About Loanguard AI
Loanguard AI is an MCP server in the RAG category: agentic GraphRAG system for loan compliance monitoring and financial crime investigation in Australian financial services. Built on Neo4j, Claude, and APRA prudential standards. Features a multi-agent pipeline, three-layer knowledge graph architecture, and full reasoning chains with cited regulatory evidence.
Install
git clone https://github.com/emillaurence/loanguard-aiThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (6)
detect_graph_anomaliesRuns one or more named anomaly detection patterns from `ANOMALY_REGISTRY`. Accepts a list of pattern names and an optional `entity_id` to scope results.
Parameters not published.
evaluate_thresholdsEvaluates a list of `Threshold` nodes against an entity's stored property values. Uses `threshold_type` to determine whether each threshold results in PASS, BREACH, TRIGGER, or N/A, and filters out `informational` thresholds from verdict logic.
Parameters not published.
persist_assessmentIdempotent MERGE to Layer 3. Creates `Assessment`, `Finding`, and `ReasoningStep` nodes linked to the entity and regulation. Returns the `assessment_id`.
Parameters not published.
retrieve_regulatory_chunksSemantic search over `Chunk` nodes using OpenAI `text-embedding-3-small` embeddings and cosine similarity. Accepts an optional `regulation_id` filter and returns top-k chunks with similarity scores.
Parameters not published.
trace_evidenceWalks a stored `Assessment` back through its `ReasoningStep` nodes to retrieve all cited `Section` and `Chunk` nodes. Used by the Orchestrator to populate `cited_sections` and `cited_chunks`.
Parameters not published.
traverse_compliance_pathCross-layer L1 to L2 traversal via the Jurisdiction bridge. Returns the full regulatory subgraph for an entity, including all thresholds with their `threshold_type` field.
Parameters not published.
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