A reference manual for people who design and build MCP (Model Context Protocol) ecosystems
A reference manual for people who design and build MCP (Model Context Protocol) ecosystems
A reference manual for people who design and build MCP (Model Context Protocol) ecosystems
The Tower of Babel, Redux
The Tower of Babel, Redux
The Tower of Babel, Redux
The chaos before the protocol • Why your AI can't talk to your tools • The M×N integration nightmare that keeps engineers up at night
The chaos before the protocol • Why your AI can't talk to your tools • The M×N integration nightmare that keeps engineers up at night
The chaos before the protocol • Why your AI can't talk to your tools • The M×N integration nightmare that keeps engineers up at night
The Universal Language That Never Was
The Universal Language That Never Was
Picture this: It's 3 AM. Sarah, a data scientist at a Fortune 500 company, stares at her screen with the particular brand of exhaustion that comes from wrestling with systems that refuse to talk to each other. Her AI assistant let's call it Claude can write poetry about quantum physics and debug Python in its sleep. But ask it to pull last quarter's sales data from their CRM? Might as well be asking it to perform actual magic.
"I have an AI that can explain the meaning of life," she mutters to her cold coffee, "but it can't read a simple spreadsheet from our company drive."
Sound familiar?
Picture this: It's 3 AM. Sarah, a data scientist at a Fortune 500 company, stares at her screen with the particular brand of exhaustion that comes from wrestling with systems that refuse to talk to each other. Her AI assistant let's call it Claude can write poetry about quantum physics and debug Python in its sleep. But ask it to pull last quarter's sales data from their CRM? Might as well be asking it to perform actual magic.
"I have an AI that can explain the meaning of life," she mutters to her cold coffee, "but it can't read a simple spreadsheet from our company drive."
Sound familiar?
Picture this: It's 3 AM. Sarah, a data scientist at a Fortune 500 company, stares at her screen with the particular brand of exhaustion that comes from wrestling with systems that refuse to talk to each other. Her AI assistant let's call it Claude can write poetry about quantum physics and debug Python in its sleep. But ask it to pull last quarter's sales data from their CRM? Might as well be asking it to perform actual magic.
"I have an AI that can explain the meaning of life," she mutters to her cold coffee, "but it can't read a simple spreadsheet from our company drive."
Sound familiar?
1.1 Bridging System
1.1 Bridging System
The M×N Problem (Or: Why Engineers Drink)
The M×N Problem (Or: Why Engineers Drink)
Here's the thing about connections they multiply faster than rabbits in springtime.
Let's say you have 5 AI applications. Not unreasonable, right? Maybe Claude for general assistance, GitHub Copilot for coding, a custom RAG system for documentation, an AI-powered analytics tool, and that experimental agent your intern built last Tuesday.
Now, you want these to connect to your company's 10 essential data sources: Slack, Gmail, Google Drive, your PostgreSQL database, Jira, Salesforce, that legacy system nobody talks about, your data warehouse, the API management platform, and SharePoint (because someone always insists on SharePoint).
Quick math: 5 × 10 = 50 custom integrations.
Here's the thing about connections they multiply faster than rabbits in springtime.
Let's say you have 5 AI applications. Not unreasonable, right? Maybe Claude for general assistance, GitHub Copilot for coding, a custom RAG system for documentation, an AI-powered analytics tool, and that experimental agent your intern built last Tuesday.
Now, you want these to connect to your company's 10 essential data sources: Slack, Gmail, Google Drive, your PostgreSQL database, Jira, Salesforce, that legacy system nobody talks about, your data warehouse, the API management platform, and SharePoint (because someone always insists on SharePoint).
Quick math: 5 × 10 = 50 custom integrations.
Here's the thing about connections they multiply faster than rabbits in springtime.
Let's say you have 5 AI applications. Not unreasonable, right? Maybe Claude for general assistance, GitHub Copilot for coding, a custom RAG system for documentation, an AI-powered analytics tool, and that experimental agent your intern built last Tuesday.
Now, you want these to connect to your company's 10 essential data sources: Slack, Gmail, Google Drive, your PostgreSQL database, Jira, Salesforce, that legacy system nobody talks about, your data warehouse, the API management platform, and SharePoint (because someone always insists on SharePoint).
Quick math: 5 × 10 = 50 custom integrations.
1.2 Puzzle System
1.2 Puzzle System
The Parable of the Protocols
The Parable of the Protocols
Humanity has been here before.
Remember USB? (Stay with me here.) In the late '90s, connecting devices to computers was its own special circle of hell. Printers needed parallel ports. Mice wanted PS/2. Keyboards demanded their own special connector. Scanners? Good luck. Each device spoke its own language,
needed its own driver, had its own idea of how data should flow.
