Legnext’s documentation is built to be consumed by AI coding agents. Point your agent at this site and it gets every endpoint, request/response field, and version-specific parameter behavior — no guessing, no outdated blog posts.
Four ways in, from tightest to loosest integration:
Agent skill
The fastest path: install the legnext agent skill. It teaches your coding agent the integration methodology — submitting tasks, polling vs webhooks, persisting images before they expire, credits, child tasks (variation/upscale), error handling — not just the endpoint list.
Then tell your agent what to build in one sentence, e.g.:
The agent follows the skill’s playbook and consults these docs for exact fields. The skill works with Claude Code, Codex, Cursor, and 70+ other agents. Source: legnext-ai/skills (MIT).
Docs MCP server
This site serves a read-only Model Context Protocol server at:
No API key or authentication is required. The server exposes tools to search the documentation and to query its pages directly, so answers always come from the live site rather than a cached snapshot.
Claude Code
Cursor
Other MCP clients
Verify the connection:legnext-docs should show as connected. Your agent can now search these docs during any session. Open Cursor Settings → MCP → Add Server, set the type to HTTP, and paste:The server appears in your MCP tool list immediately. Any MCP-compatible client can connect using the streamable HTTP transport at https://docs.legnext.ai/mcp. See the MCP specification for client setup. Every page of these docs has a page menu in the top-right corner with Copy MCP server URL, Copy MCP install command, Open in Cursor, and Open in VS Code — the fastest way to connect from the page you’re reading.
Paste this prompt
No skill support and no MCP in your tool? Paste this into any agent that can fetch URLs:
llms.txt and llms-full.txt
Both files are plain text, always in sync with the live docs, and require no authentication:
llms.txt — an index of every page with a one-line description. Small; load it first when you want the agent to decide which pages to read.llms-full.txt — the entire documentation in a single file: every endpoint, parameter table, and example. Load it when you want full context in one shot.
Coming soon: machine-readable parameter schema
We’re publishing a public JSON schema of every image and video generation parameter and its per-version support, so agents can validate requests before they hit the API. It will be announced in the Changelog.
Until then, the Image Parameters page is the authoritative reference for which parameters each Midjourney version (including V8.2) accepts — and the docs MCP server can search it live. 
