Building Flexible MCP Tools with GraphQL and Rust

In the rapidly evolving landscape of AI, the ability for large language models (LLMs) to interact with external data and services is a core requirement for building truly capable agents. The Model Context Protocol (MCP) provides a standardized framework for this interaction, allowing agents to discover and utilize tools in a consistent manner. While many approaches exist for creating these tools, exposing APIs through GraphQL offers a unique advantage. By providing a declarative, type-safe schema, GraphQL can act as a more efficient and powerful intermediary than traditional REST APIs or direct database access. This article examines a specific implementation of this pattern: the open-source Apollo MCP server¹, which is built in Rust to expose GraphQL operations as MCP tools.

Exposing Existing APIs to an Agent

A common challenge in AI agent development is connecting a model to a vast, complex ecosystem of existing data and services. A naive approach might be to provide the agent with raw API specifications, like a full OpenAPI schema, and let the model figure out the details. However, as the API grows in complexity, this can become inefficient and lead to excessive token usage in the LLM's context window.

The Apollo MCP server offers a more refined solution by allowing developers to curate and expose specific, purpose-built tools to an agent. This approach is demonstrated through an example using the massive GitHub GraphQL API. Instead of giving an agent access to the entire API, the developer uses the Apollo MCP server to create a focused tool, such as one to "fetch the latest five issues from a GitHub repository." This tool leverages the underlying GraphQL schema but abstracts the complexity, providing the agent with a clean, coarse-grained function. The server binary, written in Rust, can be configured with a simple YAML file to define which GraphQL operations to expose.

Building a Community-Driven Tool

Beyond exposing a single API, the Apollo MCP server's strength lies in its ability to combine disparate data sources into a single, cohesive tool. A practical example is a community-building tool for talk submissions. This tool orchestrates data from a Superbase database with custom logic from an Apollo Server instance. The Superbase database, which automatically generates REST APIs from its PostgreSQL schema², serves as the primary data store. The Apollo MCP server uses its REST connectors to map these REST resources to GraphQL types.

To handle custom logic, such as validating talk submissions, a separate GraphQL API is created using Apollo Server. This server, which can be written in languages like JavaScript, TypeScript, or Python, can contain business logic that would be too complex or insecure to expose directly to the agent. The Apollo MCP server can then connect to and orchestrate both the Superbase-based GraphQL schema and the custom-logic Apollo Server, exposing them as a single, unified set of MCP tools to the agent. This composable architecture allows developers to choose the best technology for each part of their application while presenting a simple, unified interface to the AI agent.

Behind the Scenes: The MCP-GraphQL Workflow

The core functionality of the Apollo MCP server and its interaction with an AI agent like Goose³ is a multi-step process.

1. Tool Discovery: The agent, which is an application configured to interact with an MCP server, queries the server to retrieve a list of available tools and their schemas. The Apollo MCP server, through its configuration, provides a list of exposed GraphQL operations, each with a name, description, and required parameters.
2. User Request: A user provides a natural language prompt to the agent, such as "fetch the latest five issues from the Apollo GraphQL rover repository."
3. Agent Reasoning: The agent uses an LLM (in this case, AWS Bedrock's⁴ Claude 3.7 Sonnet) to analyze the prompt. Based on the user's intent and the available tools, the LLM decides to call the search tool to find the relevant schema fields. The search tool is described as a more efficient alternative to full GraphQL introspection.
4. Tool Execution: Once the agent has sufficient information, it constructs a complete GraphQL query and sends it to the Apollo MCP server⁵ for execution. The agent's ability to reason about the correct parameters and structure of the query is a function of its training and the quality of the tool definitions.
5. Query Generation & Hot-Reloading: The Apollo MCP server's most compelling feature for developers is its hot-reloading capability. After an initial query is generated by the agent, developers can inspect and refine the GraphQL query on their local machine. Any changes to the query definition, such as removing extraneous fields like a URL or unnecessary labels, are immediately available to the agent without requiring a server restart. This enables a rapid, iterative development cycle for building and fine-tuning tools.
6. Response & Optimization: The server executes the optimized GraphQL query and returns a concise, relevant payload to the agent. This process minimizes token usage in the LLM's context window by ensuring only essential data is retrieved and passed back, which is a key advantage of GraphQL's declarative nature.

This workflow highlights the importance of creating granular, coarse-grained tools tailored to specific use cases, a stark contrast to simply exposing a raw, fine-grained API to the agent. This intentional design reduces LLM context window pressure and increases the predictability and efficiency of the agent's actions.

My Thoughts

The Apollo MCP server provides a pragmatic, developer-focused approach to building AI agents that avoids the pitfalls of monolithic systems. The emphasis on a coarse-grained, use case-driven tool layer is a critical architectural pattern that directly addresses the limitations of current LLMs. Unlike a simple "dump everything and hope for the best" approach, this method ensures that the agent's actions are more intentional and efficient. The ability to compose tools from multiple data sources, whether REST APIs via connectors or custom logic APIs, demonstrates a high degree of flexibility. The fast, hot-reloading developer experience is a significant win, as it allows for rapid iteration and refinement of tools—a necessary step given the somewhat unpredictable nature of prompt engineering.

The challenge, as with any agentic system, remains in the "LLM magic" of prompt engineering. While the tool layer is robust, the agent's ability to correctly reason and select the right tool still depends on the specificity of the initial prompt. Future improvements could involve even more advanced methods for agent-side reasoning or a higher-level tool definition language that further abstracts the complexities of the underlying APIs. Adding features like OAuth, as mentioned in the talk, is a crucial next step for enterprise adoption, allowing developers to secure tools with proper authentication and authorization.

Acknowledgements

Many thanks to Ma'am Amanda Martin from Apollo GraphQL for the informative talk, "Vibe Code MCP Tools with GraphQL,"⁶ and to the entire Apollo and broader MCP/AI community for their collaborative work in advancing this technology.

References

Footnotes

1. Apollo MCP Server GitHub Repository ↩

2. Supabase: Auto-generated REST APIs with PostgREST ↩

3. Goose AI Agent from Block ↩

4. AWS Bedrock: Anthropic's Claude 3.7 Sonnet ↩

5. Apollo Server Documentation ↩

6. Vibe Code MCP Tools with GraphQL, Yt link ↩