Brapi MCP Server

Pull observations across one or more studies and pivot them into a germplasm × trait matrix materialized as a canvas dataframe. Returns a dataframe handle (read it with brapi_dataframe_describe and brapi_dataframe_query) plus a summary of dimensions and aggregate method. Long-form output is suitable for downstream GROUP BY analysis by study, germplasm, or variable. A study whose pull fails is named in warnings and the rest still build the matrix; the call fails only when every study failed.

Open a connection to a BrAPI v2 server, authenticate, and return the full orientation envelope (server identity, capability profile, content summary, suggested next tools). Other BrAPI tools connect an alias the server has a URL configured for (a built-in or operator-configured alias, or `default` on a deployment with a default server) on first use; call this to connect any other server by `baseUrl`, to pass `auth`, or to re-point an alias. Supports multiple concurrent connections via named aliases. Credentials can be configured server-side and omitted from this call. When a request carries no MCP session on a deployment without per-user auth, aliases live in one namespace shared by every such caller: re-registering an alias re-points their later calls to it. Built-in known servers (callable with no `baseUrl` or `auth` — public BrAPI v2 endpoints): `bti-breedbase-demo`, `bti-cassava`, `bti-sweetpotato`. Operator-configured aliases on this deployment (credentials and/or baseUrl read from server env vars): `default`, `cassava`. Aliases are shortcuts only; any other BrAPI v2 server is reachable by passing `baseUrl` directly.

Start here after a spillover. Lists dataframes (or describes one) with columns, row counts, and originating-source provenance. The dataframe name appears inline on every find_* response that spilled (`result.dataframe.tableName`) — pass it as `dataframe` to inspect schema and provenance before writing the first brapi_dataframe_query. On a shared HTTP endpoint without per-caller auth, listing without a name is available only where each MCP session has its own workspace, and never to a request without a session; pass a known name instead.

Run SQL across in-memory dataframes. Dataframes auto-populate when find_* tools spill (named `df_<uuid>`) — the dataframe name appears inline on every find_* response that spilled (`result.dataframe.tableName`), so the typical flow is find_* → read the name → query here. Use brapi_dataframe_describe to inspect schema and provenance for a known name. SELECT only — writes/DDL/COPY/PRAGMA/ATTACH/file-reads are rejected. Use SQL as the paging idiom: `LIMIT/OFFSET` to walk results, projection to trim columns, aggregation to summarize. Use `registerAs` to chain — the result lands as a new dataframe.

List the valid filter names for a BrAPI endpoint (studies, germplasm, observations, variables, images, variants, locations) — companion lookup for the `extraFilters` passthrough on any `find_*` tool. Entries reflect the BrAPI v2.1 spec; individual servers may implement subsets.

Pull genotype calls for a germplasm × variant set and pivot them into a matrix. `matrix-json` registers a wide canvas dataframe; `vcf-lite` adds VCF-subset text in `vcf`; `plink` adds .ped/.map text in `ped`/`map`. Text formats pull /variants metadata and convert zero-based BrAPI start to one-based positions; missing/invalid positions use `.`/`0`. Read the dataframe with brapi_dataframe_describe and brapi_dataframe_query (SQL). `variantColumnLegend` maps SQL-safe columns to original variant IDs.

Pull genotype calls for a germplasm × variant set. Filter to bound cost — at minimum, set `variantSetDbId` or `germplasmDbIds`. The upstream pull is capped by deployment policy; when the pull is truncated, narrow the filters. results[] holds the leading calls up to `loadLimit` and a 24,000-byte budget; when it holds fewer than were collected, every collected call is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL) instead of paging row-by-row.

Find germplasm by name, synonym, accession number, PUI, crop, or free-text query. Matches across registered synonyms. results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL) instead of paging row-by-row.

