Client
AI Assistant
JetBrains AI Assistant provides project context and can consume external MCP tools.
JetBrains AI Assistant can call external Model Context Protocol servers, but Graphify.homes does not currently provide a native JetBrains installer or a public hosted MCP endpoint. The supported architecture is a team-managed MCP bridge over Graphify graph artifacts: JetBrains AI Assistant calls the bridge, and the bridge exposes read-only graph queries backed by the repository's graph.json. Without that bridge, JetBrains should use GRAPH_REPORT.md and graph.json directly as project context.
Published Updated
Graphify stores inspectable artifacts so codebase knowledge can be reviewed by humans and reused by AI coding assistants.

Client
JetBrains AI Assistant provides project context and can consume external MCP tools.
Bridge
A compatible MCP server must expose read-only query, path, explain, or affected operations.
Artifact
The bridge queries Graphify's persistent project graph instead of rescanning source per prompt.
JetBrains AI Assistant codebase context includes open project files, editor selection, local search, project rules, and tool results available to the assistant. MCP extends that context with external tools, but the team must provide and operate the server that exposes those tools. A Graphify bridge only improves the answer when its graph is fresh and its tool responses stay small enough for the assistant to reason with.
No. Graphify CLI 0.9.34 lists installers for Trae, Kiro, Aider, Copilot, Cursor, Codex, Claude, and other agents, but no JetBrains installer. Graphify.homes also has no public MCP endpoint at api.graphify.homes, so the page must not pretend that a one-click hosted connection exists. A separate product with a similar name may advertise a hosted MCP service, but that is not part of this site's runtime.
Install the JetBrains MCP Server plugin, open Settings, and configure AI Assistant's Model Context Protocol settings for the project or IDE. The exact UI and transport support can change by IDE release, so follow the current JetBrains documentation instead of copying a stale screenshot or endpoint. Configure only a server your team controls and verify its tool list before using it on a private repository.
A useful bridge exposes bounded read-only operations: search nodes, explain a symbol, find a path between nodes, list affected nodes, and return report context. Responses should include file paths, confidence labels, and the graph revision so AI Assistant can verify claims against source. Broad tools such as arbitrary shell execution or unrestricted file reads increase risk without improving the structural query.
Use graph queries for cross-module dependencies, call paths, subsystem boundaries, and architecture impact. Use JetBrains local search for exact text, current file contents, compiler errors, and recent edits that the graph has not yet indexed. The graph is a navigation layer, while the IDE's compiler integration is the source of truth for type and syntax errors.
Run the bridge in a trusted local or private environment, authenticate every client, and expose only read-only graph operations. Rebuild the graph after structural changes and include its revision in MCP responses so stale architecture cannot silently guide edits. Store access tokens outside the repository and keep source-derived graph content within the same data boundary as the code.
graphify extract .
graphify query "How does the billing service use the account model?"No. The current Graphify CLI does not include a native JetBrains installer.
No. The site provides hosted ZIP graph builds and downloadable artifacts, but no public MCP server at this time.
It can use project files when they are available in context, but structured querying requires a compatible MCP bridge.
Use GRAPH_REPORT.md as the architecture briefing and graph.json for lookup, then verify all relevant source and tests.
Continue through the Graphify topic cluster with pages that explain assistant context, architecture review, documentation, and graph-based codebase understanding.
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GitHub Copilot codebase context beyond file search
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MCP codebase knowledge graph for AI coding assistants
Use an MCP codebase knowledge graph to give Codex, Claude, and other agents structured queries for dependencies, architecture, and project context.
Start from the use cases page or upload a project ZIP to build a graph that your team can inspect and reuse.