diff --git a/site/docs/features/configuring-task-shards.md b/site/docs/features/configuring-task-shards.md index 76dab3b4bc3..3b5b1cdec0f 100644 --- a/site/docs/features/configuring-task-shards.md +++ b/site/docs/features/configuring-task-shards.md @@ -1,5 +1,5 @@ --- -sidebar_position: 2 +sidebar_position: 5 slug: /reference/Configuring-task-shards/ --- diff --git a/site/docs/features/mcp-integration.md b/site/docs/features/mcp-integration.md index d2b1269b4b5..bd6e005ccfb 100644 --- a/site/docs/features/mcp-integration.md +++ b/site/docs/features/mcp-integration.md @@ -1,5 +1,5 @@ --- -sidebar_position: 1 +sidebar_position: 2 --- # MCP Integration @@ -10,6 +10,10 @@ Connect to Estuary's documentation directly from your IDE or AI tool using the M The Model Context Protocol (MCP) allows AI assistants in your development environment to access Estuary's documentation contextually. This means you can ask questions about Estuary directly in your IDE and get accurate, documentation-backed answers without leaving your workflow. +:::tip +MCP keeps your assistant's answers accurate. To have it also build and operate pipelines for you, pair it with [agent skills](../guides/agent-skills.md). See [Using coding agents with Estuary](./using-coding-agents.md) for the full setup. +::: + ## Setup **Server URL:** `https://estuary.mcp.kapa.ai` @@ -152,3 +156,9 @@ Once connected, you can ask context-aware questions about Estuary from within yo The AI assistant will use Estuary's documentation to provide accurate, up-to-date answers while you code. +## Related pages + +- [Using coding agents with Estuary](./using-coding-agents.md) — end-to-end setup for flowctl, MCP, and skills +- [Agent skills](../guides/agent-skills.md) — playbooks that let your assistant build and operate pipelines +- [flowctl setup](../guides/get-started-with-flowctl.md) — install and authenticate the CLI + diff --git a/site/docs/features/time-travel.md b/site/docs/features/time-travel.md index 26aa36f21a5..bf01f6eb51d 100644 --- a/site/docs/features/time-travel.md +++ b/site/docs/features/time-travel.md @@ -1,5 +1,5 @@ --- -sidebar_position: 1 +sidebar_position: 4 slug: /reference/time-travel/ --- diff --git a/site/docs/features/using-coding-agents.md b/site/docs/features/using-coding-agents.md new file mode 100644 index 00000000000..6eda942a1ab --- /dev/null +++ b/site/docs/features/using-coding-agents.md @@ -0,0 +1,33 @@ +--- +sidebar_position: 1 +--- + +# Using Coding Agents with Estuary + +Build and operate Estuary pipelines from your AI coding assistant — Claude Code, Cursor, GitHub Copilot, OpenAI Codex, Gemini CLI, and others. Instead of stitching together docs and remembering flowctl commands, you can ask in plain English: "capture my Postgres into Snowflake" or "why is this materialization failing?" + +Three pieces work together: + +1. **[flowctl](../guides/get-started-with-flowctl.md)** — Estuary's CLI. This is what your agent actually runs to discover sources, build specs, publish, and inspect tasks. +2. **[MCP integration](./mcp-integration.md)** — connects your assistant to Estuary's documentation so its answers are backed by current docs, without leaving your editor. +3. **[Agent skills](../guides/agent-skills.md)** — step-by-step playbooks that tell your assistant the exact commands, spec shapes, and gotchas for each connector and operation. + +You can use any one of these on its own, but they're most useful together: skills drive flowctl to do the work, and MCP keeps the assistant's explanations accurate. + +## Setup + +1. **[Install and authenticate flowctl](../guides/get-started-with-flowctl.md)** — the CLI your agent drives. +2. **[Connect the MCP server](./mcp-integration.md)** — so your assistant can answer documentation questions in context. +3. **[Install the agent skills](../guides/agent-skills.md)** — so your assistant knows the right commands and spec shapes for each connector. + +## What you can do + +Once set up, ask your assistant in plain English. For example: + +- "Capture my PostgreSQL database into Estuary." +- "Materialize my collections into Snowflake." +- "Capture from MySQL and materialize into Redshift." +- "Why is my materialization failing?" +- "What's the difference between captures and materializations?" + +The skills tell your assistant which flowctl commands to run and how to build the specs; the MCP integration keeps its explanations grounded in Estuary's documentation. diff --git a/site/docs/features/working-logs-stats.md b/site/docs/features/working-logs-stats.md index 6e7fd94b0f3..4197454c621 100644 --- a/site/docs/features/working-logs-stats.md +++ b/site/docs/features/working-logs-stats.md @@ -1,5 +1,5 @@ --- -sidebar_position: 3 +sidebar_position: 6 slug: /reference/working-logs-stats/ --- diff --git a/site/docs/guides/get-started-with-flowctl.md b/site/docs/guides/get-started-with-flowctl.md index 7c92cc63e55..b18765d6d51 100644 --- a/site/docs/guides/get-started-with-flowctl.md +++ b/site/docs/guides/get-started-with-flowctl.md @@ -64,7 +64,17 @@ Once you've installed flowctl and are ready to begin working, authenticate your The token will expire after a predetermined duration. Repeat this process to re-authenticate. +## Run flowctl from an AI coding agent + +You can also run flowctl commands via AI coding assistants like Claude Code, Cursor, and GitHub Copilot to build and maintain pipelines: + +- **[Agent skills](./agent-skills.md)** provide steps to run common workflows, like creating connectors or troubleshooting pipelines. +- **[MCP integration](../features/mcp-integration.md)** provides your assistant additional context on using flowctl by connecting to Estuary's documentation. + +See [Using coding agents with Estuary](../features/using-coding-agents.md) for the full setup. + # Next steps 1. [flowctl concepts](../concepts/flowctl.md): Learn more about using flowctl. 2. [User guides](../guides/flowctl/README.md): Check out some of the detailed user guides to see flowctl in action. +3. [Using coding agents with Estuary](../features/using-coding-agents.md): Let an AI assistant build and operate your pipelines.