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littlebigbrains

@littlebigbrain/mcp

lbb_query

Read-only

Retrieve data from knowledge graphs using structured JSON queries or SPARQL, with support for filtering, grouping, and aggregations.

Instructions

Analytical and expert reads. Modes: structured (SPARQL-subset JSON body), sparql (SPARQL text), analyze. Relations are https://littlebigbrain.com/r/NAME and types https://littlebigbrain.com/class/NAME (both lowercased); entities are content-addressed, so anchor a named one by its rdfs:label rather than building its IRI. Structured and text queries pin one published watermark for the request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoStructured SPARQL-subset or analytics request body. Shape: { patterns: [{ subject, predicate, object }], filters?, group_by?, group_keys?, aggregates?, having?, order_by?, select?, limit?, distinct? }. Each pattern term is { var: "x" } or a fixed { entity: { entity_type, name } }; `predicate` is a relation name and is case-insensitive here (FOR_CLIENT and for_client both resolve — unlike SPARQL text, which needs the lowercased IRI local name). FILTER — `filters` is a list of conditions, each of exact shape { "compare": { "op": <op>, "left": <term>, "right": <term> } } (or { "and": [<filter>…] }, { "or": [<filter>…] }, { "not": <filter> }). `op` is one of eq | ne | lt | le | gt | ge (NOT the symbols =,<,>). Each <term> is exactly one of { "var": "x" }, { "property": { "var": "x", "field": "amount" } } (a typed scalar attribute), or { "value": <typed> } — and <typed> is exactly one wrapper: { "str": "…" }, { "i64": 5 }, { "f64": 0.9 }, { "bool": true }, { "date_time": "2026-01-01" } (RFC3339), or { "entity": { "entity_type": "T", "name": "N" } }. Complete runnable example — deals whose amount ≥ 1000000: { "patterns": [{ "subject": { "var": "d" }, "predicate": "for_client", "object": { "var": "c" } }], "filters": [{ "compare": { "op": "ge", "left": { "property": { "var": "d", "field": "amount" } }, "right": { "value": { "f64": 1000000 } } } }] }. Comparisons use the field's real declared type (numbers as numbers, datetimes as instants), so they run server-side. GROUP BY supports both entity-identity keys (group_by: ["s"]) and typed scalar keys via group_keys: a property value ({ property: { var, field, as } }) or a calendar bucket of a datetime property ({ date_bucket: { var, field, granularity: year|month|week|day|hour, as } }). Scalar keys come back per group under value_keys[as] — so a per-area breakdown or a commits-per-month time series is one server-side query, no client-side bucketing. Worked example -- commits per area per month in one query: { "patterns": [{ "subject": { "var": "c" }, "predicate": "committed_to", "object": { "var": "repo" } }], "group_keys": [{ "date_bucket": { "var": "c", "field": "committed_at", "granularity": "month", "as": "m" } }, { "property": { "var": "c", "field": "area", "as": "area" } }], "aggregates": [{ "func": "count", "as": "n" }], "order_by": [{ "var": "m" }] } -- area and committed_at are typed entity attributes (set via entity_properties; readable flat under attributes, never a nested metadata blob), and each group returns value_keys.m + value_keys.area + aggregates.n. `having: [...]` takes the same filter shape over the aggregated groups (e.g. { "compare": { "op": "gt", "left": { "var": "n" }, "right": { "value": { "i64": 10 } } } }); it is evaluated only on this grouped path, NOT alongside `combinators` (UNION/OPTIONAL/MINUS), which route to the analytics engine. Cheap aggregate count: pair an equality having (e.g. { "compare": { "op": "eq", "left": { "var": "n" }, "right": { "value": { "i64": 4 } } } }) with row_limit: 1 -- the response row_page.total reports how many groups match without materializing them all, so you read the count off row_page.total instead of paging every matching row. For snapshot pinning prefer the top-level `as_of` / `as_of_commit_seq` arguments below; a bare `as_of` key inside the body is rejected (the body's valid-time field is `as_of_valid_time`).
modeYesSelects the variant (one of: structured, sparql, analyze).
as_ofNoSnapshot pin (valid-time, RFC3339): evaluate the body as of this instant. Folded into the request's `as_of_valid_time`. Top-level here is the supported spelling — a bare `as_of` inside the body is rejected, since the server silently ignores it.
chartNo
fieldNo
graphNoGraph to target; defaults to the connection's graph
queryNoSPARQL 1.1 query text (SELECT or ASK). IRI scheme: relations are <https://littlebigbrain.com/r/NAME> (NAME lowercased, e.g. writes_to; reverse a relation with the ^ path operator, no stored inverse triple). Types are <https://littlebigbrain.com/class/NAME> (lowercased), matched as `?x a <…/class/NAME>` with rdfs:subClassOf closure on by default. Property fields are <https://littlebigbrain.com/p/NAME> (lowercased). The local name is ALWAYS lowercase — an uppercase one (e.g. <…/r/FOR_CLIENT>) is a different, non-existent IRI that silently matches nothing; this tool auto-lowercases the local name of /r/, /class/, and /p/ IRIs for you and adds a `notes` entry when it does, so a stray uppercase still resolves. (Structured mode's `predicate` is case-insensitive on its own.) Entities are content-addressed <https://littlebigbrain.com/e/HASH> — never build an entity IRI from a name; anchor a named entity by its label instead: `?e <http://www.w3.org/2000/01/rdf-schema#label> "Acme"`. Discover the exact relation and type names with lbb_inspect action=ontology. SELECT and ASK only (CONSTRUCT/DESCRIBE are rejected). Example: SELECT ?service ?db WHERE { ?service <https://littlebigbrain.com/r/writes_to> ?db } LIMIT 10
top_kNo
branchNoBranch to target; defaults to the connection's branch
cursorNoOpaque cursor from a previous lbb_query row page; reruns the original query at the next offset.
detailNoResponse detail level. Defaults to compact.
metricNo
sparqlNo
row_limitNoMaximum query rows to return in this page. Defaults by detail: compact=20, standard=100, full=1000.
as_of_commit_seqNoSnapshot pin: evaluate the body as of this commit_seq, hiding later commits. Errors if past head. Top-level alias for the body's `as_of_commit_seq` (either works for this one).
Behavior5/5

