wigolo

Local-first web intelligence MCP server for AI coding agents. Ships ten tools over stdio. All network results land in a local knowledge cache.

Host-LLM synthesis (read me first)

Wigolo has no internal LLM. It returns structured evidence so the calling model (you) writes the final answer. Fold structure into your reply rather than collapsing it away:

• search with format: "highlights", ML-scored passages + citations. Quote and cite [N].
• research, when MCP sampling is unavailable (common), the output carries a brief with topics, highlights, key_findings. Use it as the scaffold for the report you write.
• find_similar, may return a cold_start string. Pass it to the user; it explains why results came from the web and how to warm the cache.
• extract with mode: "structured", one call for tables + <dl> definitions + JSON-LD + chart hints + key-value pairs.
• fetch metadata, surfaces og_type, canonical_url, and og_image; use canonical_url to dedupe tracked/canonical URLs.

Quick Setup

Claude Code:

claude mcp add wigolo -- npx wigolo

Cursor / VS Code / any MCP client:

{
  "mcpServers": {
    "wigolo": {
      "command": "npx",
      "args": ["wigolo"]
    }
  }
}

Warmup (recommended, one-time):

npx wigolo warmup          # installs browser engine + bootstraps search engine
npx wigolo warmup --all    # also installs Firefox, WebKit, ML reranker, and embeddings
npx wigolo warmup --force  # wipe search engine state and rebuild

Warmup flags: --force, --all, --reranker, --firefox, --webkit, --embeddings, --no-searxng, --verify.

Tools

fetch

Fetch a single URL and return clean markdown. Use when you already have a specific URL.