74 posts tagged with "MCP"

There's no official PostgreSQL MCP server from the PostgreSQL Foundation — and there probably won't be, since PostgreSQL is an open-source project without a commercial entity pushing integrations. The community implementations that exist are mostly thin wrappers around psql — run a query, get results.

MCPBundles provides 20+ purpose-built tools that go far beyond raw SQL. Your AI explores schemas, profiles columns, analyzes index health, detects data quality issues, finds duplicates, explains query plans, and exports data — all without you writing a single SQL statement. And if you do want raw SQL, that's there too.

Discord doesn't have an official MCP server. The community implementations that exist are mostly basic bot wrappers — a few tools for sending messages and reading channels. None of them cover the full range of what you'd actually want your AI to do in a Discord server.

MCPBundles provides 13 structured tools built on the official Discord API v10 with proper OAuth2 bot authorization. Your AI reads messages, posts replies, manages threads, reacts to messages, pins important content, and looks up member profiles — all through authenticated API calls with proper permission scoping.

Figma is where product teams live — design files, component libraries, design tokens, comments, activity logs. But AI agents can't see inside Figma unless you give them structured access to the right data at the right granularity.

MCP (Model Context Protocol) solves this by letting AI agents call Figma as a set of typed tools. Instead of pasting screenshots into ChatGPT or manually describing your component hierarchy, the AI reads your files, inspects your components, checks your variables, and posts comments — all programmatically.

There are two ways to connect Figma via MCP: Figma's official MCP server and MCPBundles' 47-tool REST API bundle. This guide covers both.

Google Ads is where most B2B and B2C teams spend their performance marketing budget — campaigns, keywords, RSA ads, budgets, search term reports, geographic and device breakdowns. AI agents that can read and manage Google Ads campaigns can research keywords, build ad groups, write copy, analyze performance, and optimize spend — all through natural language.

MCP (Model Context Protocol) gives AI agents structured access to the Google Ads API. There are two ways to connect: Google's official MCP server and MCPBundles' 24-tool campaign management bundle. This guide covers both.

Every LinkedIn MCP server on GitHub is either a scraper that violates LinkedIn's Terms of Service or a thin wrapper around unofficial endpoints that can break at any time. Some use Patchright (a Playwright fork) to automate the browser. Others reverse-engineer private APIs. LinkedIn actively blocks these — and your account is at risk if you use them.

MCPBundles is the only LinkedIn MCP server built entirely on LinkedIn's official REST API with proper OAuth 2.0 scopes. Your AI manages company pages, publishes posts with images and carousels, engages with comments and reactions, runs ad campaigns, and tracks analytics — all through authenticated API calls that LinkedIn explicitly supports.

Browser automation is one of the most powerful capabilities you can give an AI agent. Navigate to any page, read its content, fill forms, click buttons, take screenshots, inspect network traffic, run JavaScript — all programmatically through natural language.

Playwright is the industry standard for browser automation: fast, reliable, cross-browser, built for modern web apps. There are two ways to connect it to AI agents via MCP: Microsoft's official Playwright MCP server and MCPBundles' browser bundles with local and cloud deployment options.

QuickBooks Online is where millions of businesses manage invoicing, bills, payments, and financial reporting. AI agents that can read and write QuickBooks data can automate invoice creation, pull financial reports, track overdue payments, and reconcile changes — all through natural language.

MCP (Model Context Protocol) gives AI agents structured access to the QuickBooks API. There are two ways to connect: Intuit's official MCP server and MCPBundles' 34-tool accounting bundle. This guide covers both.

Databases are the highest-impact MCP use case we've found. Nothing else comes close in terms of time saved per tool call.

Think about how much of your day involves ad-hoc queries. "How many users signed up this week?" "What's the distribution of plan types?" "Show me the last 10 failed webhook deliveries." Each of these used to mean opening a database client, remembering the schema, writing the SQL, running it, copying the results somewhere useful. With a database MCP server, you describe what you want in plain English and the AI writes the query, runs it, and summarizes the results — in the same conversation where you asked.

We run PostgreSQL MCP as part of our daily workflow at MCPBundles. It handles ad-hoc reporting, data exploration, schema understanding, and debugging. It's the first tool we recommend to anyone evaluating MCP.

Yesterday a support engineer asked "how many workspaces are using custom bundles?" Instead of opening a SQL client, remembering the join between workspace_bundle_access and mcp_bundles, and filtering for user-created bundles — the AI wrote the query, ran it against our read-only replica, and returned the count with a breakdown by plan tier. Thirty seconds from question to answer, including the plan-tier breakdown nobody asked for but everyone wanted.

DevOps engineers live in a dozen dashboards. Datadog for metrics, Sentry for errors, PagerDuty or Opsgenie for on-call, GitHub for PRs, some combination of Terraform and cloud consoles for infrastructure. Every incident means opening five tabs, correlating timestamps across three tools, and context-switching until the problem is resolved or you've forgotten what you were looking at.

MCP servers change this by letting AI agents query those tools directly. Instead of navigating a Datadog dashboard, you ask your agent to pull the metric. Instead of clicking through Sentry issues, you ask it to summarize the top unresolved errors from the last 24 hours. The agent handles authentication, pagination, and response formatting — you stay in one interface.

We run MCPBundles and maintain MCP servers across monitoring (21), cloud infrastructure (19), project management (48), and developer tools (184). This guide covers the ones that matter most for DevOps and platform engineering work.

Two Saturdays ago our error rate spiked at 2 AM. Instead of opening Datadog, Sentry, and GitHub in three separate tabs, one prompt: "Show me the error rate for the API service in the last hour, the top 5 unresolved Sentry issues tagged api, and the last three merged PRs." The AI correlated the spike with a dependency update that shipped at 1:47 AM — a library bump that changed how connection timeouts were handled. Rollback PR was up in 15 minutes. Without MCP, the investigation phase alone would have taken longer than the fix.

Marketing teams run on SaaS. A typical stack includes an analytics platform, an email tool, a CRM, an SEO suite, an ads manager, a social scheduler, and at least three more things nobody remembers signing up for. Every campaign involves switching between tabs, exporting CSVs, copy-pasting numbers into slides, and praying the data matches.

MCP servers change this. Instead of you operating each tool, your AI agent operates them directly — pulling analytics, checking keyword rankings, sending emails, updating CRM records — all from a single conversation. No tab switching, no exports, no manual cross-referencing.

We maintain 88 marketing-category MCP servers on MCPBundles. Some of them are excellent. Some are brand new and still proving themselves. This guide covers the ones we'd actually recommend to a marketing team today, with honest assessments of what works and what's still early.

Here's what this looks like in practice. Last month our blog traffic dropped 15% week-over-week and we had no idea why. One conversation: GSC pulled the top declining pages, Ahrefs showed the keywords that slipped, PostHog confirmed the conversion impact on those pages. Three services, five minutes. The culprit was a competitor who published a nearly identical guide and outranked us on four key terms. We knew what to rewrite before the meeting started.