docs-decomposer
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Docs Decomposer
TypeScript PDF document processor with a token-efficient MCP server.
Extracts PDF text with PDF.js and builds a nested section tree from the table of contents and bookmarks.
Exposes documents through MCP tools with paged outlines, section search, and capped section reads, so an agent never has to ingest a whole PDF at once.
Includes a terminal browser for opening PDFs in
docs/and paging through long section previews.Supports absolute or allowed-root-relative paths, project root registration, and optional resources and prompts.
Quick start
npm install
npm test
npm run build
DOCS_DECOMPOSER_ROOTS="$PWD/docs" npm run mcp
npm run tuiRelated MCP server: jdocmunch-mcp
MCP client configuration
This is the form used by the project's .mcp.json:
{
"mcpServers": {
"docs-decomposer": {
"command": "sh",
"args": ["-c", "exec npm --prefix \"$DOCS_DECOMPOSER_HOME\" run mcp"],
"env": {
"DOCS_DECOMPOSER_HOME": "/path/to/docs_Decomposer",
"DOCS_DECOMPOSER_ROOTS": "/path/to/documents"
}
}
}
}DOCS_DECOMPOSER_HOME is expanded by sh to locate the project; DOCS_DECOMPOSER_ROOTS is the document directory the server is allowed to read.
A simpler form without the shell wrapper:
{
"mcpServers": {
"docs-decomposer": {
"command": "npm",
"args": ["--prefix", "/path/to/docs_Decomposer", "run", "mcp"],
"env": {
"DOCS_DECOMPOSER_ROOTS": "/path/to/documents"
}
}
}
}See USAGE.md for tool flows, pagination, resources, prompts, root safety, and the register_root workflow.
Commands
Command | Purpose |
| Print the full Markdown extraction to stdout |
| Start the stdio MCP server |
| Browse |
| Run the test suite |
| Type-check the project |
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Generate and read PDFs for AI agents: a generate_pdf and a read_pdf tool, priced per document.
Parse, extract, split, and ask over digital PDFs (text layer, no OCR) from Cursor and Claude.
Extract PDFs to Markdown, RAG chunks and cited tables; publish tracked Doc Links with read stats.
Query your own PDFs and documents from any MCP client. Every answer cites the page it came from.
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables AI agents to efficiently process large local and online PDFs through selective extraction of text, images, and metadata. It provides tools for content search and document outline navigation to optimize context window usage.716ISC
- FlicenseAqualityAmaintenanceLets AI agents navigate documentation by section instead of reading files by brute force, reducing token usage and improving precision.642,599 PyPI207-
- AlicenseAqualityBmaintenanceEnables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.7MIT
- AlicenseNot gradedqualityCmaintenanceServes read-only tools that let an agent inspect PDFs step by step: outline a document, search for text or figure values, read pages and tables in order, describe figures and chart data, and render page or block images, with every block cited to its page and bounding box. It also lists the PDFs available under a configured root folder, which it never reads outside of.MIT