Mantic is a context-aware code search engine that prioritizes relevance over raw speed. After testing across 5 repositories (cal.com, next.js, tensorflow, supabase, chromium), it demonstrates…
Mantic is an MCP project maintained in the marcoaapfortes/mantic.sh repository. The maintainer describes its purpose as “Mantic is a context-aware code search engine that prioritizes relevance over raw speed. After testing across 5 repositories (cal.com, next.js, tensorflow, supabase, chromium), it demonstrates…”. Read the project documentation for the complete scope.
The documented npm package is mantic.sh. The configuration below comes from the maintainer’s example; adapt its paths and account values to your environment.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| search_files | Primary search (supports semantic: true for neural reranking) |
| get_definition | Go to definition of a symbol |
| find_references | Find usages of a symbol |
| get_context | Zero-query mode for proactive context |
| session_record_view | Track viewed files |
| analyze_intent | Understand query intent |
{
"mcpServers": {
"mantic": {
"command": "npx",
"args": [
"-y",
"mantic.sh@latest",
"server"
]
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
Retrieve public web pages as readable Markdown with the reference Fetch MCP server, including pagination for longer responses.
Use Firecrawl MCP to retrieve website content and extract information for research through Firecrawl’s API-backed web tools.
The original Chromium automation reference server — simple, screenshot-driven browser control.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Automates browsers and APIs, enabling Large Language Models (LLMs) to interact with web pages for tasks like screenshot capture and JavaScript
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.