The problem
Enterprise AI systems need to answer from specific knowledge — processes, tables, rules, support history. Without a unified index, every agent re-scans on its own, misses recency and hallucinates vocabulary.
The core
senior-mcp indexes structured knowledge (HCM/ERP data dictionaries, screens, tables) and unstructured knowledge (tickets, forum) in a single PostgreSQL base with pgvector. Search combines three stages:
- Vector (75%) — semantic similarity over embeddings.
- Text (25%) — term matching, catching the exact vocabulary embeddings miss.
- Rerank — the top-25 goes through a reranker (Voyage) before returning to the agent.
The catalog
Access and integration
The service exposes 8 MCP tools — among them senior_search (hybrid search with rerank), senior_get_chunk (full context of a result), senior_catalog and senior_stats. It runs on Node.js behind a Cloudflare Tunnel (seniormcp.gbbragadev.com) with token authentication — secrets never reach the client.
Real consumption
Coding agents (Claude Code, Codex) use senior-mcp as a grounding tool daily — the query becomes part of the agent’s flow, not a documentation tab. In Braga Suite, it is the knowledge layer designed into the BFF, server-side and allowlist-gated — live querying is under controlled validation.
My role
Hybrid indexing concept, rerank architecture, PostgreSQL/pgvector integration, MCP contract design and service operation.
senior-mcp is independent infrastructure. Brands mentioned belong to their respective owners.