mcpField
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officegrep

officegrep

Local
github.com/AsgoresmilelongSearch0Updated 2w ago

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

  1. 01Index Word, PowerPoint, Excel, PDF, text, and Markdown
  2. 02Search with SQLite FTS5
  3. 03Return precise locations and snippets
  4. 04Update indexes incrementally
  5. 05Watch folders for file changes

Use cases

  1. 01Find a phrase in office documents
  2. 02Locate a result by page, slide, or spreadsheet cell
  3. 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.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbofficegrep-0.1.3.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

officegrep

ci license: MIT python MCP

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 / ripgrep only read plain text. .docx is a zip of XML, .xlsx keeps strings in a shared table, .pdf is 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"]
    }
  }
}
ToolWhat it does
search_docsFull-text search → file path, locator (p 42, slide 3, Sheet1!12, page 7), highlighted snippet
list_docsIndexed files with block counts
index_folderBuild / 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 trigram tokenizer: substring matching for both CJK and Latin text, no word segmenter required, ranked by bm25().
  • 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:

MetricValue
Files43 (docx / pptx / xlsx / md, incl. an 8.6 MB deck)
Text blocks2,260
First index2.16 s
Database1.9 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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