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littlebigbrains

@littlebigbrain/mcp

lbb_search

Read-only

Search a Little Big Brain knowledge graph with natural language queries. Combine multiple phrasings to fuse results via reciprocal-rank fusion, or follow bounded graph paths from seed entities.

Instructions

Natural-language retrieval over Little Big Brain. Use query for one phrasing, queries for reciprocal-rank fusion across phrasings, and follow_paths: true when you want bounded graph paths from text-resolved seed entities. When you can judge returned results, call lbb_commit mode=search_feedback with good=3, partial=1, bad=0 so Little Big Brain can build customer-specific qrels for embedding training.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
as_ofNoValid-time cursor (RFC 3339): results reflect facts true at this instant
graphNoGraph to target; defaults to the connection's graph
queryNoNatural-language query
top_kNo
branchNoBranch to target; defaults to the connection's branch
detailNoResponse detail level. Defaults to compact.
profileNo
queriesNoMultiple phrasings to fuse
max_hopsNo
directionNo
follow_pathsNo
as_of_commit_seqNoSnapshot pin: results reproduce the graph as of this commit sequence (echoed back in snapshot.as_of_commit_seq); a pin past head is an error
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description's behavioral additions are valuable. It explains that follow_paths activates bounded graph paths and that feedback can be sent via lbb_commit. It does not disclose potential side effects like performance or rate limits.

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?

Three sentences, dense and informative with no wasted words. Front-loads core purpose and usage patterns.

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 complexity (13 params, no output schema), the description covers main usage patterns and feedback use-case. However, it lacks explanation of return format or error handling. Adequate for a search tool.

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 description coverage is 54%, moderate. The description adds meaning beyond schema by explaining how to use query, queries, and follow_paths. It does not cover all 13 parameters but adds significant value for key ones.

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 it is for 'Natural-language retrieval over Little Big Brain' and distinguishes between using query, queries, and follow_paths for different retrieval modes. It also differentiates from siblings by mentioning feedback via lbb_commit.

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 provides explicit guidelines on when to use query, queries, and follow_paths. It also suggests using lbb_commit for feedback. However, it does not specify cases when the tool should not be used or alternatives.

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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