brapi-mcp-server

Brapi Find Observations

brapi_find_observations

Read-only

Pull observation records filtered by study, germplasm, variable, season, or observation unit. When the upstream total exceeds loadLimit, the full result set is materialized as a dataframe — query it with brapi_dataframe_query (SQL).

Input Schema

TableJSON Schema

Name	Required	Description
`alias`	No	Connection alias registered via brapi_connect. Omit to read the connection registered under alias `default` — i.e. a prior brapi_connect call that did not specify an alias. Calls that used a non-default alias must pass that same alias here.
`trials`	No	Filter by trialDbIds.
`seasons`	No	Filter by seasonDbIds (e.g. "2022").
`studies`	No	Filter by studyDbIds.
`programs`	No	Filter by programDbIds.
`germplasm`	No	Filter by germplasmDbIds.
`loadLimit`	No	Cap on rows returned inline. Omit for the deployment default. Rows beyond the cap land in a dataframe; query with brapi_dataframe_query (SQL) instead of paging row-by-row.
`variables`	No	Filter by observationVariableDbIds.
`timestampTo`	No	ISO 8601 end of the observation-time window.
`extraFilters`	No	Extra BrAPI filters forwarded verbatim. Valid keys vary by endpoint; brapi_describe_filters enumerates them. Named params on this tool take precedence on conflict.
`observations`	No	Filter by observationDbIds.
`timestampFrom`	No	ISO 8601 start of the observation-time window.
`observationUnits`	No	Filter by observationUnitDbIds.
`observationLevels`	No	Observation unit level (plot, plant, field, etc.).

Output Schema

TableJSON Schema

Name	Required	Description
`alias`	No	Alias of the registered BrAPI connection the call used.
`error`	No	Present when the call failed. Absent on success.
`notice`	No	Guidance when no rows were returned — how to broaden filters or retry.
`hasMore`	No	True when more rows exist beyond the returned set.
`results`	No	Observation rows returned in-context (up to loadLimit).
`warnings`	No	Advisory messages (filter overrides, partial data, capability gaps).
`dataframe`	No	Dataframe handle when the full result set was materialized as a dataframe. Query it with brapi_dataframe_query (SQL).
`totalCount`	No	Total rows reported by the server.
`distributions`	No	Value frequency per field across the full result set.
`returnedCount`	No	Length of results[].
`appliedFilters`	No	The final filter map sent to the server (named + extraFilters).
`refinementHint`	No	Suggested next-step query refinement when the result set is large.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 19, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With readOnlyHint and openWorldHint already supplied by annotations, the description adds non-obvious behavior: large result sets are materialized as a dataframe and queried via SQL through brapi_dataframe_query. This tells the agent what to expect and how to proceed after a large call, which goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler: the first fronts the purpose and filters, the second delivers the critical overflow behavior and next-step tool. It earns every word and is immediately scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 14 parameters, an output schema, and annotations already present, the description covers the main behavioral twist (dataframe overflow) and points to the right continuation tool. It does not enumerate every filter, but the schema already provides full per-parameter descriptions, so nothing essential is missing for invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description's filter term list largely restates parameter names already defined in the schema, and the loadLimit dataframe behavior is already in the schema's loadLimit description. Thus the description adds no material parameter semantics beyond the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with the specific operation "Pull observation records" and names the exact filter dimensions (study, germplasm, variable, season, observation unit), which makes the resource and scope unambiguous. This distinguishes it clearly from sibling tools like brapi_find_studies, brapi_find_germplasm, and brapi_find_variables without needing to open the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The first sentence identifies the intended use case: retrieving observation records under BrAPI filters. It also gives a concrete handoff rule: when the upstream total exceeds loadLimit, the result is a dataframe and should be queried with brapi_dataframe_query rather than paged row-by-row. It does not explicitly list exclusions versus generic tools like brapi_raw_search, but the use context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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