Real-world MCP: How companies are using MCP in production today
When Anthropic released the Model Context Protocol (MCP)
To understand the real impact of MCP, we investigated how companies are implementing it in production, from Zed
Here’s what we learned.
Block’s MCP implementation
Between Cash App
Block’s AI agent goose
The MCP-enabled system democratizes data access at Block: Employees who don’t know SQL can solve their own data problems by describing what they need in plain English. Security analysts create detection rules by describing threats naturally instead of wrestling with complex query syntax.
Block developers can build an MCP server for any tool and instantly make it available to AI agents. As the setup uses OAuth with short-lived credentials, employees don’t need to manage API keys.
Three-quarters of Block’s engineers report saving 8-10 hours per week using goose. The tool has become so effective that one engineer says
Zed makes MCP feel native
The team behind performance-focused code editor Zed
Take Zed’s database integration. You can type /pg-schema users
and get the schema for your users table instantly.
But it gets more interesting with Zed’s Neon integration, which lets you safely modify production databases:
That’s the kind of workflow that would be nerve-wracking without proper guardrails, but MCP’s stateful sessions make it possible to build safely.
The technical implementation is clean: MCP servers run as separate processes that communicate with Zed’s Agent Panel via stdio
, with support for HTTP and server-sent events coming soon. Extension developers can register servers in their extension.toml
and implement a simple context_server_command
method.
Replit’s MCP playground
Replit
Recently, Replit pivoted to agent-powered coding, positioning itself as an AI-first development platform for vibe coding entire applications with zero setup.
Replit’s MCP templates come with pre-configured servers, so you can start a new environment and have working AI integrations without installing or configuring anything yourself.
The Learn About MCP template includes YouTube processing, filesystem access, and external API integration out of the box. The configuration lives in a simple JSON file:
What makes this compelling is that you can type something like “Summarize this video https://youtu.be/1qxOcsj1TAg
Replit Agent takes this multi-service coordination further by automatically integrating services like EmailJS and Mailtrap when you describe what you want to build. Ask for a React app with email functionality, and the agent configures the MCP servers, sets up the integrations, and builds the app. It’s the kind of seamless experience that makes MCP’s complexity worthwhile.
Code intelligence platforms embrace MCP
Sourcegraph
Sourcegraph implements MCP through OpenCtx
The workflow feels natural: Cody can analyze your database schema to suggest query optimizations, pull GitHub issues directly into your editor context, or search across multiple repositories with semantic understanding. It’s code intelligence that actually understands the broader context of your work.
Codeium took a different approach with the Windsurf
Windsurf also features UI panels for managing MCP servers, one-click setup for popular servers, and proper sandboxing. It’s the kind of polish that suggests MCP integration is becoming table stakes for development tools.
Solana blockchain data access
Several developers have built MCP servers that make Solana blockchain data accessible to AI assistants. While you can’t execute transactions, these tools turn complex blockchain queries into simple conversations.
The most documented implementation comes from QuickNode’s comprehensive tutorial
The Solscan MCP
Both Solana MCP implementations focus on data access rather than transaction creation. You can ask an AI assistant to “analyze this wallet’s DeFi activity” or “explain what happened in this transaction” and get useful answers without any security risks. The read-only design means you get the benefits of natural language blockchain analysis without worrying about accidental transactions or compromised keys. For most blockchain analysis use cases, this is exactly what you want: insight without risk.
Stripe introduces paid MCP servers
The Stripe Agent Toolkit
When an AI agent calls a paid tool, the MCP server checks if the user has already paid. If not, it creates a Stripe checkout session and returns a payment URL. After payment, the tool becomes available.
The system supports one-time payments, subscriptions, and usage-based billing through Stripe’s metering API. Developers can charge per API call, search query, or processing job.
The growing MCP ecosystem
The MCP ecosystem has grown to over 15,000 community-built servers, creating powerful network effects.
AWS ecosystem integration
The hundreds of services available from AWS are notoriously complex to navigate. MCP servers simplify this by letting AI assistants work directly with AWS through standard interfaces.
Amazon Bedrock Samples
The AWS Cloud Development Kit (CDK)
Cost Analysis tools decode AWS billing complexity through usage analysis for optimization recommendations, CloudWatch-based rightsizing suggestions, and cost forecasting with budget alerts.
Database platform innovations
MCP bridges conversational AI with complex data operations, letting developers work with sophisticated databases using natural language instead of learning new APIs.
DataStax Astra DB
The ClickHouse MCP Server
The server handles query optimization automatically, turning natural language questions into optimized SQL that processes billions of events in seconds.
Creative tool integration
Creative MCP implementations solve real workflow problems for artists, game developers, and content creators working with complex tools.
BlenderMCP
MCP Unity Editor
The script generation understands Unity’s patterns and conventions, producing properly structured C# code with appropriate component references rather than generic code that won’t work in Unity’s context.
Why companies are choosing MCP
Let’s take a look at the compelling technical and business reasons companies are adopting MCP.
The USB moment for AI tools
Just as the USB standard
Without MCP, connecting an AI assistant to Zendesk, Salesforce, and Slack means building three separate integrations from scratch. Each integration needs custom authentication, error handling, and data formatting – like having a different port for every device.
With MCP, you write one server implementation that exposes Zendesk’s capabilities to any AI system that speaks the protocol. Instead of building N integrations for every M tools (N x M complexity), you only need to build N servers and M clients. This matters when you’re dealing with dozens of internal tools and multiple AI agents.
Inherent security
MCP creates a single chokepoint for AI access to company systems. Instead of tracking permissions across dozens of custom integrations, connections flow through MCP servers that can be monitored, logged, and controlled consistently.
For example, Gram’s implementation of MCP
With Gram, you can create separate staging
and production
environments, each with its own credentials and server URL tailored to different access requirements.
Switching models without rebuilding from scratch
Since MCP servers are model-agnostic, companies can experiment with different AI providers without throwing away their tool integrations. This flexibility helps with avoiding vendor lock-in, testing new models for specific tasks, and lowering costs as inference pricing comes down.
Getting started with MCP
If you’re considering MCP for your organization, MCP.so
To learn more about MCP and how to try it out, check out our quickstart guide to installing MCP servers.
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