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littlebigbrains

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lbb_ask

Read-only

Ask natural-language questions about a knowledge graph and receive grounded answers with citations. Set 'execute: false' to retrieve only the mapped vocabulary without retrieval.

Instructions

Ask a natural-language question about the graph and get a grounded answer with citations. The database snaps the question to its real vocabulary (never invented), retrieves against the pinned snapshot, and answers. mode is resident_planner when a small resident model synthesized the prose, or grounding_only when it returns the grounded evidence for your own model to finish. Prefer this over lbb_search when you want a direct, cited answer rather than a ranked list; set execute: false to get only the grounding — the real vocabulary the question maps to — without retrieval. The response explain block reports how much the database narrowed (vocabulary candidates the question snapped to, plus retrieved entity/assertion counts) and the per-stage latency (ground / retrieve / synth / total ms), so you can see the pipeline that produced the answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoValid-time cursor (RFC 3339): retrieval reflects facts true at this instant
graphNoGraph to target; defaults to the connection's graph
top_kNoMax citations to return (default 8)
branchNoBranch to target; defaults to the connection's branch
detailNoResponse detail level. Defaults to compact.
executeNoRun retrieval and answer (default true); false returns only the grounding
questionYesThe natural-language question to answer from the graph
as_of_commit_seqNoSnapshot pin: retrieval and citations reproduce the graph as of this commit sequence
Behavior4/5

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

Annotations indicate readOnlyHint=true, and the description adds transparency about vocabulary snapping to real terms, pinned snapshot retrieval, mode behavior, and the explanation block with latency metrics. No contradictions.

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 well-organized in two paragraphs, front-loads the main purpose, and includes details without being verbose. It earns its 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 8 parameters and no output schema, the description covers the tool's purpose, parameter semantics, behavioral details, and response structure (explain block). It is sufficiently complete for an LLM agent to use correctly.

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

Parameters4/5

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

Schema coverage is 100%, and the description adds meaning beyond the schema, such as explaining that question is natural-language, top_k is max citations, execute false returns grounding, and mode details (resident_planner vs grounding_only).

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 clearly states the tool's purpose: 'Ask a natural-language question about the graph and get a grounded answer with citations.' It distinguishes from the sibling lbb_search by advising preference when a direct, cited answer is wanted rather than a ranked list.

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 description explicitly recommends when to use this tool over lbb_search and explains the execute parameter for retrieving grounding only. It could be more detailed about scenarios to avoid, but it provides clear 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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