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Run BM25 search on llms.txt indexes and add sources at runtime.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.praveenc/llmstxt-doc-search" MCP server from askskill: Run: claude mcp add 'io-github-praveenc-llmstxt-doc-search' -- npx -y @praveenc/llmstxt-doc-search
Search the connected llms.txt document indexes with BM25 for "tool calling" and "context window", then return the most relevant results.
A ranked list of the most relevant document entries or snippets.
Add a new llms.txt document index as a search source, then search for content related to "authentication".
Relevant search results from the newly added source after it is connected.
Search for "agent memory" across the Strands, Kiro, and AWS document indexes, and summarize the matches by source.
Relevant matches grouped by source for easy comparison across documentation sets.
When developers or researchers need to find a term across multiple llms.txt document indexes, they can use this tool to run a unified BM25 search. It helps them find relevant documentation faster instead of checking each source manually.
When the existing indexes are not enough, users can add new search sources at runtime. This is useful for search workflows that need expanding documentation coverage.
It runs BM25 search over llms.txt document indexes. Known sources include Strands, Kiro, and AWS, and it also supports adding new sources at runtime.
The description explicitly mentions Strands, Kiro, and AWS. Beyond those, it supports adding more sources at runtime.
The provided material does not include installation or configuration steps. Please see the source repository for details.
Search project docs semantically to find relevant files before coding changes.
Search, scrape, and query official docs for fast, reliable development answers.
Search versioned documentation for AI tools and coding assistants semantically.
Make MDX documentation searchable and answerable for AI assistants.
Enable full-text search across Markdown and MDX documentation folders.
Lets AI search and query documentation with flexible retrieval configurations.