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All types of databases, Redis, Postgres, Clickhouse, Mysql etc. now display important stats and metrics aswell as most run queries in the Maple service map

Makisuo

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244,474 просмотров • 1 месяц назад •via X (Twitter)

Наука и технологии Финансы

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Maple's service map automatically shows Databases your services are calling, with integrations for providers like Planetscale, Tinybird and Clickhouse coming soon to show more details about the specific Database

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We're excited to share we're partnering with Stripe as part of their Stripe Projects developer preview. Create and manage PlanetScale Postgres and Vitess databases straight from the Stripe CLI. All of your dev tool provisioning and billing now centralized in one place.

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Ben Dicken

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$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

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