MCP Document Server

Overview

The MCP Document Server is a lightweight, pluggable service that brings document search and retrieval into the Model Context Protocol ecosystem. It solves a common pain point for AI developers: accessing structured, searchable knowledge bases from an assistant without having to build custom connectors. By exposing a simple tool and optional document creation endpoint, the server lets AI agents query existing content or inject new pages directly into external platforms such as Confluence, all while staying within the MCP framework.

At its core, the server implements a standard document service interface. Each service can be swapped in or out without touching client code, allowing teams to choose the storage backend that best fits their workflow. The bundled local JSON store offers instant, offline access to Markdown or HTML files, while the Confluence integration unlocks enterprise knowledge bases through REST calls and CQL queries. This dual‑mode approach means a single MCP client can search across both local drafts and corporate documentation in one request, simplifying agent logic.

Key capabilities include:

• Semantic search: The server accepts keyword arrays and returns ranked document matches, enabling agents to surface the most relevant content for a query.
• Document creation: Agents can create new Confluence pages on the fly, facilitating dynamic knowledge capture during conversations.
• Extensibility: Adding a new backend (e.g., Google Drive, Notion, GitHub Wiki) requires only implementing the interface and registering it—no changes to the MCP protocol or client.
• HTML support: Returned documents preserve formatting, allowing agents to present rich content without additional parsing.

Typical use cases involve knowledge‑base assistants that answer user questions, generate summaries from internal docs, or draft new pages based on conversation context. In a software engineering environment, an agent could pull API docs from Confluence and embed them in responses; in a marketing setting, it might retrieve campaign briefs stored locally. Because the server speaks MCP natively, any client that understands the protocol—Cursor, Claude, or custom agents—can tap into these capabilities with minimal effort.

The MCP Document Server stands out by marrying ease of use with deep integration options. Its modular design keeps the core lightweight while offering powerful search across multiple document stores, making it an attractive choice for developers who want to enrich AI assistants with reliable, context‑aware knowledge sources.