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  3. Shelfmark
Shelfmark logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 4:46:48 PM

Shelfmark

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
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Local, privacy-first document catalogue for AI agents: metadata-only discovery, no cloud, no RAG.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "shelfmark": {
      "command": "uvx",
      "args": [
        "shelfmark"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

shelfmark

PyPI CI Python License: MIT

Give AI agents the right context, not your entire filesystem.

Shelfmark turns years of scattered documents into a governed context map for AI agents. An agent can discover what exists, understand what kind of material it is, and select the documents relevant to a task β€” before spending context opening files.

No document migration. No duplicated content store. No need for a perfect folder structure.

sh
uv tool install shelfmark          # or: pipx install shelfmark

Local by design Β· metadata only Β· governed discovery Β· built for MCP


Your best knowledge is probably sitting in your folders

Reports, presentations, models, research, proposals and working documents accumulate over years. Some are carefully organised. Others sit inside crowded project folders, old archives, download directories, or collections that made sense only at the time.

The value is still there. The problem is that agents cannot use what they cannot discover β€” and giving an agent unrestricted filesystem access does not solve that. It transfers the work of finding, interpreting and filtering thousands of files into the context window.

Shelfmark gives the agent a map first.

Context is the scarce resource

An agent does not need every document. It needs to know what exists, what is likely to matter, where it came from, and whether it should be used at all.

Shelfmark is a discovery layer between the agent and your files. The agent searches the catalogue, narrows the field, and requests only the material the task actually needs β€” so an existing document estate becomes working context without turning the filesystem into one enormous prompt.

A catalogue of pointers, not another document store

Shelfmark builds a local SQLite catalogue of references and derived metadata: paths, filenames, formats, sizes, document types, authors, dates, classifications, selected Office properties, presentation titles, and optional content hashes.

It does not copy your documents into the catalogue. It does not index document body text. It does not create a second repository to govern, synchronise and maintain. Your files stay where they are; the catalogue points at them and describes what can be established from their metadata.

Finding a document does not expose its contents.

Turn messy folders into usable agent context

Shelfmark does not require a designed information architecture. Point it at accumulated project files, forgotten archives, or folders where documents have simply been dropped over the years.

It builds an inventory from signals that already exist β€” filenames, extensions, paths, authorship, dates, Office properties, and your own classification rules β€” creating a virtual structure across the material. Agents can then search by document type, client, project, purpose, author, year or location without anything being moved or renamed.

A messy folder can become navigable even when it never becomes tidy.

Shelfmark also stays honest about the limits of metadata. A file called final7.pdf with no useful properties cannot reveal its meaning without someone reading it. Shelfmark reports what is known rather than inventing certainty β€” the same reason it drops placeholder slide titles instead of listing twelve headings a deck does not have.

Built for selective context

  • Discover before opening. Search thousands of references before deciding which few files deserve attention.
  • Preserve the context window. Concise catalogue results instead of whole documents that may not be relevant.
  • Build on previous work. Reports, models, proposals, research and methods stay discoverable across new tasks and future engagements.
  • Keep provenance visible. Path, date, author, document type and surrounding context help an agent judge relevance.
  • Reduce blind exploration. Structured search instead of repeatedly walking directories and inspecting files one at a time.
  • Separate discovery from access. Shelfmark helps identify material; opening the original remains a separate, controllable decision.

Your roots are the trust boundary

Shelfmark walks only the roots you configure.

Symlinks are not followed. A link inside a root reads as an ordinary file and would otherwise walk straight out of the tree you configured β€” and hash opens files. Skipped links are reported, never silently dropped. To index another tree, add it as an extra root: the boundary widens by saying so in config, not by planting a link.

The catalogue is refused inside any root, because a database that indexes itself grows on every refresh. Both checks compare resolved paths, so .. and a symlinked root cannot slip past them.

