Library MCP
Your private knowledge corpus, exposed as a single MCP server — so Cursor, Claude Desktop, ChatGPT Desktop, and any MCP-aware AI client can read it directly, with governance and audit applied.
What you get
One MCP server that exposes your company's private knowledge — internal docs, runbooks, code, customer notes — to any MCP-aware AI client your team uses. Cursor can read it during development. Claude Desktop and ChatGPT Desktop can read it during research. Any agent your team builds can read it through the same connection. Permissions and audit applied uniformly.
What makes it different
Today, every AI tool your team uses re-implements its own integration to your internal docs. Library MCP unifies the integration: write once, connect everywhere. Governance lives in the registry; audit lives in observability; the MCP-aware clients just see a clean tool surface.
What it can do for you
Stop re-implementing 'connect to internal docs' in every AI tool. Give your developers and analysts a single source for company-grounded queries. Govern what each client sees. Audit what was retrieved by whom. Future-proof against new MCP-aware AI tools as they ship — they just connect to your existing server.
- Single MCP server exposing your corpus
- Connects to Cursor, Claude Desktop, ChatGPT Desktop, others
- Per-client and per-user permission scopes
- Full audit trail on every query
- Multi-source ingestion (docs, code, tickets, structured data)
- Hybrid retrieval with citation contract
- Self-hostable or managed
- Versioned schema for schema-aware clients
Who it's for
Engineering orgs where multiple AI tools are already in use and each one is half-integrated with internal docs. Research and analyst teams using Claude or ChatGPT and tired of pasting context. Companies building internal AI agents that need a clean knowledge surface to call.
How we ship it
Typical deployment is 2 weeks. Source connection, permission mapping, server hosting, client onboarding for the team's existing tools. Self-serve onboarding planned for v2.
Questions people actually ask
What does exposing our docs as an MCP server actually mean?
MCP (Model Context Protocol) is the standard connection AI tools use to read external sources. Instead of every AI tool your team uses re-implementing its own integration with your internal docs, you get one server that Cursor, Claude Desktop, ChatGPT Desktop, and any agent you build all connect to. Write the integration once, connect everywhere.
If any AI client can connect, what stops the wrong one seeing too much?
Permission scopes, set per client and per user — governance lives in the registry, not in each tool's goodwill. Every query lands on a full audit trail showing what was retrieved and by whom, and retrieval carries a citation contract so answers trace back to real passages.
How long does setup take and what do you need from us?
A typical deployment is 2 weeks: source connection, permission mapping, server hosting, and client onboarding for the tools your team already uses. We need access to the sources you want exposed — docs, runbooks, code, customer notes — and a decision on who may see what.
Are we tied to your hosting?
No — the server is self-hostable or managed, your choice, and the corpus behind it stays yours. New MCP-aware tools that ship next year just connect to the server you already have, and if you build custom internal agents later, they call the same knowledge surface instead of a new integration.
Frequently paired with
Related products
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