bitwise-embedded-docs

bitwise-mcp

MCP server for embedded developers. Ingests PDF reference manuals (1000+ pages), extracts register definitions, and provides fast semantic search. Built with FastMCP and available as a Claude Code plugin.

Features

• PDF Ingestion - Parses large reference manuals preserving structure
• Register Table Extraction - Detects and converts register definitions to structured JSON
• Hybrid Search - Combines keyword matching (SQLite FTS5) with semantic similarity (FAISS)
• Context-Aware Chunking - Chunks include section hierarchy prefixes (e.g. [Manual > FlexCAN > MCR Register]) for better retrieval
• Sentence-Aware Splitting - Text splits on sentence boundaries with 1-2 sentence overlap, never mid-word
• Compact Output - Formats responses to minimize token usage

Installation

Option 1: Claude Code Plugin (Recommended)

Install directly from the Claude Code plugin marketplace:

claude plugin add bitwise-embedded-docs

This registers the MCP server and adds /ingest-docs and /search-docs slash commands.

Option 2: Global Install

Install once, use across all projects:

# From this repository directory
pip install -e .

# Then in ANY project directory where you want to use it
claude mcp add --scope project embedded-docs python -m mcp_embedded_docs

Each project maintains its own isolated documentation index. When you run the server in a project, it only indexes and searches PDFs in that project's docs/ directory.

Option 3: Poetry Install (Development)

poetry install

# Add to Claude Code
claude mcp add embedded-docs --command poetry --args "run" "mcp-embedded-docs" "serve" --cwd "<path-to-this-repo>"

Restart Claude Code after adding the server.

Usage

Place PDFs in a docs/ directory, then in Claude Code:

What PDFs are available?
Ingest any files that haven't been ingested yet
What's the base address for FlexCAN0?

Example: Checking Available PDFs

Example: Searching Documentation

The MCP server automatically queries the indexed documentation when you ask questions:

CLI Usage

poetry run mcp-embedded-docs ingest docs/manual.pdf --title "MCU Manual"
poetry run mcp-embedded-docs list  # View indexed documents

MCP Tools

Tool	Description
search_docs	Search documentation with hybrid keyword + semantic retrieval
find_register	Find specific register definitions by name
list_docs	List all documentation files with status (indexed + available)
ingest_docs	Ingest PDF documentation files into the search index
remove_docs	Remove documents from the search index by ID

Architecture

Built on FastMCP for the MCP server layer. The ingestion pipeline:

1. PDF Parsing (PyMuPDF) - Extracts text with layout, TOC, and section hierarchy
2. Table Detection (pdfplumber) - Identifies register maps, bitfield definitions, memory maps
3. Semantic Chunking - Leaf-only section chunking with contextual hierarchy prefixes, sentence-aware splitting, and content-based deduplication
4. Embedding (sentence-transformers, bge-small-en-v1.5) - Local embeddings, no API calls
5. Indexing (FAISS + SQLite FTS5) - Hybrid vector + keyword search

Tech Stack

Python 3.10+ | FastMCP | PyMuPDF | pdfplumber | sentence-transformers | FAISS | SQLite FTS5

Performance

Tested: S32K144 Reference Manual (2,179 pages, 14MB) Results: 3min indexing, <500ms search, ~500MB memory

License

MIT