Library MCP

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.