Production-Grade Hybrid RAG with Intelligent Routing. Provides hybrid search (Vector + BM25), intelligent query routing across 6 specialized routes, multimodal image processing, visual search, cross-encoder reranking, and recursive chunking.
Server exposes 22 tools with significant definition gaps. Most tools lack proper input schemas (only 6 of 22 have visible, complete JSON Schema definitions with types and descriptions). Descriptions are present but often generic (avg ~80 chars, below 194-char baseline). Parameter descriptions are sparse or missing entirely. No output schemas documented. Error handling is absent from tool definitions. Tool naming is inconsistent: some use verb_noun (search, ingest), others use REST paths (documents/:id, license/validate). Several tools combine multiple concerns (ingest/batch, reflect/batch, mcp/tools + mcp/call). No evidence of idempotency, pagination, or recovery guidance in definitions.
Cost analytics and projections for different routing strategies
Analyze image using Llama 4 Scout vision model
Run benchmark tests on search routes
Get benchmark results
Test intent classification for query routing
Delete document by ID with tenant-aware access control
Update document metadata without re-embedding
Find visually similar images by uploading a query image
REST-style path parameters (documents/:id, license/validate) violate verb_noun naming convention. LLMs cannot infer action from path syntax alone. Should be delete_document, update_document_metadata, validate_license, etc.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields (e.g., does search return document_id, chunk_id, both?). Breaks tool chaining.
Multiple tools combine unrelated concerns: ingest/batch (ingestion + concurrency), reflect/batch (reflection + sampling), mcp/tools + mcp/call (discovery + execution). Each should be a separate tool for composability.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 53 | 2026-07-28+ | v2 |
Ingest document with auto-chunking, embedding generation, and vector indexing
Ingest image with AI-generated description using Llama 4 Scout multimodal processing
Batch ingest up to 100 documents with concurrency control
Create license (admin only)
List all licenses (admin only)
Revoke a license (admin only)
Validate a license key
Legacy: Execute MCP tool (JSON-RPC)
Legacy: List MCP tools (JSON-RPC)
OpenAPI 3.0 specification for the API
Public statistics endpoint for live system verification (no auth required)
Generate reflections for a random sample of un-reflected documents
Hybrid search (query, topK, rerank) with V4 intelligent routing (SQL/BM25/VECTOR/GRAPH)
Get index statistics including document count, reflection count, and model information
No error handling guidance in tool definitions. Descriptions do not explain what to do on failure (retry? ask user? fatal?). No recovery hints (e.g., 'If license validation fails, try license/list to see available keys').
Destructive tools (documents/:id delete, license/revoke) lack confirmation or dry-run support. No mention of idempotency or rollback. Agents could accidentally delete data without safeguards.