MCP Gateway

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What is MCP?

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Capabilities

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Get Started

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Why GraphQL with MCP?

• Precise data selection — GraphQL’s nature allows you to define exactly what data AI models can access, from simple queries to complex operations across your entire graph.
• Declarative operation definition — Create purpose-built .graphql files with operations tailored specifically for AI consumption. These function as persisted operations (trusted documents), giving you complete control over what queries AI models can execute.
• Self-documenting operations — Using the September 2025 GraphQL spec, you can embed rich descriptions directly in your operation definitions, making them immediately understandable to AI models without external documentation.
• Flexible data exposure — Control exactly which operations and fields are exposed to AI systems with granular precision.
• Compositional API design — Build different operation sets for different AI use cases without changing your underlying API.
• Runtime safety — GraphQL’s strong typing ensures AI models can only request valid data patterns that match your schema.
• Built-in validation — Operation validation prevents malformed queries from ever reaching your backend systems.
• Evolve without breaking — Change your underlying data model while maintaining stable AI-facing operations.
• Federation-ready — Works seamlessly with federated GraphQL schemas, giving AI access to data across your entire organization.

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Real-World Example: AI Integration in Finance

1. Security vulnerabilities: Their existing REST endpoints contained mixed sensitive and non-sensitive data, making them unusable for AI integration without major restructuring.
2. Development bottlenecks: Their engineering team estimated 6+ months to create and maintain a parallel “AI-safe” REST API, delaying their AI initiative significantly.
3. Data governance issues: Without granular control, they couldn’t meet regulatory requirements for tracking and limiting what data AI systems could access.

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Using GraphQL and MCP to Define a Safe Access Layer

"""
Retrieves recent transaction history for a customer account.
Returns only non-sensitive transaction details suitable for AI assistant responses.
Excludes: account numbers, routing information, precise location data, and full merchant details.
Use this to answer customer questions about recent purchases and payment status.
"""
query GetTransactionHistory($accountId: ID!, $last: Int!) {
  account(id: $accountId) {
    transactions(last: $last) {
      id
      date
      merchantNameMasked
      category
      amount
      status
    }
  }
}

• What data the operation provides
• What sensitive information is excluded
• When to use this operation appropriately

• Accelerate compliance review by clearly documenting what data AI could access in the operation definitions themselves
• Avoid duplicating APIs, using GraphQL’s type system and persisted operations
• Enforce operational boundaries through schema validation and mutation exclusion
• Provide self-documenting operations that AI models could understand without external documentation
• Scale safely by exposing new fields to AI only when explicitly approved

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Outcome

• Achieve compliance sign-off in weeks instead of months
• Reduce security review effort by 95%
• Maintain a single source of truth for internal and AI clients
• Future-proof their integration as the API evolved

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How It Works

1. Loads GraphQL operations from a specified directory
2. Validates them against your schema
3. Generates JSON schemas for operation variables
4. Exposes these operations as tools that AI models can discover and use
5. Handles execution of operations when called by AI models

1. It discovers available GraphQL operations as tools and their descriptions
2. Reads the tool descriptions to understand what each operation does, what data it returns, and when to use it
3. Understands input requirements through the JSON schema
4. Executes tools with appropriate parameters
5. Receives structured data that it can interpret and use in its responses

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Tools

• Built-in tools provided by the server itself: get_operation_info for HTTP execution and integration instructions, and the optional get_schema and execute_graphql for schema access and arbitrary queries.
• Tools you create, each defined by a GraphQL operation. This is the primary way to shape what AI models can do with your API.