grp-kb 0.1.3

Semantic search MCP server for an Acumatica KB, single multilingual index, no Obsidian/Smart Connections required

grp-kb

Semantic search MCP server for an Acumatica knowledge base. Single index over one vault of markdown notes, embedded with the multilingual paraphrase-multilingual-MiniLM-L12-v2 — handles both English content and Bahasa Malaysia content (e.g. GRP manuals) well, so a query in either language can find the right doc in either language.

No Obsidian, no Smart Connections plugin, anywhere in this pipeline — the index is built directly from plain .md files with sentence-transformers.

This package ships code only. It needs a real, pre-built vault + index pair to do anything useful — either build your own (see below) or get one shared from someone who already has it (just the .md vault + kb_index.npy / kb_meta.json, no re-embedding needed on the receiving end).

Note on mixed-content corpora: if your vault has a small, distinctly-tagged subset (e.g. guide: GRP-Manual in frontmatter) mixed into a much larger general corpus, that subset can get outranked by volume on any topic that exists in both — unfiltered search may return zero of the small subset's results even in the top 15, regardless of query wording. Use guide_filter to force results from that subset when you know that's what you want; the search_kb tool description already tells a calling LLM when to do this.

Build an index

export KB_VAULT_DIR=/path/to/vault
export KB_MCP_INDEX_DIR=/path/to/index
python -m grp_kb.build_index

Optional env vars: KB_FILE_GLOB (default *.md) to scope which files get indexed, KB_EMBED_MODEL (default paraphrase-multilingual-MiniLM-L12-v2) to use a different sentence-transformers model.

Run the server

export KB_VAULT_DIR=/path/to/vault
export KB_INDEX_DIR=/path/to/index
grp-kb

Register with Claude Code

claude mcp add grp-kb -s user \
  -e KB_VAULT_DIR=/path/to/vault \
  -e KB_INDEX_DIR=/path/to/index \
  -- grp-kb

Tools

• search_kb(query, top_k=10, source_only=False, guide_filter="") — returns a JSON array of results (score, path, heading, title, breadcrumb, forms, guide, snippet).
• read_kb_file(path) — full markdown content by relative path (as returned by search_kb).