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littlebigbrains

@littlebigbrain/mcp

lbb_commit

Idempotent

Commit graph updates: add triplets, embeddings, and properties; retract wrongly added edges or entities; and store search relevance labels.

Instructions

Write graph facts, retract them, or label ranked 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 stores query/result relevance labels (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.
Install Server

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, the description discloses meaningful behavioral details: explicit idempotency_key wins, otherwise MCP derives a content hash for dedupe; facts mode defaults edge_idempotency to append; skip_unchanged is recommended for re-runnable backfills; retract removes current edges while keeping history for as-of reads. These details align with the idempotentHint and destructiveHint annotations rather than contradicting them.

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?

The description is dense but efficient: four sentences cover all three modes, idempotency behavior, feedback grading, and backfill guidance. Every sentence carries operational value, and the most important mode distinction is front-loaded.

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?

For a 12-parameter, three-mode mutation tool with no output schema, the description covers mode selection, idempotency, retract semantics, and backfill behavior well. It does not broadly describe the response/return shape outside a mention of written_properties in the entity_properties parameter, so a small completeness gap remains.

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?

With 58% schema coverage, the description compensates for several sparse parameters by explaining mode semantics, idempotency_key behavior, edge_idempotency defaults, observed_at backfill usage, and feedback grade meanings. Some parameters like graph, branch, and triplets still rely on the schema, but the description adds substantial meaning beyond the raw JSON 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 a specific verb and resource: 'Write graph facts, retract them, or label ranked results.' It then enumerates the three modes (facts, retract, search_feedback) with concrete actions, making the tool's purpose unmistakable and clearly distinct from read-only siblings like lbb_query and lbb_inspect.

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 gives clear mode-level guidance: use facts for writing triplets/embeddings/properties, retract for removing wrongly-added facts without a full reset, and search_feedback for storing relevance labels. It does not explicitly name sibling alternatives or say when not to use the tool, but the mode selection guidance is strong enough for an agent to choose correctly.

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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