Filter images by observation unit, observation, study, descriptive ontology term, file name, or MIME type. Returns metadata only — use brapi_get_image to fetch bytes inline. results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Find research stations / field sites by country, abbreviation, type, location ID, or free-text. Countries filter by ISO 3166-1 alpha-3 code via countryCodes, or by free-form English country name via countryNames (resolved client-side to alpha-3 — "Uganda" → "UGA"). Optional bbox parameter restricts rows to a latitude/longitude window. When no first-page row falls inside it under the spec GeoJSON [lon, lat, alt] reading and a row carries a Point geometry, bbox is retried with axes swapped; if that matches, the swapped reading is used (non-conformant servers store [lat, lon, alt]) with a warning and `coordinateAxisOrder: "swapped"`. results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Pull observation records filtered by study, germplasm, variable, season, or observation unit. Without a studies, trials, observationUnits, or observations anchor, a GET query first runs a one-row count probe and skips the bulk pull (warnings says so) when the probe fails or reports more than 5,000 matches. results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when those bounds cut it short, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Locate studies matching crop, trial type, season, location, or program. Enriches results with program/trial/location context in one call. results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Find observation variables (traits) by name, trait class, ontology term, or free-text query. Free-text queries are ranked against every fetched row by PUI, name, synonym, and trait text; a candidate carries the ontology PUI of its row as termId when the row has one. results[] holds the leading rows up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Find variant records by variant set, reference sequence, or genomic region (start/end, 0-based inclusive / exclusive per the BrAPI spec; servers may differ). results[] holds the leading matches up to loadLimit and a 24,000-byte budget; when it holds fewer rows than matched, the matched set is materialized as a dataframe — read it with brapi_dataframe_describe and brapi_dataframe_query (SQL).

Aggregate a single germplasm's observations across studies, returning per-variable statistics (n, mean, median, sd, min, max), contributing studies, seasons, and collection coverage. SGN/Breedbase uses advertised germplasm-scoped observation units with embedded measurements, including zeros. Other servers discover up to 200 studies before pulling per study. Pass studyDbIds to skip discovery and restrict collection to known studies. Per-study observation limits, page limits, failures, and incomplete scans appear in coverage and warnings. For the underlying matrix, use brapi_build_phenotype_matrix.

Fetch a single germplasm by DbId with attributes and direct parents. Response companions report study count, direct parent count, and direct descendant count — signals for pedigree depth and observation coverage.

Fetch image bytes for up to 5 imageDbIds and return them inline as `type: image` content blocks. Falls back to the metadata `imageURL` when the server lacks dedicated image-content delivery or that request fails. No filesystem side-effects.

Fetch a single study by DbId with its program, trial, and location records resolved where those lookups succeed. Response includes cheap observation/observation-unit/variable counts as drill-down signals.

Passthrough to BrAPI GET /{path} without foreign-key resolution. List results (result array or result.data) return a leading preview within loadLimit and a 24,000-byte budget on each serialized row representation; an oversized first row returns no inline rows. Omitted fetched rows remain in a dataframe readable with brapi_dataframe_describe and brapi_dataframe_query. Explicit params.page/pageSize fetches only that upstream page, staging it if trimmed. Non-list results and envelope siblings are preserved. Emits a suggestion when a curated tool covers the endpoint.

Passthrough to BrAPI POST /search/{noun}, polling async search IDs. Sends loadLimit as the initial pageSize unless body.page/pageSize drives explicit paging. List previews fit loadLimit and a 24,000-byte budget on each serialized row representation; an oversized first row returns no inline rows. Every fetched row omitted inline stays in a dataframe readable with brapi_dataframe_describe and brapi_dataframe_query. Explicit paging fetches only the requested page, staging it if trimmed. Non-list results and envelope siblings are preserved; no distributions or foreign-key resolution.

Return the full orientation envelope for a BrAPI connection — server identity, capabilities, content counts, suggested finders, and notes. Pass forceRefresh to refetch the cached capability scan; pass an alias to read a non-default connection.

Walk germplasm ancestry or descendancy as a deduplicated DAG with cycle detection and depth limits. leafCount counts graph terminals, confirmedLeafCount counts successfully verified leaves, and frontier records unresolved directions. Nodes and edges each fit loadLimit and a 24,000-byte preview budget; trimmed discovered graphs and already-fetched omitted relatives are staged for brapi_dataframe_describe and brapi_dataframe_query (SQL). Diagrams describe only the discovered graph, never unvisited relatives.