Local, privacy-first document catalogue for AI agents: metadata-only discovery, no cloud, no RAG.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
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.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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.
Local by design Β· metadata only Β· governed discovery Β· built for MCP
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.
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.
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.
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.
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.
Not every useful document should be treated the same way. Shelfmark separates two questions that usually get confused:
rights: OWN / REFERENCE /
RESTRICTEDconfidential: 0 / 1A 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.
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.
| Consultants and advisors | Find previous analyses, proposals, frameworks and deliverables without exposing unrelated client material. |
| Researchers and analysts | Navigate large collections of reports, datasets and source material through consistent metadata. |
| Product and strategy teams | Reconnect decisions, research, roadmaps and previous thinking across projects and time. |
| Studios and independents | Turn years of accumulated work into reusable context while keeping control over client files and IP. |
| Agent builders | Give local agents a governed discovery layer over MCP. |
shelfmark review asks a few
questions about your own folders and writes the answers to config.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).
Install the MCP server and its catalogue-management, context-finding, and archive-research skills as one versioned plugin:
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:
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):
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:
No reviews yet β be the first to share how this listing worked for you.
Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.
[](https://allmcps.com/mcp/shelfmark)<a href="https://allmcps.com/mcp/shelfmark"><img src="https://allmcps.com/api/badge/shelfmark?style=directory" alt="Shelfmark on AllMCPs" /></a>