Companies that have a MCP Server

Source: Analysis of job postings that mention MCP (using the Bloomberry Jobs API)

My analysis shows that MCP purchasing decisions are driven by technical leadership, with Directors of Software Engineering (12%) and Directors of Product Management (10%) leading the charge. These leaders are focused on building what one posting calls "AI-first experiences" and creating platforms that deliver "on-demand, least privileged access to infrastructure." They're investing in teams that can bridge traditional software development with emerging AI capabilities, particularly around agentic systems and generative AI integration.

The day-to-day users are predominantly Machine Learning Engineers (13%), Backend Engineers (11%), and AI Engineers (9%) who are tasked with connecting AI agents to enterprise systems. These practitioners are building what multiple postings describe as "autonomous orchestration tools" and "AI-powered tools used by game, art, marketing, and live ops teams." They're creating MCP servers, managing authentication systems, and integrating AI capabilities into existing workflows to boost developer productivity across their organizations.

The common thread across these roles is the challenge of making AI adoption safe and scalable. Companies repeatedly mention needing "security, observability, and control" for AI systems and the ability to "safely connect agents to their systems." One posting captures this perfectly: "enterprises can't adopt it safely without security, observability, and control." Another emphasizes building solutions with "consent-driven AI at the center" while ensuring systems remain "resilient, scalable, and compliant." These organizations are racing to operationalize AI while managing risk.

Source: Analysis of Linkedin bios of 1,605 companies that use MCP

I analyzed these companies and found that MCP users are predominantly software and technology companies building developer tools, SaaS platforms, and AI-powered products. Many are creating infrastructure for other businesses: analytics platforms, workflow automation tools, testing frameworks, and data management systems. There's a strong representation of companies serving specific verticals like healthcare, e-commerce, real estate, and financial services with specialized software solutions.

These are primarily growth-stage companies. The employee counts cluster heavily in the 11-50 and 51-200 ranges, suggesting companies past the initial startup phase but still scaling. Many have raised seed or Series A funding, typically in the $2M to $20M range. Even the larger enterprises like Meta or SumUp represent the innovative, tech-forward end of their sectors. The smaller companies without funding data often describe ambitious missions that suggest they're building for scale.

Source: Analysis of tech stacks from 1,605 companies that use MCP

I noticed something striking about companies using MCP: they're automation-obsessed businesses that want AI to connect to everything. The extreme concentration of integration platforms like Pipedream, Zapier, and Claude Connectors tells me these aren't companies dabbling in AI. They're building it directly into their operational workflows. They need their AI systems to actually do things, not just answer questions.

The pairing of Claude Connectors with AWS makes perfect sense. These companies are building custom AI applications on cloud infrastructure, and they need those applications to reach into their actual business systems. The Cloudflare Agents correlation is particularly revealing. It suggests they're deploying AI at the edge, making it accessible through APIs and web interfaces rather than keeping it locked in internal tools. When I see Zapier and Pipedream both appearing frequently, it tells me these companies are stitching together complex automation chains where MCP-enabled AI acts as an intelligent routing layer between different services.

The full stack reveals a product-led company in growth mode. They're not spending on expensive enterprise sales tools. Instead, they're investing in infrastructure that lets their product work everywhere their customers already work. The heavy AWS presence suggests they're past the startup phase and running real production workloads. The integration tools indicate they're likely selling to other businesses who need their AI capabilities to mesh seamlessly with existing workflows.