GraphQL Skill

The GraphQL Skill equips your AI agents with structured expertise in GraphQL schema design and implementation, covering everything from type definitions and resolver architecture to subscriptions, federation, caching, and performance tuning. Instead of pasting boilerplate resolver code or schema stitching logic into a chat window, teams route this skill through a single hosted MCP server so any AI agent — Claude, GPT, Gemini, or an internal copilot — can draft schemas, review resolver logic, and reason about federation boundaries consistently across your organization.

Use it when you're designing a new GraphQL API from scratch and need help modeling types, unions, and interfaces that map cleanly to your domain, or when you're retrofitting GraphQL onto an existing REST backend. The skill understands resolver best practices, including dataloader-style batching to avoid N+1 query problems, field-level authorization patterns, and how to structure nested resolvers so they stay testable and performant as your schema grows. It also handles GraphQL subscriptions — helping agents reason about pub/sub wiring, transport choices (WebSockets vs. SSE), and how to keep subscription resolvers lightweight under load.

For teams running distributed GraphQL architectures, the skill brings federation know-how: composing a supergraph from multiple subgraphs, defining entity keys and reference resolvers, and avoiding common federation pitfalls like circular entity references or inconsistent type ownership. On the performance side, it covers caching strategies at multiple layers — persisted queries, response caching, field-level caching with directives, and CDN-friendly patterns for public GraphQL endpoints — plus guidance on query complexity analysis and depth limiting to protect your API from expensive or malicious queries.

Because BusinessMCP unifies your tools, databases, and internal knowledge behind one hosted MCP server, the GraphQL Skill isn't isolated context that lives only in one IDE session. Connect it once through your company's /api/mcp endpoint using a Bearer mcph_* key, and every agent that touches your stack — code review bots, internal support assistants, or automated PR reviewers — gets the same GraphQL schema conventions, resolver patterns, and federation rules. Pair it with your actual database or API tool connections in the same MCP server so agents can propose schema changes with real awareness of your existing data model, then track how often GraphQL-related queries and skill invocations happen through the business-intelligence dashboard.

This skill is a strong fit for platform teams standardizing GraphQL conventions across squads, for engineers migrating a REST API to GraphQL federation, and for anyone who wants AI-assisted code review to catch resolver performance issues (like missing batching or unbounded query depth) before they reach production. It's model-agnostic, so whichever LLM your team standardizes on, the GraphQL schema design and performance-tuning guidance stays consistent, auditable, and centrally managed rather than scattered across ad hoc prompts.