GitWand's MCP server exposes its deterministic conflict engine to AI agents. GitWand resolves the trivial 95% instantly and deterministically; your agent takes over the complex cases with full context — ours/theirs/base content, classification trace and confidence scores. AI is always opt-in, always auditable.
Three steps: the engine handles the deterministic work, the agent handles the judgment calls.
The agent calls gitwand_preview_merge to assess the number of conflicts, their complexity, and the percentage GitWand can resolve on its own.
GitWand instantly resolves trivial patterns (whitespace, one-side-change, same-change…) and returns ambiguous hunks with their classification trace.
For each complex conflict, the agent has full context: ours/theirs/base content, classification trace and confidence scores — then proposes a resolution you can accept or reject.
With Claude Code: claude mcp add gitwand -- npx -y @gitwand/mcp. For Claude Desktop, Cursor or Windsurf, drop the config block into your client. The server is also on the official MCP Registry, so registry-aware clients discover it automatically.
{
"mcpServers": {
"gitwand": {
"command": "npx",
"args": [
"@gitwand/mcp",
"--cwd",
"/path/to/repo"
]
}
}
}For the hunks patterns can't resolve, opt in to LLM resolution via Claude, OpenAI-compatible endpoints, Ollama, or MCP — your model, your key. Every suggestion runs through the same parse-tree validation pipeline as the deterministic patterns, with a decision trace and a reject button. No built-in model, no data leaving your machine unless you choose it.
Deterministic patterns run first and free. The LLM is called only for what's left.
LLM output is parsed and validated like any other resolution — not trusted blindly.
Claude · OpenAI-compatible · Ollama · MCP. Your key, your endpoint, your data.
Full decision trace and a one-click reject on every proposed resolution.