⚠️ Tome — DEPRECATED

This package is deprecated. Use precis-mcp instead.

precis-mcp unifies paper reading (formerly tome-mcp / acatome-mcp) and manuscript editing into a single MCP server with 4 tools: search(), get(), put(), move().

pip install precis-mcp

---

Tome (archived)

A Python MCP server that manages a research paper library: PDFs, bibliography, semantic search, figure tracking, and Semantic Scholar integration.

No LLM inside — pure deterministic code. The AI client provides the intelligence; Tome provides the tools.

Developed and tested with Windsurf + Claude Opus 4.6 (thinking). Should work with any MCP-capable client and sufficiently capable model, but this combination is where the magic happens.

Installation

pip install tome-mcp

For development (tests, linting):

git clone https://github.com/retospect/tome-mcp.git
cd tome-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Dependencies

• chromadb — vector database for semantic search (includes built-in all-MiniLM-L6-v2 embeddings, no external server needed)
• PyMuPDF (fitz) — PDF text extraction
• bibtexparser ≥ 2.0 — BibTeX parsing and serialization
• httpx — HTTP client for CrossRef, Semantic Scholar, Unpaywall APIs
• mcp — Model Context Protocol SDK
• PyYAML — config file parsing

MCP configuration

Quickest setup — uses uvx to run without a manual venv:

{
  "mcpServers": {
    "tome": {
      "command": "uvx",
      "args": ["tome-mcp"],
      "env": {
        "TOME_ROOT": "/path/to/your/project",
        "SEMANTIC_SCHOLAR_API_KEY": "optional"
      }
    }
  }
}

Or point your MCP client at a local install:

{
  "mcpServers": {
    "tome": {
      "command": "/path/to/tome/.venv/bin/python",
      "args": ["-m", "tome.server"],
      "env": {
        "TOME_ROOT": "/path/to/your/project",
        "SEMANTIC_SCHOLAR_API_KEY": "optional"
      }
    }
  }
}

Alternatively, use set_root(path='...') at the start of each session.

Quick start: your first session

Once Tome is installed and your MCP client is configured, open your project in the IDE and type these prompts in order:

1. Orient

This is a LaTeX project using the Tome MCP server for paper management. Call guide('getting-started') to see the tool index, then set_root('/path/to/my/project') to connect.

2. Describe your project (so the LLM builds context)

The book/paper is about [your topic]. The main file is main.tex. Run toc() to see the document structure and paper() to see the library.

3. Ingest your first paper

I dropped a PDF in tome/inbox/. Ingest it and verify the DOI.

4. Search and cite

Find papers in our library about [topic] and show me relevant quotes.

5. Compile

Compile the document and check for warnings.

That's it. The LLM discovers Tome's tools via guide() and learns your project structure from the filesystem. From here, explore the built-in guides — call guide() with no arguments to see all topics.

Environment variables (all optional)

Variable	Default	Purpose
TOME_ROOT	(none)	Project root directory (alternative to set_root() or cwd)
SEMANTIC_SCHOLAR_API_KEY	(none)	Higher S2 rate limits
UNPAYWALL_EMAIL	(none)	Email for Unpaywall open-access PDF lookup

Directory layout

User-facing (git-tracked)

project-root/
├── tome/
│   ├── references.bib          # AUTHORITATIVE bibliography
│   ├── inbox/                  # Drop PDFs here for processing
│   ├── figures/                # Source figure screenshots
│   └── notes/                  # LLM-curated paper notes (authorYYYY.yaml)

Cache (gitignored, fully regenerable via tome:rebuild)

project-root/
├── .tome/
│   ├── tome.json               # Derived metadata cache
│   ├── staging/                # Ingest prep area (transient)
│   ├── raw/                    # Extracted text: raw/xu2022/xu2022.p1.txt
│   ├── chroma/                 # ChromaDB persistent storage (embeddings + index)
│   ├── corpus_checksums.json   # Checksum manifest for .tex/.py files
│   └── tome.json.bak           # Safety backup before each write

Data model

Durability tiers

Tier	Data	Location	Recovery
Source of truth	PDFs, figure screenshots	Vault (~/.tome-mcp/pdf/), tome/figures/	Unrecoverable
Self-contained archives	.tome HDF5 files	Vault (~/.tome-mcp/tome/)	Unrecoverable (contain text + embeddings)
Authoritative metadata	Bibliography	tome/references.bib	Git rollback
Derived cache	Everything else	.tome-mcp/	Rebuildable from .tome archives

