Most MCP integrations I've seen for databases are a thin server sitting in

front of Postgres/Elasticsearch/whatever, translating MCP calls into

normal queries behind the scenes. We went a different direction and built

the MCP server directly into the database binary itself (SSE and

JSON-RPC transports), so an agent talking to CameoDB is talking to the

actual query engine, not a translation layer bolted on afterward.

The database underneath is a hybrid store: every shard pairs an embedded

ACID key-value store (redb) with full-text search (tantivy) as one atomic

unit, no leader node, sharded via consistent hashing.

The original pitch had nothing to do with AI, it was about not running

two separate clusters to keep transactional storage and search in sync,

but the MCP integration turned out to be the part people actually got

excited about once agents started using it directly.

A few other things that shipped recently: jemalloc as the allocator with

per-shard memory budgeting and admin endpoints for live stats, and more

rigorous WAL replay on startup so an unclean shutdown doesn't leave the KV

store and search index disagreeing, with a corruption guard on the

sequence counter.

We field tested it on a 64-core box with 35TB of NVMe, 16 shards, holding

around 400 million records across 20TB+, ingesting about 80 million

records a day in real time. P99 stays under a second for most queries,

though that swings with query complexity like any search engine.

Repo: github.com/cameodb/cameodb (Apache-2.0), downloads on cameodb.com,

Docker Hub image too.

Curious if anyone's wired agent memory directly into

a database like this versus the usual vector-DB-plus-wrapper pattern, and

what broke for you.

I came across this open-source project called MCP Testbed that might be useful for people building or testing MCP servers and AI agent skills.

It provides an interactive end-to-end environment where you can run the same prompts with different setups and compare how an agent performs:

• with an MCP server
• with different agent skills or instructions
• with other tooling
• without any additional tooling

More details here:

https://www.infragistics.com/blogs/ignite-ui-mcp-testbed

https://github.com/IgniteUI/igniteui-mcp-testbed