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Search Stack
Managed search API your AI agent can run end-to-end
Full-text, vector, geo, and image search over one JSON API — with typeahead, a drop-in autocomplete widget, and official TypeScript/.NET clients. What makes it different: the entire index is controllable via MCP (read and write, not just querying), so an AI agent can create lists, add records, and tune search for you. And built-in evaluations let you prove a relevance change is actually better before you ship it. Lucene-backed, no cluster to babysit.
Maker here 👋 SearchStack is the search backend I wish I'd had: POST JSON records, get typeahead + full-text + vector + geo + image search back, over one API. The part I'm most excited about is that everything — including writes and index management — runs over MCP, plus a Claude Code plugin (/plugin install searchstack@searchability), so your agent can stand up and refine search without glue code. There's also an evaluations harness so relevance tuning is measured, not guessed. Would love your feedback on the agent-first workflow.
how does the MCP write access handle permissions when multiple agents could be tuning the same index at the same time
How does the MCP write access actually get scoped in practice? Like, can I lock an agent to only mutate a specific index or field so it can't accidentally rewrite production data?
The MCP control angle is genuinely useful, I had an agent tune my index while I watched and the eval step caught a regression before I shipped. Typeahead latency feels solid too.
How does the MCP write access handle conflicts if my agent and a teammate are both updating records at the same time, and is there any audit trail to see what changed?
How does the MCP read/write access work in practice when an agent makes changes — is there version history or some way to roll back if an automated tweak tanks relevance on a key query?
About Search Stack on Product Hunt
“Managed search API your AI agent can run end-to-end”
Search Stack was submitted on Product Hunt and earned 4 upvotes and 6 comments, placing #160 on the daily leaderboard. Full-text, vector, geo, and image search over one JSON API — with typeahead, a drop-in autocomplete widget, and official TypeScript/.NET clients. What makes it different: the entire index is controllable via MCP (read and write, not just querying), so an AI agent can create lists, add records, and tune search for you. And built-in evaluations let you prove a relevance change is actually better before you ship it. Lucene-backed, no cluster to babysit.
Search Stack was featured in API (98.4k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 194.2k products, making this a competitive space to launch in.
Who hunted Search Stack?
Search Stack was hunted by Lee Smith. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Lee Smith