Most privacy-preserving AI tools make a policy promise: "we won't look at your data." io.github.Euda1mon1a/koa-fhe makes a mathematical one: the server cannot look at your data.
How FHE Works
Fully Homomorphic Encryption allows computation on encrypted data without decryption. You encrypt your numbers locally. Send the ciphertext to the server. The server performs operations — addition, multiplication, comparison, even machine learning inference — on the encrypted values. Returns encrypted results. You decrypt locally. At no point does the server possess the secret key or see plaintext.
This is not theoretical. The koa-fhe client library, built on Zama's concrete-python FHE compiler, exposes these operations as MCP tools:
| Operation | Latency | Cost |
|---|---|---|
| Encrypted comparison | ~625ms | $0.002 |
| Encrypted addition | ~260ms | $0.001 |
| Encrypted multiplication | ~685ms | $0.002 |
| Schedule rigidity analysis | ~3.5s | $0.05 |
| Full schedule optimization | ~5s | $0.10 |
| Encrypted ML inference (XGBoost) | ~7s | $0.01 |
The Payment Model
All payments settle via the x402 micropayment protocol in USDC on the Base blockchain. No subscriptions, no API keys, no accounts — just pay and compute. The service registers as ERC-8004 Agent #21648 on Base mainnet, giving it a verifiable, blockchain-anchored identity. An autonomous AI agent can discover the service via MCP, verify its identity on-chain, pay per query, and compute on encrypted data — all without human intervention or plaintext exposure.
The Origin Story
The schedule analysis operations suggest a concrete origin: the developer (Euda1mon1a) has a separate medical residency scheduling project. Medical schedules contain sensitive workforce data — who works when, how many hours, fatigue patterns. FHE lets you optimize schedules without exposing the underlying personnel information.
What to Know
Alpha stage. 4 commits, 0 stars, released March 1, 2026. MIT license. The server component is not open-sourced — only the Python client is. FHE operations are inherently slow (even addition takes 260ms), which is a fundamental property of the cryptography, not a code quality issue. The operation set is narrow: six operations. This is a focused tool, not a general-purpose FHE platform.
But the concept is sound and currently unique in the MCP registry. Privacy-preserving computation as a callable tool primitive — discoverable by agents, payable per query, mathematically guaranteed to be blind to your data.
Score: 55. No flags. MIT (client).
Sources: Euda1mon1a — GitHub · koa-fhe — repo · PyPI · Scorecard: io.github.Euda1mon1a (score 55)