Why MCP Matters
Before MCP, every AI agent integration was bespoke. Want your agent to query a database? Write a custom tool. Want it to read from Confluence? Another custom tool. Slack? Jira? Salesforce? Each one is a snowflake integration with its own auth flow, error handling, and schema mapping.
MCP standardizes this entire layer. It defines a protocol for how AI models discover, authenticate with, and invoke external tools. One interface to rule them all.
What We Built With It
We deployed MCP servers for four client integrations in the past two months:
- Legal document management — MCP server wrapping iManage with document search, retrieval, and metadata extraction
- Financial data pipeline — MCP server connecting to Bloomberg Terminal APIs with real-time market data access
- HR knowledge base — MCP server over Workday and internal wikis for employee self-service queries
- DevOps automation — MCP server bridging PagerDuty, Datadog, and AWS for incident response agents
Each took 2-3 days to build. Before MCP, equivalent integrations took 2-3 weeks because we had to build the entire tool-calling infrastructure from scratch for each client.
The Network Effect Is Coming
Here's what makes MCP transformative: every MCP server you build is reusable across any MCP-compatible model. Built an iManage integration for Client A? Client B gets it for free. This creates a flywheel where the ecosystem of available tools grows exponentially.
We're already seeing this internally. Our library of MCP servers is becoming one of our most valuable assets. We predict MCP server development will be a standalone service line within 12 months.
The teams investing in MCP now will have a massive head start when agentic AI goes mainstream. And that's happening faster than most enterprises expect.