.tome archives — HDF5, not zip

Each ingested paper produces a .tome file in the vault. These are HDF5 archives (opened with h5py, not zipfile). Each archive is fully self-contained:

import h5py, json
f = h5py.File('~/.tome-mcp/tome/x/xu2022.tome', 'r')
meta = json.loads(f['meta'][()])       # key, title, authors, year, doi, ...
pages = f['pages'][:]                  # extracted page text (one string per page)
chunks = f['chunks/texts'][:]          # chunked text for search
embeds = f['chunks/embeddings'][:]     # (N, 384) float32 vectors
f.attrs['content_hash']               # SHA256 of the source PDF
f.attrs['embedding_model']            # "all-MiniLM-L6-v2"
f.close()

All databases (catalog.db, ChromaDB) can be rebuilt from .tome files alone.

references.bib — authoritative

The bib file is the single source of truth for paper metadata. Tome parses it with bibtexparser and writes back using full parse-modify-serialize (not regex surgery). A roundtrip test (parse → serialize → parse → compare) runs before every write; if anything changed unexpectedly, the write aborts.

A .bak copy is made before every write.

x-fields (curated, survive .tome/ rebuild)

Field	Values	Meaning
x-pdf	true/false	PDF has been ingested (stored in vault)
x-doi-status	valid/unchecked/rejected/missing	DOI verification state
x-tags	comma-separated	Freeform tags for search filtering

Key format

authorYYYY[a-c]? — first author surname + publication year. Collisions get letter suffixes. Datasheets use manufacturer_partid. Patents use the patent number.

tome.json — derived cache

Rebuilt from references.bib + filesystem on rebuild. Contains expensive-to- derive operational state:

{
  "version": 1,
  "papers": {
    "xu2022": {
      "title": "...",
      "authors": ["Xu, Y.", "..."],
      "year": 2022,
      "doi": "10.1038/s41586-022-04435-4",
      "s2_id": "CorpusId:12345678",
      "s2_fetched": "2026-02-13",
      "citation_count": 47,
      "cited_by_in_library": ["chen2023"],
      "references_in_library": ["lambert2015"],
      "abstract": "...",
      "file_sha256": "a1b2c3...",
      "pages_extracted": 12,
      "embedded": true,
      "doi_history": [],
      "crossref_fetched": "2026-02-13T19:29:00Z",
      "figures": {
        "fig3": {
          "status": "captured",
          "file": "figures/xu2022_fig3.png",
          "page": 3,
          "reason": "QI transfer diagram",
          "requested": "2026-02-13",
          "captured": "2026-02-13",
          "_caption": "Conductance measurements...",
          "_context": [{"page": 1, "text": "As shown in Fig. 3..."}],
          "_attribution": "Reproduced from Xu et al. (2022), Figure 3"
        }
      }
    }
  },
  "requests": {
    "ouyang2025": {
      "doi": "10.1063/5.0xxx",
      "tentative_title": "Fano interference...",
      "reason": "PDF behind paywall",
      "added": "2026-02-13",
      "resolved": null
    }
  }
}

Fields prefixed with _ are derived (regenerable from raw text extraction).

DOI lifecycle

Status	Meaning	doi field
valid	CrossRef resolves, title/authors match	Present, verified
unchecked	DOI present, not yet verified	Present, unverified
rejected	Was wrong or hallucinated, DOI removed	Absent
missing	Never had a DOI	Absent

Transitions:

• Added with DOI → unchecked
• Added without DOI → missing
• unchecked + check_doi succeeds → valid
• unchecked + check_doi fails → rejected (DOI removed, history in tome.json)
• rejected + set_paper with new DOI → unchecked
• missing + set_paper with DOI → unchecked

Invariant: if x-doi-status = valid, the DOI is trustworthy.

Ingest pipeline

Two-phase commit

Phase 1: Prepare (writes only to .tome/staging/, reversible)

1. Copy PDF from inbox to .tome/staging/{key}/
2. Extract PDF metadata (title, authors from doc.metadata)
3. Extract first-page text (DOI regex, title heuristic)
4. If DOI found → query CrossRef → structured metadata
5. If no DOI but title found → query Semantic Scholar → metadata
6. Extract text page-by-page
7. Chunk (500 chars, 100 overlap, sentence boundaries)
8. Return proposal to LLM (suggested key, extracted vs API metadata)

The LLM reviews the proposal and confirms or corrects.

