About officegrep
officegrep indexes text from office documents and PDFs in a local SQLite database for agents and CLI users. Its full-text search returns file paths, precise document locations, and snippets, without relying on an embedding service.
Key features
- 01Index Word, PowerPoint, Excel, PDF, text, and Markdown
- 02Search with SQLite FTS5
- 03Return precise locations and snippets
- 04Update indexes incrementally
- 05Watch folders for file changes
Use cases
- 01Find a phrase in office documents
- 02Locate a result by page, slide, or spreadsheet cell
- 03Keep a local document index updated
MCP support
The MCP server exposes `search_docs`, `list_docs`, and `index_folder` tools over stdio, enabling clients to search and manage the local index.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEofficegrep
Full-text search inside Word, PowerPoint, Excel and PDF — for agents.
grep cannot see a single character inside a .docx. officegrep extracts the text, indexes it into one SQLite file, and hands it to any agent over MCP.
$ officegrep search "bending moment"
Found 3 hits:
F:\reports\pole-design.docx [table p 89]
design bending moment (load point 0.25 m from tip); deflection limit; test moment = 2× …
F:\notes\composites.md [line 32]
… utilization 81%. **test bending moment = 2× the standard test moment** …
中文说明:README.zh-CN.md
Why
grep/ripgreponly read plain text..docxis a zip of XML,.xlsxkeeps strings in a shared table,.pdfis a compressed stream.- Generic RAG needs an embedding service, a vector store, and a network round-trip.
- This needs one SQLite file: offline, incremental, milliseconds.
Install
# one command, no clone, no uv
pip install https://github.com/Asgoresmilelong/officegrep/releases/download/v0.1.1/officegrep-0.1.1-py3-none-any.whl
# or from source
pip install git+https://github.com/Asgoresmilelong/officegrep
Runtime deps: lxml, openpyxl; PDF support adds optional pypdf.
Listed in the official MCP Registry as io.github.Asgoresmilelong/officegrep; there is also an .mcpb bundle on the releases page for MCP hosts that install bundles (needs uv).
CLI
officegrep index ~/Documents ~/Downloads # build / update (incremental)
officegrep search "island reef" --limit 5
officegrep search "wind load" --under ~/Documents
officegrep stats
The index lives in ~/.officegrep/index.db; override with --db or OFFICEGREP_DB.
First run
Nothing to do. If the index is empty when officegrep mcp starts, it scans ~/Documents, ~/Downloads and ~/Desktop in the background (25 MB per-file cap) while the agent keeps talking; a search_docs call made in the meantime answers with a "still building" note instead of a misleading "no results".
- opt out:
OFFICEGREP_AUTOINDEX=0 - scan other folders:
OFFICEGREP_ROOTS="~/work;~/papers"
Stay in sync
officegrep watch ~/Documents # re-index on change: 2s poll, no extra dependency
officegrep watch ~/Documents --once # single sync pass (cron / CI friendly)
Or let the MCP server watch in the background:
OFFICEGREP_WATCH="~/Documents;~/Downloads" officegrep mcp
The index runs in WAL mode, so a watch process and an MCP server can share one database file concurrently — something that fails with database is locked on a default rollback journal.
MCP server
officegrep mcp
Client config (Claude Desktop / Cursor / Codex / any MCP host):
{
"mcpServers": {
"officegrep": {
"command": "officegrep",
"args": ["mcp", "--db", "/Users/me/.officegrep/index.db"]
}
}
}
| Tool | What it does |
|---|---|
search_docs | Full-text search → file path, locator (p 42, slide 3, Sheet1!12, page 7), highlighted snippet |
list_docs | Indexed files with block counts |
index_folder | Build / refresh the index (incremental) |
The protocol is a hand-written JSON-RPC over stdio — no MCP SDK dependency.
How it works
docx / pptx → zipfile + lxml, read the XML directly
xlsx → openpyxl (read_only)
pdf → pypdf (optional)
txt / md → line chunks, line numbers kept
- Every block carries a human-readable locator, because the first question an agent asks is "where?".
- The index uses SQLite FTS5 with the
trigramtokenizer: substring matching for both CJK and Latin text, no word segmenter required, ranked bybm25(). - Queries of 1–2 characters cannot match trigrams, so they fall back to a scan ranked by hit count (CJK queries are frequently two characters).
- Incremental: compare
(size, mtime)first, hash with SHA-1 only when they differ.
Benchmark on a real engineering corpus:
| Metric | Value |
|---|---|
| Files | 43 (docx / pptx / xlsx / md, incl. an 8.6 MB deck) |
| Text blocks | 2,260 |
| First index | 2.16 s |
| Database | 1.9 MiB |
Limits
- No OCR: scanned PDFs yield nothing.
- Literal search, not semantic — "tower" will not find "pole" unless both appear.
- 1–2 character queries scan the table; slow past a few hundred thousand blocks.
- Indexes body text only: no comments, tracked changes, headers or footers.
License
MIT
Comments
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