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cuba_faro

Search a knowledge graph to ground responses with evidence, returning confidence scores. Supports temporal filtering, session-aware retrieval, and optional diversification.

Instructions

Search memory BEFORE answering to ground responses. Returns grounding scores. Mode 'verify' checks claims against evidence (confidence: verified/partial/weak/unknown). Session-aware: boosts results matching active session goals. Supports temporal filtering. v0.9: optional MMR diversification + OOD abstention + exact tiktoken-based budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoSearch mode (default: hybrid). 'verify' checks if claim is grounded.
tagsNoFilter observations by tag keyword (exact match against auto-extracted tags)
afterNoISO8601 datetime — return results created after this time
limitNoMax results (default 10, max 50)
queryYesSearch text
scopeNoWhere to search (default: all)
beforeNoISO8601 datetime — return results created before this time
formatNoResponse format. compact (DEFAULT): abbreviated keys — e=entity, c=content, t=type, i=importance, s=score. 71% fewer tokens (798 vs 2787 at limit=10, measured). verbose: full key names, only when you need every field.
rerankNov0.9.2: cross-encoder rerank top-50 → top-K with bge-reranker-v2-m3 (Xiao 2023). Auto-enabled when CUBA_RERANKER_PATH points to a valid ONNX. Identity fallback otherwise.
diversifyNov0.9: post-RRF MMR pass that penalizes near-duplicates among top-K. Default false.
max_tokensNoToken budget for results (default 5000). Counted exactly via tiktoken cl100k_base.
mmr_lambdaNov0.9: MMR balance — 1.0 pure relevance, 0.0 pure diversity. Default 0.7.
abstain_oodNov0.9: abstain (return empty results with abstain_reason) when query is out-of-distribution via Mahalanobis distance. Default false.
associativeNov0.11: multi-hop expansion (HippoRAG-style). Seeds spreading activation from query-matched entities and pulls in observations on graph-connected entities that no lexical/vector signal surfaced. Additive — never lowers a base hit. Measured +10pts recall@10 on the smoke set. Default false.
enable_bm25Nov0.9: enable BM25 (ts_rank_cd) as third RRF signal alongside text + vector. Catches queries with rare terms that dense embeddings miss. Default true.
ood_thresholdNov0.9: Mahalanobis distance threshold for abstention. Defaults to sqrt(chi2_0.99(d)), which scales with the embedding dimension (~21.25 for d=384). Override only if you calibrated on your own corpus.
Behavior4/5

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

With no annotations provided, the description adequately discloses behavioral traits: it is a search (read-only) operation, returns grounding scores, supports temporal filtering, and details version-specific features. It does not mention authentication or rate limits, but these are less critical for a search tool.

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 concise at 4-5 sentences, front-loaded with the primary purpose, and efficiently lists key features and version numbers without extraneous information.

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 16 parameters and no output schema, the description covers the essential behavior, including temporal filtering, modes, and versioned features. It could briefly mention the return structure (e.g., list of results) but overall is fairly complete.

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?

The description adds significant value beyond the 100% schema coverage by explaining parameter contexts (e.g., '71% fewer tokens' for format, 'cross-encoder rerank' for rerank, defaults and version details). This enriches understanding.

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 core function: 'Search memory BEFORE answering to ground responses.' It specifies modes ('verify') with confidence levels, session-aware boosting, and temporal filtering, distinguishing it 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 provides clear context for use (grounding responses) and explains the primary modes. However, it lacks explicit guidance on when not to use this tool versus siblings, which would be helpful given the large sibling list.

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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