get_study_by_id( +# accession_id: str +# ) -> dict: +# """ +# Retrieve a single study by its Genestack accession ID. + +# accession_id: Genestack accession ID (e.g. GSF1678476) +# """ +# response = httpx.get( +# f"{odm_url}/api/v1/as-user/studies/{accession_id}", +# headers=_headers, +# params={"returnedMetadataFields": "minimal_data"}, +# ) +# response.raise_for_status() +# return response.json() + +# @mcp.tool +# def inspect_omics_endpoint( +# params: dict, +# ) -> dict: +# """ +# params +# """ +# params['page_limit'] = 5 +# response = httpx.get( +# f"{odm_url}/api/v1/as-user/omics/samples", +# headers=_headers, +# params=params, +# ) +# response.raise_for_status() +# return response.json() + + +async def test_func(): + cases: list[tuple[str, dict]] = [ + ("covid", {"full_text": "covid"}), + ("tcga + oncology", {"full_text": "TCGA", "metadata": {"Therapeutic Area": ["Oncology"]}}), + ("empty (discovery)", {}), + ] + async with Client(mcp) as client: + for label, search_params in cases: + print(f"\n=== explore_studies: {label} ===") + result = await client.call_tool("explore_studies", {"search_params": search_params}) + d = result.data + print( + f"summary: found={d.summary.studies_found} " + f"returned={d.summary.studies_returned} " + f"truncated={d.summary.truncated} " + f"sample_sum={d.summary.samples_in_returned_studies}" + ) + print(f"matrix columns: {d.data_class_matrix.columns}") + print("matrix rows:") + for r in d.data_class_matrix.rows: + print(f" studies={r.study_count} samples={r.sample_count} dc={r.data_classes}") + print("totals_per_class:") + for c, t in d.data_class_matrix.totals_per_class.items(): + print(f" {c}: studies={t.study_count} samples={t.sample_count}") + print( + "available_filters.data_classes: " + f"{[(o.name, o.study_count) for o in d.available_filters.data_classes.options]} " + f"has_more={d.available_filters.data_classes.has_more}" + ) + print("available_filters.metadata:") + for k, g in d.available_filters.metadata.items(): + print(f" {k} (has_more={g.has_more}): {[(o.name, o.study_count) for o in g.options]}") + if d.studies: + top = d.studies[0] + print(f"top study: 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--required OPENAPI_VERSION - SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/openapi-mcp-server:${OPENAPI_VERSION} - SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/openapi-mcp-server:latest + SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/mcp-server:${OPENAPI_VERSION} + SAVE IMAGE --push ${HARBOR_DOCKER_REGISTRY}/mcp-server:latest main: BUILD +swagger @@ -210,4 +210,4 @@ main: BUILD +docs BUILD +r-api-client BUILD +python-api-client - BUILD +openapi-mcp-server + BUILD +mcp-server From cc19c47f8bbb7ab14dcbc6beede796f013a18f6a Mon Sep 17 00:00:00 2001 From: Kunitsyn Oleg Date: Tue, 28 Apr 2026 14:20:33 +0200 Subject: [PATCH 3/3] [ODM-13248] Fix run statement --- mcp-server/src/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mcp-server/src/main.py b/mcp-server/src/main.py index ed4e282e..5389e518 100644 --- a/mcp-server/src/main.py +++ b/mcp-server/src/main.py @@ -367,4 +367,4 @@ async def test_func(): if __name__ == "__main__": # asyncio.run(test_func()) - mcp.run() + mcp.run(transport="streamable-http", host=server_host, port=server_port)