precis-mcp 3.1.0

MCP server for structured document access — read, write, search, and annotate DOCX, LaTeX, Markdown, and scientific papers with minimal token usage

Precis — Structured Document MCP Server

Stop burning tokens on raw documents. Precis is an MCP server that gives AI agents structured, token-efficient access to DOCX, LaTeX, Markdown, and scientific papers — read, write, search, and annotate through four simple tools.

Instead of dumping a 100k-token PDF into your context window, Precis lets your agent navigate to exactly the section it needs, read just that chunk, and move on. One MCP server replaces the PDF-in-context anti-pattern that burns through Claude, ChatGPT, and Cursor usage limits in minutes.

Why Precis?

• Slash context bloat — Navigate by heading, grep for keywords, or read specific paragraphs by slug. No more feeding entire documents into the context window.
• Write back, not just read — Edit DOCX with tracked changes, insert LaTeX sections, append Markdown — all through the same put() tool. Your agent becomes a true co-author.
• Semantic search over papers — Vector search across thousands of ingested PDFs. Get the relevant chunk, not the whole paper.
• One server, every format — DOCX, LaTeX (multi-file projects), Markdown, plaintext, and scientific papers. No format-specific plugins to juggle.
• Token-aware output — Automatic RAKE keyword summaries for large documents. Depth filtering, pagination, and adaptive truncation keep responses lean.
• Plugin architecture — Add custom document types or URI schemes via Python entry points.

Works with

Claude Desktop · Cursor · Windsurf · Cline · any MCP-compatible client

Quick start

pip install precis-mcp          # core: Markdown, plaintext, LaTeX
pip install precis-mcp[word]     # + Word DOCX support
pip install precis-mcp[paper]    # + scientific paper library
pip install precis-mcp[all]      # everything

Add to your MCP client config:

{
  "mcpServers": {
    "precis": {
      "command": "precis"
    }
  }
}

That's it. Your agent now has structured document access.

Four tools, zero sprawl

Tool	What it does
get(id)	Read any document node — heading, paragraph, table, figure, chunk, citation
put(id, text, mode)	Write, replace, delete, annotate, or comment on any node
search(query)	Semantic search across your paper library or grep within a document
move(id, after)	Reorder sections and paragraphs within a document

get — Structured reading

get(id='report.docx')                    # table of contents with slugs
get(id='report.docx›PLXDX')             # read specific paragraph by slug
get(id='report.docx', grep='methods')    # find all nodes matching 'methods'
get(id='report.docx', depth=2)           # outline: H1 + H2 only
get(id='wang2020state')                  # paper overview + abstract
get(id='wang2020state›38')              # read chunk 38 of a paper
get(id='wang2020state›38..42')          # read chunk range
get(id='wang2020state/cite/bib')         # BibTeX citation
get(id='wang2020state/fig/3')            # figure 3 with caption
get(id='doi:10.1021/jacs.2c01234')       # lookup by DOI
get(id='arxiv:2301.12345')               # lookup by arXiv ID
get(grep='year:2020-2024')               # filter papers by date

put — Write and annotate

put(id='report.docx', text='## Methods\n\nWe used...', mode='append')
put(id='report.docx›PLXDX', text='Revised paragraph.', mode='replace')  # tracked changes
put(id='report.docx›PLXDX', text='Needs citation.', mode='comment')     # margin comment
put(id='report.docx›PLXDX', mode='delete')
put(id='wang2020state', note='Key finding about selectivity')            # paper annotation
put(id='wang2020state', link='jones2023surface:cites')                   # link papers

search — Find what matters

search(query='CO2 capture metal-organic frameworks')         # semantic search
search(query='selectivity', scope='wang2020state')           # search within one paper
search(query='thermal stability', scope='chapter3.tex')      # search within a doc

Supported formats

Format	Read	Write	Track changes	Comments	Extras
DOCX	✓	✓	✓	✓ margin comments	Citations, tables, lists, bibliography
LaTeX	✓	✓	—	—	Multi-file projects, .bib parsing, equations, figures, raw file access
Markdown	✓	✓	—	—	Headings, code blocks, tables, lists. Zero deps
Plaintext	✓	✓	—	—	Paragraph-based. Zero deps
Papers	✓	notes	—	—	Semantic search, figures, citations, Semantic Scholar graph

URI grammar

id = path[›selector][/view[/subview]]

Precis auto-detects the scheme from the identifier:

• File extension (.docx, .tex, .md, .txt) → file handler
• doi:, arxiv: prefix → paper lookup
• Bare DOI pattern (10.1234/...) → auto-detected
• Everything else → paper slug

How it saves tokens

The problem: Feeding a raw 4,500-word PDF to Claude burns ~100,000 tokens. Every follow-up message resends the entire conversation history, compounding the waste. This is the #1 cause of hitting usage limits fast.

The fix: Precis parses documents into a navigable tree of headings, paragraphs, tables, and figures. Your agent reads the table of contents (tiny), drills into the section it needs (small), and gets exactly the paragraph it wants (minimal). Total tokens: a fraction of the raw dump.

Approach	Tokens for a 30-page paper
Raw PDF in context	~100,000
Precis: TOC → section → paragraph	~2,000–5,000

For large documents (100+ nodes), Precis automatically returns headings-only and lets the agent drill in. RAKE keyword extraction provides compressed summaries for scanning without reading full text.

Output markers

Every line of output is prefixed with a provenance marker:

Marker	Meaning	Safe to quote?
=	Verbatim text from the document	✓
~	Derived (keywords, summary)	✗
%	Annotation (user note or comment)	context-dependent

Plugin system

Extend Precis with new document types or URI schemes:

[project.entry-points."precis.schemes"]
chem = "my_plugin:ChemHandler"

[project.entry-points."precis.file_types"]
".sdf" = "my_plugin:SDFParser"

Implement the Handler protocol (just a read() method) and register via entry points. Precis discovers plugins at startup.

License

GPL-3.0-or-later