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ohwise-mcp
MCP server connecting AI coding agents to graph-native code context and multi-agent pipelines.
ohwise-mcp implements the Model Context Protocol so that any MCP-compatible AI agent (Claude Code, Claude Desktop, and others) can:
- Build and query knowledge graphs from code repositories via codebase2graph
- Build and query knowledge graphs from documents via 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
pip install ohwise-mcp[all]Add to Claude Code
claude mcp add ohwise -- ohwise-mcpOr manually in your Claude Code config (~/.claude.json or .mcp.json):
{
"mcpServers": {
"ohwise": {
"command": "ohwise-mcp",
"env": {
"OHWISE_URL": "https://your-ohwise-instance.com",
"OHWISE_TOKEN": "your-token-here"
}
}
}
}OHWISE_URLandOHWISE_TOKENare 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: all, call, entity, schema, workflow, infra, security, web, android, decision, folder
Document graph tools
| Tool | Description |
|---|---|
build_doc_graph(paths, graph_type) | Extract a knowledge graph from documents (PDF, DOCX, MD, HTML, …) |
rank_doc_nodes(query, graph_id, k) | Rank document nodes by relevance — get focused document context |
OhWise Studio tools (requires OHWISE_URL + OHWISE_TOKEN)
| Tool | Description |
|---|---|
start_pipeline(user_input, agent_ids) | Trigger an OhWise coordinator pipeline |
get_pipeline_result(thread_id, poll_seconds) | Poll a pipeline for results |
Example usage in Claude Code
Once configured, Claude Code can use graph context automatically:
> What does the authentication flow look like in this repo?Claude Code calls build_code_graph → rank_code_nodes("authentication flow") → receives ranked nodes with call relationships and content snippets → answers with precise, relationship-aware context.
> Delegate this refactoring task to the OhWise pipeline and get back the planClaude Code calls start_pipeline → OhWise native agents run in parallel → Claude Code receives the synthesized result.
Installation variants
# Graph tools only (no OhWise backend needed)
pip install ohwise-mcp[code] # code graphs
pip install ohwise-mcp[docs] # document graphs
pip install ohwise-mcp[all] # both
# Core only (Studio pipeline tools work without graph extras)
pip install ohwise-mcpPython API
The tools are also importable directly:
from ohwise_mcp.server import mcp
# Run as MCP server
mcp.run()Design principles
- No LLM dependency — graph construction is pure static analysis
- Offline-first — code and document graph tools work without any server
- Composable — works standalone or alongside OhWise Studio
- Model-agnostic — any MCP-compatible agent can use these tools
Related projects
| Package | What it does |
|---|---|
| codebase2graph | Code repository → knowledge graph |
| docs2graph | Documents → knowledge graph |
| graph2sql | Schema graph → SQL context |
Contributing
git clone https://github.com/jw-open/ohwise-mcp
cd ohwise-mcp
pip install -e ".[dev]"
pytest tests/ -vLicense
Apache-2.0 — see LICENSE