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@littlebigbrain/mcp

lbb_commit

Idempotent

Write triplets, entity properties, and embeddings to a knowledge graph. Retract incorrect facts or label search result relevance, with idempotent deduplication and backfill timestamps.

Instructions

Write graph facts, retract them, or label search results. mode=facts writes triplets/embeddings/properties; mode=retract removes a wrongly-added fact (by edge or by entity) without a full reset; mode=search_feedback labels query/result relevance after lbb_search (Feedback grades: 3=ideal/good, 1=partial, 0=bad; include query, search_id when available, target, rank, score). Explicit idempotency_key wins; when omitted, MCP derives a stable content hash so content-identical retries dedupe. Facts mode defaults edge_idempotency to append; pass skip_unchanged for re-runnable backfills.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
graphNoGraph to target; defaults to the connection's graph
branchNoBranch to target; defaults to the connection's branch
tripletsNo
observed_atNoBackfill timestamp (RFC3339). Records this commit AS OF that instant: stamps transaction time and defaults each triplet's valid_time.start. Replay history in order with observed_at per commit so as-of reads by date work. Omit for live writes.
retract_edgesNomode=retract: specific edges to remove, matched by (source, relation, target).
idempotency_keyNo
search_feedbackNo
edge_idempotencyNoDefaults to append in MCP. Use skip_unchanged for backfills; it skips exact current-edge duplicates and drops evidence-only repeats.
retract_entitiesNomode=retract: entities whose every current edge is removed (a current-state tombstone; the record and its history are kept for as_of reads).
entity_embeddingsNo
entity_propertiesNoTyped scalar attributes per entity. Each item is { type, name, properties }. `properties` is a flat map of field -> value, e.g. { "type": "PERSON", "name": "Ada Lovelace", "properties": { "h_index": 52, "title": "VP", "last_contact": "2026-06-26" } }. Values are coerced to each field's declared type, so a string like "2026-06-26" lands in a date_time field and "52" in an i64 field. (The verbose form [{ field, value: { i64: 52 } }] is also accepted.) Register a field first with lbb_configure evolve_ontology add_property; the commit response echoes written_properties so you can confirm what landed.
Behavior4/5

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

Annotations indicate idempotent=true and not read-only. The description adds details on idempotency key derivation, edge_idempotency defaults, and that retraction is a tombstone (history kept). No contradictions. Could mention transactional scope but adequate.

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

Conciseness3/5

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

The description is a single dense paragraph covering many details. While comprehensive, it lacks structured formatting (e.g., bullet points or sections) which would improve readability for an AI agent. It is moderately concise but not optimally structured.

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 tool's complexity (12 parameters, nested objects, no output schema), the description covers modes, key parameters, idempotency strategy, and retraction behavior. Missing details on return values and error handling, but reasonable for a commit tool.

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?

With 58% schema coverage, the description compensates by explaining mode enums, retract_edges vs. retract_entities semantics, observed_at timestamp usage, search_feedback grade meanings (3=ideal, etc.), and entity_properties format with examples. This provides crucial context beyond the 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 clearly states the tool's three modes: writing graph facts, retracting them, and labeling search results. Each mode is described with specific actions, making the tool's purpose unambiguous and distinct from sibling tools.

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 explains when to use each mode (e.g., retract for removing wrongly-added facts, search_feedback for labeling after lbb_search) and provides guidance on idempotency. However, it does not explicitly state when not to use this tool or compare it to siblings, which are already distinct.

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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