# Can GraphQL Become the Universal Backend for MCP Servers?

GraphQL is an API query language developed by Facebook (now Meta) in 2012 and released publicly in 2015. It provides an alternative to traditional REST APIs by allowing clients to request exactly the data they need, rather than relying on predefined endpoints.

In REST, an application might need to make multiple API calls to retrieve related information. GraphQL addresses this through a single endpoint where clients can specify the structure of the response they want. This reduces unnecessary data transfer and can simplify communication between applications and servers.

GraphQL revolves around four important capabilities:

- **Queries:** Retrieve data from a server.
- **Mutations:** Create, update, or delete data.
- **Subscriptions:** Receive real-time updates when data changes, typically through persistent connections.
- **Introspection:** Discover an API's schema, including its available types, fields, queries, mutations, and relationships. This allows clients and tools to understand an API's capabilities programmatically.

Another important feature is its strongly typed schema, which defines the structure of data and the operations an API supports.

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GraphQL is particularly useful when applications need flexible data fetching, especially when dealing with complex relationships between different entities. And isn't it the same case with MCPs. At the moment most of the MCPs are wrappers around rest APIs.

One of the main limitations of REST APIs is that they are quite rigid. The server decides what information each endpoint can provide, and clients have to work within those boundaries. If you need some additional information or a different combination of data, you often have to create a new endpoint or modify an existing one. This makes things less flexible, especially when different clients have different data requirements.\
This limitation might effect the LLMs most of the time when it needs to understand the complicated relationship between different data.

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Imagine an MCP server with four tools:

1. **Introspection:** Discover available operations, parameters, and schemas.
2. **Query:** Retrieve information.
3. **Mutation:** Create, update, or delete data.
4. **Subscription:** Receive real-time updates.

Before using the other tools, the AI agent calls the introspection tool to understand their capabilities. Based on the user's request, it can then select and execute the appropriate operation.

This allows the agent to discover what an API can do before interacting with it, making the integration more flexible and dynamic. And agents like ReAct(Reasoning + Action) does this in a very efficient and better way.

For safety of the data we can limit the power of Mutation, so that the LLM don't end up deleting the whole data.

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Lets take an example of an e-commerce AI agent asked to retrieve a customer's latest orders, including product names, prices, and delivery status. With REST, the agent might need to call multiple predefined endpoints and combine their responses. With GraphQL as MCP, the agent can first introspect the schema, discover the available fields and relationships, and construct a single query to retrieve exactly the required information. This makes data retrieval more flexible and reduces unnecessary API calls. And also the LLMs instead of fetching all data then trying to find the relationship, it can get it right way.

I think GraphQL and MCP make an interesting combination. Instead of building fixed tools for every possible operation, we can let AI agents first understand what an API offers and then decide what information they need or what actions they want to perform. This makes API interactions more flexible and opens up interesting possibilities for building AI-powered applications. REST still has its place, but GraphQL could make things simpler when agents need more dynamic access to data.