Apollo MCP Server

Overview

Apollo MCP Server bridges the gap between GraphQL APIs and AI assistants by exposing your Apollo‑managed GraphQL operations as Model Context Protocol (MCP) tools. It allows large language models—such as Claude, Gemini, or GPT—to discover and invoke your GraphQL endpoints directly from within a conversational context. Instead of writing custom adapters or dealing with raw HTTP requests, developers can rely on a single, standardized interface that translates MCP tool calls into GraphQL queries or mutations.

The server solves the problem of integration friction between AI workflows and existing GraphQL services. Many teams already maintain robust Apollo graphs for data fetching, authorization, and caching. When an AI assistant needs to retrieve or mutate that data, it traditionally requires custom code or a third‑party SDK. Apollo MCP Server eliminates this overhead by automatically generating MCP tool definitions from the GraphQL schema and exposing them over a lightweight HTTP endpoint. The result is a declarative, type‑safe bridge that respects the schema’s structure and validation rules.

Key capabilities of Apollo MCP Server include:

• Automatic tool discovery – The server scans the GraphQL schema and surfaces operations as MCP tools without manual mapping.
• Type‑aware invocation – Input arguments and return types are derived from the GraphQL schema, ensuring that AI models can only request valid operations.
• Configurable operation exposure – Developers control which queries or mutations are available through a concise configuration file, enabling fine‑grained security and access control.
• Seamless integration with MCP clients – Any MCP‑compliant client, from LLMs to the official inspector tool, can connect and start calling GraphQL operations as if they were native functions.
• Rich metadata support – The server includes operation descriptions and documentation, allowing AI assistants to provide context‑aware prompts or explanations to users.

Typical use cases involve building conversational agents that need real‑time data from a GraphQL backend—such as a customer support chatbot querying ticket status, an internal help desk bot retrieving employee records, or a data‑analysis assistant generating reports from complex queries. In each scenario, the MCP server removes boilerplate code and guarantees that only approved operations are exposed, reducing both development time and the risk of accidental data leaks.

By integrating Apollo MCP Server into an AI workflow, developers can treat their GraphQL API as a first‑class “tool” for language models. The server’s standardized MCP interface ensures that the AI can orchestrate API calls, handle responses, and even chain multiple operations together—all while preserving type safety, schema validation, and the rich tooling ecosystem that Apollo already provides.