Phase 2: Commit (fast, ordered for crash safety)

1. Write bib entry to tome/references.bib (via bibtexparser)
2. Copy PDF to vault (~/.tome-mcp/pdf/), write .tome archive
3. Move staging artifacts → .tome-mcp/raw/, .tome-mcp/cache/
4. Upsert into ChromaDB
5. Update .tome/tome.json
6. Clean up staging dir

If commit fails partway: staging dir still exists, inbox file may already be gone but bib entry exists. rebuild reconciles.

Verification

The LLM performs title/author verification (not Tome). Tome extracts metadata from the PDF and from APIs, returns both to the LLM. The LLM handles fuzzy matching (encoding variants like ç/c, abbreviations, reordering).

Corpus indexing (.tex / .py files)

Separate from papers. Living documents that change frequently.

Sync model

sync_corpus or lazy sync on search_corpus:

1. Scan glob patterns (e.g. sections/*.tex)
2. Checksum each file (SHA256)
3. Compare against .tome/corpus_checksums.json
4. Changed files: delete old ChromaDB entries, re-chunk, re-embed, insert
5. Deleted files: remove from ChromaDB
6. New files: add to ChromaDB
7. Unchanged files: skip

ChromaDB collections: paper_pages, paper_chunks, corpus_chunks (separate).

MCP tools

Many formerly separate tools have been unified into multi-action tools. Call guide() for the full topic index, or guide('getting-started') for orientation.

Paper management

Tool	Description
paper	Unified: get/set/list/remove/request/stats. No args = library stats. key = metadata + notes. action='list' = browse.
ingest	Process inbox PDFs. Without confirm: proposes key + metadata. With confirm=True: commits to library + vault.
notes	Read/write/clear paper notes or file meta. Paper notes in tome/notes/, file meta in % === FILE META blocks.
link_paper	Link/unlink a vault paper to the current project. No args = list linked papers.

Search & navigation

Tool	Description
search	Unified search: scope (all/papers/corpus/notes) × mode (semantic/exact). Filters: key, keys, tags, paths.
toc	Document structure: locate (heading/cite/label/index/tree). Replaces old doc_tree, find_cites, list_labels.

Document analysis

Tool	Description
doc_lint	Structural issues: undefined refs, orphan labels, shallow cites, tracked patterns.
dep_graph	Labels, refs, cites for a single .tex file.
review_status	Tracked marker counts from tome/config.yaml patterns.
validate_deep_cites	Verify deep-cite quotes against source PDF text in ChromaDB.

Discovery & exploration

Tool	Description
discover	Unified: federated search (S2 + OpenAlex), citation graph, shared citers, refresh, stats, lookup.
cite_graph	S2 citation graph (who cites this paper, what it cites). Flags in-library papers.
explore	LLM-guided citation beam search — fetch, triage, expand, dismiss.

DOI & figures

Tool	Description
doi	Unified DOI management: verify, reject, list rejected, fetch open-access PDF (via Unpaywall → inbox).
figure	Request, register, or list figures. No args = list all.

Task tracking

Tool	Description
needful	List N most urgent tasks, or mark a task as done. Ranked by never-done > changed > overdue.
file_diff	Git diff annotated with LaTeX section headings.

Maintenance

Tool	Description
set_root	Switch project root. Scaffolds directories. Surfaces open issues.
reindex	Re-index papers, corpus files, or both. Rebuilds from vault archives.
guide	On-demand usage guides. Call without args for topic index.
report_issue	Log a tool issue to tome/issues.md (git-tracked).

Tool descriptions

Every tool has a carefully written MCP description (~100 words) using consistent terminology. Tool responses include a next_steps field when follow-up action is needed.

Terminology (used in all descriptions)

Term	Meaning
library	The collection of papers in tome/references.bib
key	The bib key, e.g. miller1999. Same as \cite{miller1999}
has_pdf	Whether a PDF has been ingested (exists in vault)
inbox	tome/inbox/ — drop PDFs here for processing

Error handling

All errors are specific exception classes with messages that tell the LLM what went wrong and what to do about it.

