coral

Why we included this project

Coral is for teams whose agents need answers that span several systems at once, where the usual approach means chaining dozens of tool calls and stitching results together with brittle prompts. It runs a local SQL engine that can query across APIs, files, and other data sources, so a model answers with one query instead of a pile of tool calls, and you can do the same work yourself from the CLI, inspecting schemas and tables or running joins. The runtime also exposes itself over MCP, giving agents a consistent interface without custom glue code. The project's benchmark against direct provider MCPs for Datadog, Sentry, Linear, Slack, and GitHub found Claude was 31% more accurate and 3.4x more cost efficient on multi-step coding agent tasks, and 20% more accurate with 42% lower latency across all 82 tasks. For teams whose agents regularly chase data across several systems, that is a concrete reason to try Coral before wiring up another stack of per-tool connectors.