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Synapsor Runner
Give AI agents reviewed database actions, not raw SQL
Most database MCP servers hand your AI agent execute_sql â full read/write authority, one prompt injection away from disaster. Synapsor Runner replaces that with reviewed business actions: tenant-scoped semantic tools like billing.propose_late_fee_waiver. Writes become proposals a human approves outside the model's reach, then apply with guards, receipts, and replay. Run npx @synapsor/runner audit to score your current MCP setup's risk in 60 seconds. Local-first, Apache-2.0, no account needed.
Hi Product Hunt!
I'm Sandesh, the founder of Synapsor. I built this after watching how casually teams were wiring AI agents straight into their databases with execute_sql. One prompt injection away from a very bad day and realizing the missing piece wasn't a better model, it was a real boundary.
The fastest way to connect an AI agent to a database is also the most dangerous: hand it execute_sql(sql: string) and hope it behaves. Most database MCP servers work exactly this way.
We built Synapsor Runner around a different boundary. The model never sees raw SQL. It gets reviewed business capabilities like support.propose_plan_credit. It can inspect tenant-scoped data and create an exact proposal, but it cannot approve or commit the write. A human approves outside the model's reach; guarded writeback then checks the row, tenant, allowed columns, and expected version before applying, and every step leaves a receipt and a replay trail.
You can see your current risk before adopting anything. This scores a typical raw-SQL setup in about 60 seconds, no database or account needed:
npx -y @synapsor/runner audit --example dangerous-db-mcp
And the full proposal, approval, and receipt loop, also with no database:
npx -y @synapsor/runner demo --quick
It's local-first and Apache-2.0. Your database credentials never leave your machine. The contract spec and SQL-like authoring DSL are separate open-source packages, so capabilities get reviewed in Git like any other code. We deliberately don't claim safe arbitrary SQL, prompt-injection prevention, or unbounded multi-row writes. It's a scoped safety layer, not magic.
If you're already letting agents touch a staging database: which business action is hardest to expose without handing the model too much authority? That's the question we keep running into, and I'd love to hear yours.
Code and demo: https://github.com/Synapsor/Syna...
Why we built it: https://synapsor.ai/blog/stop-gi...
Finally ran the audit on my setup and the risk score genuinely surprised me. Love that the proposal-then-approve pattern keeps writes out of the model's reach, such a sensible approach.
About Synapsor Runner on Product Hunt
“Give AI agents reviewed database actions, not raw SQL”
Synapsor Runner was submitted on Product Hunt and earned 4 upvotes and 2 comments, placing #152 on the daily leaderboard. Most database MCP servers hand your AI agent execute_sql â full read/write authority, one prompt injection away from disaster. Synapsor Runner replaces that with reviewed business actions: tenant-scoped semantic tools like billing.propose_late_fee_waiver. Writes become proposals a human approves outside the model's reach, then apply with guards, receipts, and replay. Run npx @synapsor/runner audit to score your current MCP setup's risk in 60 seconds. Local-first, Apache-2.0, no account needed.
Synapsor Runner was featured in Open Source (68.6k followers), Developer Tools (516.2k followers), Artificial Intelligence (474.3k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 224.8k products, making this a competitive space to launch in.
Who hunted Synapsor Runner?
Synapsor Runner was hunted by Sandesh Tiwari. 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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Sandesh Tiwari