MCP server exposing graph-native code context, document retrieval, schema ranking, document store, cache, and agent pipeline tools.
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# ohwise-mcp [](https://pypi.org/project/ohwise-mcp/) [](https://pypi.org/project/ohwise-mcp/) [](LICENSE) **MCP server connecting AI coding agents to graph-native code context and multi-agent pipelines.** `ohwise-mcp` implements the [Model Context Protocol](https://modelcontextprotocol.io/) so that any MCP-compatible AI agent (Claude Code, Claude Desktop, and others) can: - Build and query **knowledge graphs** from code repositories via [codebase2graph](https://github.com/jw-open/codebase2graph) - Build and query **knowledge graphs** from documents via [docs2graph](https://github.com/jw-open/docs2graph) - Rank the most relevant nodes for any query using **Personalized PageRank** - Trigger and poll **OhWise Studio pipelines** for multi-agent task execution **Pure Python. No LLM dependency for graph tools. Bring your own model.** --- ## Quick start ```bash pip install ohwise-mcp[all] ``` ### Add to Claude Code ```bash claude mcp add ohwise -- ohwise-mcp ``` Or manually in your Claude Code config (`~/.claude.json` or `.mcp.json`): ```json { "mcpServers": { "ohwise": { "command": "ohwise-mcp", "env": { "OHWISE_URL": "https://your-ohwise-instance.com", "OHWISE_TOKEN": "your-token-here" } } } } ``` > `OHWISE_URL` and `OHWISE_TOKEN` are only required for Studio pipeline tools. Graph tools work offline without them. --- ## Tools ### Code graph tools | Tool | Description | |------|-------------| | `build_code_graph(repo_path, graph_type)` | Extract a knowledge graph from a code repository | | `rank_code_nodes(query, graph_id, k)` | Rank nodes by relevance to a query — get focused code context | | `search_code_graph(keyword, graph_id, kind_filter)` | Find nodes by keyword or kind (function, class, file, …) | **Graph types**:…
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