Governance belongs in the retrieval layer

Not every useful document should be treated the same way. Shelfmark separates two questions that usually get confused:

  • Who owns or may reuse this? β†’ rights: OWN / REFERENCE / RESTRICTED
  • May this document leave its current context? β†’ confidential: 0 / 1

A method may belong to you while the client deliverable containing it stays confidential. Modelling the two separately lets agents discover reusable knowledge without treating everything discoverable as freely shareable.

Files matching your private/secret patterns become RESTRICTED: no tool returns their path, name, metadata or content, no argument overrides it, they are never opened for hashing, and the database is opened read-only. corpus_stats() reports a single corpus-wide count of sealed files and nothing else about them β€” not which root, not which folder. That count is the one thing disclosed, deliberately: silence about it would misrepresent the size of the corpus.

Governance is applied by the catalogue, not left to the wording of a prompt.

Know whether the map can be trusted

A search result is only useful if the agent knows the catalogue is current.

The MCP server keeps its own index current while it runs, so nothing has to be scheduled and no agent has to remember. When it cannot β€” never built, stale, a failed refresh, a clock it cannot reason from, or an index that no longer agrees with the filesystem β€” every tool says so above its answer, and corpus_stats() compares index against disk in full.

An old snapshot is never presented as complete knowledge.

Designed for knowledge-intensive work

Consultants and advisorsFind previous analyses, proposals, frameworks and deliverables without exposing unrelated client material.
Researchers and analystsNavigate large collections of reports, datasets and source material through consistent metadata.
Product and strategy teamsReconnect decisions, research, roadmaps and previous thinking across projects and time.
Studios and independentsTurn years of accumulated work into reusable context while keeping control over client files and IP.
Agent buildersGive local agents a governed discovery layer over MCP.

How it works

  1. Point Shelfmark at your existing folders. One or more roots. Files stay where they are.
  2. Build the local catalogue. It walks the permitted roots, extracts available metadata, applies classification rules, and writes references into SQLite.
  3. Review ownership and confidentiality. shelfmark review asks a few questions about your own folders and writes the answers to config.
  4. Connect an MCP-compatible agent. It searches, browses and inspects catalogue records through structured tools.
  5. Retrieve only what matters. The agent identifies the relevant artefacts before any separate content access takes place.

Install

sh
uv tool install shelfmark          # or: pipx install shelfmark
# from a checkout:
uv tool install /path/to/shelfmark

Python β‰₯ 3.11. macOS, Linux and Windows β€” the full suite runs on all three in CI, including the Windows-specific behaviours (OneDrive placeholder detection, junction refusal at the root boundary).

Codex plugin

Install the MCP server and its catalogue-management, context-finding, and archive-research skills as one versioned plugin:

sh
codex plugin marketplace add Dankaro-projects/shelfmark
codex plugin add shelfmark@shelfmark

Then create the local catalogue once with uvx shelfmark init followed by uvx shelfmark refresh. The plugin starts the pinned Shelfmark MCP server over stdio; document paths and metadata stay on the local machine.

To install the three companion skills without the MCP configuration:

sh
pnpm dlx skills add Dankaro-projects/shelfmark --full-depth

Email ingestion is optional, and the extra you want depends on the format you have. .msg resolves to wheels everywhere; .pst needs libpff-python, which publishes no wheels and compiles from C source, so it requires a build toolchain (Visual C++ Build Tools, build-essential, or the Xcode command line tools):

sh
uv tool install "shelfmark[msg]"      # .msg β€” no compiler needed
uv tool install "shelfmark[pst]"      # .pst β€” compiles from C source
uv tool install "shelfmark[email]"    # both

Quickstart

Three commands, and init finds your documents for you β€” when the default root misses, it sweeps for the folders that do hold documents and one keypress fixes the config:

text
$ shelfmark init
Wrote ~/.config/shelfmark/config.toml

WARNING: ~/Documents does not exist on this machine.

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Shelfmark

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "shelfmark": { "command": "uvx", "args": ["shelfmark"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
Last updatedSep 10, 2026
13/13 checks healthy over the last 45d
Views0
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Installs0
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Last commit15d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 10, 2026
40Quality signal: Fair Β· 40/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev Β· shelfmark (PyPI)

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