As AI systems become more capable, their value increasingly depends on the freshness and fidelity of the data they access. Traditional retrieval architectures like RAG (Retrieval-Augmented Generation) rely on embeddings and vector databases. While effective, they’re expensive, slow to update, and introduce security trade-offs.
Enter the Model Context Protocol (MCP): a new open standard designed to give AI models direct, real-time access to files, APIs, and databases, without intermediate layers.
This shift creates new opportunities for developers, but also new architectural requirements. And at the heart of this transformation is a critical enabler: scalable, federated GraphQL APIs.
That’s where Grafbase comes in.
An MCP server acts as a broker between an AI assistant (like Claude or ChatGPT) and real-world data sources like databases, file systems, internal APIs, and cloud services.
Instead of relying on its interned knowledge or additional training, MCP servers offer live access to data. They respond to structured requests from AI assistants, returning up-to-date, permissioned data on demand.
Core MCP architecture includes:
- MCP Client: The AI assistant (like Claude, ChatGPT, or other LLMs) that initiates requests for data and tools
- MCP Host: The runtime environment that manages the connection between clients and servers, handling protocol negotiation and message routing
- MCP Server: The component that exposes data sources and tools to AI assistants through standardized interfaces
- Data Sources: The underlying systems like databases, APIs, file systems, and cloud services that MCP servers connect to
The protocol defines how these components communicate using JSON-RPC over various transports (stdio, SSE, WebSockets), ensuring secure, structured data exchange between AI systems and live data sources.
MCP significantly reduces the effort to enhance an agent with custom data. No expensive additional training is required and context bloat is contained with data being provided only when necessary.
A GraphQL MCP server is an MCP server that sits in front of a GraphQL API and exposes an MCP protocol compliant interface so an AI agent/LLM can ask questions about the GraphQL schema, most importantly to formulate GraphQL queries. The Grafbase MCP server also exposes the execute tool to enable the LLM to execute these queries.
The Grafbase MCP server is a production ready MCP server built into the Grafbase Gateway and the Grafbase CLI. It is built to keep the context sent to your LLM as small and relevant as possible to enable effective reasoning on large schemas without filling the context window with bloat.
MCP doesn’t dictate how APIs are implemented, but only that the server can expose structured, semantically rich interfaces to the model.
GraphQL is uniquely suited to this role:
- Schema-driven: AI agents can introspect capabilities programmatically.
- Precise queries: Models can request only what they need, with full field-level control.
- Composable: Data from many services can be unified behind one endpoint.
- Real-time: GraphQL supports subscriptions natively.
As MCP adoption grows, organizations need an API layer that’s discoverable, governable, and safe for model access. Federated GraphQL APIs are the natural foundation for this.
Grafbase is a modern GraphQL platform designed to operate as an MCP-compatible data layer: fast, composable, secure, and deeply introspectable.
Here’s how it fits the model:
Grafbase allows teams to expose internal APIs, services, and tools via subgraphs, then compose them into a unified GraphQL schema.
- Schema Registry with CI/CD integration
- Native Apollo Federation v2 support
- Extensions to enrich the graph with non-GraphQL sources such as REST, Kafka, NATS or Postgres
This allows you to build a rich, model-facing API from many internal systems without tightly coupling teams or deployments.
Grafbase enables fine-grained protection over data access. This is essential when AI models are retrieving sensitive or dynamic data. Here are a few examples of the features offered by the gateway for access control:
- Trusted documents: Only allow-listed queries are accepted
- Authentication: Enforce JWTs or scopes per request
- Rate and operation limits: Limit the rate and complexity of queries
- Message signatures: Verify request integrity with RFC 9421
This ensures AI agents can query your systems safely with full auditability.
With Grafbase, every model-issued request can be traced, logged, and inspected.
- Native OpenTelemetry tracing and metrics support etc.
- Access Logs via custom gateway hooks
This gives platform teams operational confidence to expose internal APIs to MCP servers.
Need to expose REST endpoints? Connect to NATS? Enforce dynamic auth rules? Grafbase supports pluggable extensions for:
- REST and event-based sources
- Custom policies per field or operation
- Caching and lifecycle controls
These extensions help expose exactly the right surface to the model, without exposing complexity.
With the Grafbase MCP, we put a lot of effort into returning just the necessary context to your LLM, avoiding bloating its reasoning context with irrelevant parts of your GraphQL schema. This leaves more room for useful tokens in your LLM context, helping reduce hallucinations and improve response relevance.
If you're considering MCP adoption for your AI stack, Grafbase can serve as the structured interface layer between your model and your infrastructure.
Example use cases:
- Finance: Secure real-time access to risk engines, customer data, and transaction APIs.
- Enterprise search: Replace brittle RAG flows with precise, low-latency structured queries.
- AI-powered assistants: Enable chat-based interfaces to interact directly with your systems via GraphQL.
MCP offers a compelling new model for how AI interacts with data: direct, live, and semantically structured. But to implement it effectively, organizations need more than endpoints. They need a federated, governed API layer optimised for AI consumption.
Grafbase provides that layer.
Whether you’re building a model-aware assistant or planning to expose enterprise systems to an AI layer, Grafbase enables you to do so with confidence, speed, and scale.
Ready to build MCP-ready APIs? Explore Grafbase’s federation platform or get started in minutes.