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

The description adds extensive behavioral context beyond the readOnlyHint annotation, including snapshot pinning via as_of, content-addressed entities, case-insensitivity in structured mode, auto-lowercasing of IRIs in SPARQL mode, cursor-based pagination, and default row limits by detail level.

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

Conciseness4/5

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

The description is lengthy but well-structured: it front-loads the core purpose and modes, then systematically details each parameter with examples. Some redundancy exists (e.g., body documentation repeats in sparql), but clarity justifies the length.

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 the complexity (15 parameters, nested objects, no output schema), the description covers most aspects: mode selection, body construction, snapshot pinning, pagination, detail levels. It lacks explicit description of the response format (e.g., fields in each row), but mentions row_page.total for count queries, making it reasonably complete.

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

Parameters5/5

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

The description provides rich semantic details for parameters, especially body (with full JSON structure, filter syntax, grouping examples), mode, query (IRI scheme and examples), as_of (snapshot pinning), and row_limit (defaults). It compensates for the 67% schema coverage with detailed explanations and runnable examples.

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 starts by stating 'Analytical and expert reads' and enumerates three specific query modes (structured, sparql, analyze), clearly identifying the tool as a knowledge graph query interface. It distinguishes itself from siblings by focusing on SPARQL and structured queries, while referencing lbb_inspect for ontology discovery.

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

Usage Guidelines3/5

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

The description implies usage for querying the Little Big Brain knowledge graph but does not explicitly state when to use this tool versus alternatives like lbb_search or lbb_inspect. It does mention that lbb_inspect can be used for ontology discovery, providing some guidance.

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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