Fibery MCP GraphQL Server

The Fibery MCP GraphQL Server bridges the gap between AI assistants and Fibery’s powerful GraphQL API. By exposing a set of MCP tools, it lets language models discover the structure of a Fibery workspace and generate syntactically correct queries and mutations on the fly. This eliminates the need for developers to manually parse API documentation or write boilerplate code, allowing AI assistants to interact with Fibery data seamlessly.

What Problem Does It Solve?

When an LLM needs to retrieve or modify information in Fibery, it must first understand the shape of the data: what spaces exist, which types are available, and how queries should be structured. Traditionally, developers write custom wrappers or rely on autogenerated clients that can become brittle when schemas evolve. The Fibery MCP GraphQL Server solves this by providing runtime introspection tools that keep the assistant in sync with the current schema, ensuring generated GraphQL statements are valid before they hit the API.

Core Capabilities

• List Spaces and Types – Enumerates every GraphQL space and its associated types, giving the assistant a global view of the workspace.
• Get Schema SDL – Retrieves the full schema definition language (SDL) for a chosen space, exposing all queries, mutations, and type definitions.
• Validate Fibery GraphQL – Checks any user‑generated query or mutation against the live schema, returning detailed error messages if the statement is malformed.

These tools are exposed as MCP actions that any compliant client can call, making the server a drop‑in component for existing AI pipelines.

Why It Matters to Developers

For developers building AI‑powered interfaces, the server removes a major friction point: schema discovery. With these tools, an assistant can ask a user for the desired data (e.g., “Show me all open tasks in the Marketing space”), introspect the available fields, craft a precise GraphQL query, validate it instantly, and then execute it. This workflow reduces runtime errors, speeds up feature iteration, and allows developers to focus on higher‑level business logic rather than low‑level API plumbing.

Use Cases and Real‑World Scenarios

• Chatbots for Business Workflows – An AI assistant can read user intent, look up the relevant Fibery space, and generate a query that pulls or updates records without hard‑coded endpoints.
• Dynamic Reporting Dashboards – By validating queries on demand, dashboards can let users compose custom reports that the assistant translates into efficient GraphQL calls.
• Automated Data Migration – Scripts can introspect source and target schemas, validate transformation queries, and execute them safely across Fibery spaces.

Unique Advantages

• Live Schema Introspection – The server queries the actual Fibery instance, ensuring that any schema changes are immediately reflected in generated queries.
• Zero Boilerplate for LLMs – Developers need only expose the MCP endpoint; the assistant handles schema parsing, validation, and error reporting automatically.
• Extensibility – The MCP framework allows additional tools (e.g., mutation helpers or pagination utilities) to be added without changing the client side.

In summary, the Fibery MCP GraphQL Server empowers AI assistants to interact with Fibery in a fully type‑safe, dynamic manner. It streamlines development, enhances reliability, and opens the door to sophisticated AI‑driven applications that can adapt instantly to evolving data schemas.