The Model Context Protocol (MCP) server in Document360 exposes your knowledge base content through a structured interface for interacting with documentation. Instead of accessing documentation as rendered web pages, MCP allows AI assistants (ChatGPT, Claude) to interact directly with the underlying content. With MCP enabled, your knowledge base becomes accessible through defined AI assistants that perform specific operations such as searching, retrieving, creating, and updating content, all within your existing project rules.
What is MCP?
The MCP is an open standard that allows AI assistants to securely connect with external systems and access structured data. Instead of building separate integrations for each AI assistant and each platform, MCP provides a common interface that standardizes how AI assistants communicate with external platforms.
An MCP server acts as a bridge between an AI assistant and your system. It exposes specific capabilities such as searching content, retrieving documents, or performing structured updates through a defined protocol. The AI assistant sends a structured request to the MCP server, and the server responds with structured data that the assistant can use. You can think of MCP as a universal connector for AI integrations. Rather than creating custom connections for every tool, MCP enables a single, standardized way for AI assistants to interact with real, up-to-date systems.
By using MCP, AI assistants are not limited to generating responses from training data alone. They can retrieve live content, access structured documentation, and perform defined actions through the MCP server making their outputs more relevant and context aware.
The challenge organizations face with AI assistants
Many organizations are adopting AI assistants such as:
ChatGPT
Claude
GitHub Copilot
Internal AI agents
However, these assistants often lack direct access to structured business knowledge.
As a result:
AI tools cannot easily access knowledge bases
Documentation is not integrated into AI workflows
Developers and support teams copy-paste documentation manually
Maintaining consistent documentation becomes difficult
Organizations need a structured way to connect AI assistants with their documentation systems. Document360 MCP solves this problem.
Why MCP in Document360
Documentation in Document360 is structured across project versions, multiple languages, category hierarchies, and role-based visibility settings. This structure defines how content is organized, accessed, and governed within your knowledge base.
When documentation is consumed only through rendered web pages, much of that structure is experienced visually. Version context, language selection, and hierarchical relationships are present, but not always explicit when accessed programmatically.
MCP availability depends on your project configuration and plan. The MCP Server introduces a standardized way for AI assistants to interact with your documentation through defined capabilities such as searching, retrieving, and updating content. Instead of relying purely on rendered output, AI assistants can interact with your knowledge base in a structured and controlled manner.
For example:
Search operations can span across project versions by default or target a specific version when required.
Retrieved content respects your configured visibility and permission rules.
Write operations follow existing versioning behavior and preserve content structure.
At the same time, MCP operates entirely within your existing configuration. Content availability reflects your project version settings, language configuration, permission model, and visibility rules. Unpublished or restricted content remains governed by those rules.
When write operations occur, existing versioning behavior is preserved. MCP does not override documentation workflows, publishing rules, or governance controls. It simply provides structured access to your knowledge base while maintaining the integrity of your documentation system.
Document360 MCP tools
The MCP Server provides ten tools that define how documentation can be searched, retrieved, and managed.
Each tool represents a specific and intentional action against your knowledge base. Rather than exposing raw content or open-ended endpoints, MCP structures interactions through clearly defined operations. This ensures that documentation access remains predictable, version-aware, and aligned with your project configuration.
These tools collectively support structured discovery, contextual retrieval, and controlled content updates.
Search and retrieval tools
Tool | Description |
|---|---|
| Performs a semantic search across your knowledge base. By default, searches across all project versions unless a specific version is provided. Returns relevant content along with metadata and article references. |
| Retrieves full article content using an article ID or URL. Supports multiple languages and allows fetching published or draft versions. |
| Retrieves the complete category structure for a selected project version, including child categories and associated articles. |
| Retrieves detailed information for a specific category, including its content, subcategories, and associated articles. |
| Lists all available project versions along with their identifiers, enabling version-specific operations. |
These tools allow structured discovery and contextual access to documentation content.
Write tools
Tool | Description |
|---|---|
| Creates a new article in a specified category and project version. Supports structured content formats and requires confirmation before creation. |
| Updates an existing article while preserving its structure and formatting. Supports partial updates and requires retrieving the article before modification. |
| Creates a new category within a selected project version. Supports hierarchical category structures. |
| Updates category properties such as name, position, hierarchy, or visibility within the knowledge base. |
| Updates the content of category pages (applicable for Page or Index type categories) while preserving existing structure. |
Write operations follow the same versioning behavior and content handling rules as the Document360 editor.
These search and write tools can also be used in combination to support batch or multi-step operations. For example, AI assistants can retrieve multiple articles, generate updates across related content, or create multiple articles as part of structured workflows. This makes MCP suitable for automation scenarios such as bulk content updates, large-scale documentation creation, and maintaining consistency across multiple articles. The experience of prompts and responses may vary depending on the AI assistant being used, as each tool interprets and processes requests differently.