TomeError (base)
├── PaperNotFound          — key not in library
├── PageOutOfRange         — page N requested, paper has M pages
├── DuplicateKey           — key already exists
├── DOIResolutionFailed    — CrossRef error (404, 429, 5xx)
├── IngestFailed           — could not identify paper from PDF
├── BibParseError          — bib file could not be parsed
├── BibWriteError          — roundtrip test failed, write aborted
├── ChromaDBError          — search index init/query failed
├── ConfigError (base)     — project configuration issue
│   ├── ConfigMissing      — no tome/config.yaml found
│   ├── RootNotFound       — named root not in config
│   ├── RootFileNotFound   — root .tex file doesn't exist on disk
│   ├── NoBibFile          — no references.bib yet
│   ├── NoTexFiles         — tex_globs matched no files
│   └── UnpaywallNotConfigured — no email for Unpaywall API
├── APIError               — external API error (CrossRef, S2, Unpaywall)
├── TextNotExtracted       — paper exists but no raw text yet
├── FigureNotFound         — no such figure for paper
└── UnsafeInput            — path traversal or unsafe characters

Every error message includes: what happened, why, and what to do next.

Testing

• Every module gets a corresponding test_*.py
• Tests use small fixtures (2-entry bib, 1-page PDF mock)
• Error paths tested explicitly (more important than happy paths for MCP)
• External services (CrossRef, S2) are mocked
• Integration tests requiring live services marked @pytest.mark.integration
• pytest with no marks runs all unit tests (no network required)

Package structure

~/repos/tome/
├── pyproject.toml
├── README.md
├── LICENSE                      # AGPL-3.0
├── .gitignore
├── examples/
│   └── config.yaml              # Full config example (all features)
├── src/
│   └── tome/
│       ├── __init__.py
│       ├── __main__.py          # python -m tome.server entry point
│       ├── py.typed             # PEP 561 type marker
│       ├── server.py            # MCP server + tool handlers
│       ├── errors.py            # Exception hierarchy
│       ├── config.py            # Project config (config.yaml parsing)
│       ├── manifest.py          # tome.json read/write (atomic, backup)
│       ├── bib.py               # BibTeX parser + writer (bibtexparser)
│       ├── extract.py           # PDF text extraction (PyMuPDF)
│       ├── chunk.py             # Sentence-boundary overlapping chunker
│       ├── store.py             # ChromaDB management (built-in embeddings)
│       ├── checksum.py          # SHA256 file checksumming
│       ├── identify.py          # PDF identification + key generation
│       ├── crossref.py          # CrossRef API client
│       ├── semantic_scholar.py  # Semantic Scholar API client
│       ├── openalex.py          # OpenAlex API client
│       ├── unpaywall.py         # Unpaywall open-access PDF lookup
│       ├── http.py              # Shared HTTP client utilities
│       ├── figures.py           # Figure request/registration + caption extraction
│       ├── notes.py             # Paper notes (YAML + ChromaDB indexing)
│       ├── issues.py            # Issue tracking (tome/issues.md)
│       ├── analysis.py          # LaTeX document analysis (labels, refs, cites)
│       ├── latex.py             # LaTeX parsing utilities
│       ├── toc.py               # Table of contents parsing
│       ├── index.py             # Back-of-book index (.idx parsing)
│       ├── find_text.py         # Normalized .tex source search
│       ├── grep_raw.py          # Normalized PDF raw text grep
│       ├── validate.py          # Path traversal + input validation
│       ├── git_diff.py          # Git diff with LaTeX section annotations
│       ├── cite_tree.py         # Citation tree (S2 graph caching)
│       ├── s2ag.py              # Local S2AG database (offline citations)
│       ├── s2ag_cli.py          # S2AG CLI utilities
│       ├── needful.py           # Recurring task tracking
│       ├── summaries.py         # File content summaries
│       ├── guide.py             # On-demand usage guide loader
│       ├── filelock.py          # Cross-process file locking
│       └── docs/                # Built-in guide markdown files (11)
└── tests/
    ├── conftest.py              # Shared fixtures
    ├── test_analysis.py
    ├── test_bib.py
    ├── test_checksum.py
    ├── test_chunk.py
    ├── test_cite_tree.py
    ├── test_concurrent_bib.py
    ├── test_config.py
    ├── test_crossref.py
    ├── test_discovery.py
    ├── test_errors.py
    ├── test_extract.py
    ├── test_figures.py
    ├── test_filelock.py
    ├── test_git_diff.py
    ├── test_grep_raw.py
    ├── test_guide.py
    ├── test_http.py
    ├── test_identify.py
    ├── test_index.py
    ├── test_issues.py
    ├── test_latex.py
    ├── test_manifest.py
    ├── test_needful.py
    ├── test_notes.py
    ├── test_openalex.py
    ├── test_semantic_scholar.py
    ├── test_store.py
    ├── test_summaries.py
    ├── test_toc.py
    ├── test_unpaywall.py
    └── test_validate.py