Fibery MCP Server

The Fibery MCP Server bridges the gap between a collaborative workspace platform and conversational AI assistants. By exposing Fibery’s RESTful API through the Model Context Protocol, it lets an LLM—such as Claude or any other MCP‑compatible model—talk to your Fibery environment in plain language. This eliminates the need for custom integrations or manual API calls, enabling developers to focus on building higher‑level workflows while the server handles authentication, request routing, and data formatting.

What Problem Does It Solve?

In many teams, Fibery is the single source of truth for projects, products, and knowledge bases. However, interacting with Fibery programmatically usually requires writing HTTP requests, handling pagination, and mapping JSON schemas to domain objects. Developers often end up building repetitive tooling or writing boilerplate code just to read or write entities. The Fibery MCP Server removes this friction by providing a ready‑made, protocol‑compliant interface that any LLM can consume. It turns natural language queries into structured API calls, turning the assistant into a real‑time data explorer and editor.

Core Value for Developers

• Seamless Natural‑Language Access: Query, list, and modify Fibery data without writing SQL or custom scripts.
• Unified Toolset: The server bundles a small but powerful set of tools—listing databases, describing schemas, querying data, creating and updating entities—that cover most day‑to‑day interactions.
• Security & Authentication: It accepts an API token and host URL, ensuring that all traffic is authenticated against your Fibery instance.
• Extensibility: Since the server follows MCP standards, it can be plugged into any LLM client that supports MCP, making it future‑proof as new assistants emerge.

Key Features Explained

• List Databases: Returns a catalog of every database in the workspace, giving the assistant context about available collections.
• Describe Database: Delivers a detailed schema of a chosen database, including field titles, names, and types—essential for the assistant to understand how to structure queries or updates.
• Query Database: Exposes a flexible query interface that translates natural language into Fibery’s API filters, enabling complex data retrievals.
• Create & Update Entity: Lets the assistant create new records or modify existing ones directly from a conversation, streamlining data entry and maintenance tasks.

Real‑World Use Cases

• Product Management: A product owner can ask the assistant to pull the latest sprint backlog, add a new user story, or update status flags—all without leaving the chat.
• Project Tracking: Teams can retrieve progress reports, generate burndown charts, or modify task assignments via conversational commands.
• Knowledge Base Maintenance: Writers and editors can search for existing articles, create new entries, or update metadata by speaking to the assistant.
• Automation: Combine MCP calls with other workflow tools (e.g., Zapier, Power Automate) to trigger actions in Fibery when external events occur.

Integration with AI Workflows

Once the MCP server is registered in an LLM client’s configuration, every tool becomes available as a tool call that the assistant can invoke. The LLM’s natural language understanding selects the appropriate tool, passes arguments derived from user input, and receives a structured response. This tight coupling allows developers to build sophisticated assistants that can read, write, and manipulate Fibery data on the fly, all while maintaining a conversational interface. The result is a powerful, low‑friction bridge between human intent and the rich data stored in Fibery.