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mcp-searchable

mcp-searchable is a local-first Model Context Protocol (MCP) server that helps AI agents perform practical web research:

  • Search with multiple providers (duckduckgo, google, bing, brave)
  • Fetch pages and convert them into readable Markdown
  • Stash useful content into local SQLite storage
  • Grep stashed content with FTS5
  • Ask questions over local context using Ollama

It is built for reproducible, tool-based research flows where the agent can both gather and reuse context.

What does it do?

  • Standardizes web search output across providers into { title, url, snippet }
  • Extracts readable content from raw web pages
  • Persists page content locally so agents can reuse it later
  • Enables local semantic workflows without depending on hosted vector services

Why mcp-searchable?

Most agent workflows lose context between searches and page reads. mcp-searchable gives agents a persistent local knowledge loop:

  1. discover sources (web_search)
  2. extract content (web_fetch)
  3. store useful material (web_stash)
  4. retrieve relevant context (web_grep)
  5. synthesize answers (web_ask)

Next steps