Then USB came along and said: "What if... everything just worked the same way?"
Revolutionary. Scandalous, even. One port to rule them all.
Humanity has been here before.
Remember USB? (Stay with me here.) In the late '90s, connecting devices to computers was its own special circle of hell. Printers needed parallel ports. Mice wanted PS/2. Keyboards demanded their own special connector. Scanners? Good luck. Each device spoke its own language,
needed its own driver, had its own idea of how data should flow.
Then USB came along and said: "What if... everything just worked the same way?"
Revolutionary. Scandalous, even. One port to rule them all.
1990s
2010
2020
Future
1990s
2010
2020
Future
1990s
2010
2020
Future
1.3 Revolution
1.3 Revolution
The Language Barrier
The Language Barrier
But here's where our story gets interesting. The problem isn't just about connections—it's about language.
Your AI speaks in tokens and embeddings. Your database speaks SQL. Slack speaks in webhooks and JSON. Your spreadsheet speaks in cells and formulas. It's like hosting a dinner party where every guest speaks a different language, and you're desperately running around with Google Translate trying to keep the conversation going.
Each system has its own way of saying simple things:
- "Get me that file"
- "Run this search"
- "Update this record"
- "Send this message"
Multiply this by every possible action across every possible system, and you begin to see why Sarah is on her third coffee at 3 AM.
But here's where our story gets interesting. The problem isn't just about connections—it's about language.
Your AI speaks in tokens and embeddings. Your database speaks SQL. Slack speaks in webhooks and JSON. Your spreadsheet speaks in cells and formulas. It's like hosting a dinner party where every guest speaks a different language, and you're desperately running around with Google Translate trying to keep the conversation going.
Each system has its own way of saying simple things:
- "Get me that file"
- "Run this search"
- "Update this record"
- "Send this message"
Multiply this by every possible action across every possible system, and you begin to see why Sarah is on her third coffee at 3 AM.
SELECT * FROM users;
SELECT * FROM users;
GET /users
GET /users
{ users { id name } }
{ users { id name } }
$ get-users
$ get-users
frustration meter
frustration meter
frustration meter
1.4 The Babel Fish Translator
1.4 The Babel Fish Translator
The Hidden Cost of Chaos
The Hidden Cost of Chaos
Time
Every new integration is a project. Every project needs planning, development, testing, deployment. Months pass. Markets shift. Opportunities vanish.
Time
Every new integration is a project. Every project needs planning, development, testing, deployment. Months pass. Markets shift. Opportunities vanish.
Talent
Your best engineers—the ones who should be building the future—are instead building bridges. Again. And again. And again
Talent
Your best engineers—the ones who should be building the future—are instead building bridges. Again. And again. And again
Innovation
When 80% of your effort goes into making things talk to each other, only 20% goes into making things worth talking about.
Innovation
When 80% of your effort goes into making things talk to each other, only 20% goes into making things worth talking about.
Sanity
There's a reason "integration engineer" has become synonymous with "person who has seen things." Those things usually involve SOAP, XML transforms, and authentication schemes that would make Kafka proud.
Sanity
There's a reason "integration engineer" has become synonymous with "person who has seen things." Those things usually involve SOAP, XML transforms, and authentication schemes that would make Kafka proud.
ai apps
Apps
data sources
Apps
developer hours needed
Hours
Maintenance burden
Coffee budget
ai apps
Apps
data sources
Apps
developer hours needed
Hours
Maintenance burden
Coffee budget
ai apps
Apps
data sources
Apps
developer hours needed
Hours
Maintenance burden
Coffee budget
1.5 Calculator
1.5 Calculator
Time
Every new integration is a project. Every project needs planning, development, testing, deployment. Months pass. Markets shift. Opportunities vanish.
Time
Every new integration is a project. Every project needs planning, development, testing, deployment. Months pass. Markets shift. Opportunities vanish.
Talent
Your best engineers—the ones who should be building the future—are instead building bridges. Again. And again. And again
Talent
Your best engineers—the ones who should be building the future—are instead building bridges. Again. And again. And again
Innovation
When 80% of your effort goes into making things talk to each other, only 20% goes into making things worth talking about.
Innovation
When 80% of your effort goes into making things talk to each other, only 20% goes into making things worth talking about.
Sanity
There's a reason "integration engineer" has become synonymous with "person who has seen things." Those things usually involve SOAP, XML transforms, and authentication schemes that would make Kafka proud.
Sanity
There's a reason "integration engineer" has become synonymous with "person who has seen things." Those things usually involve SOAP, XML transforms, and authentication schemes that would make Kafka proud.
The Dream of Connection
The Dream of Connection
But what if? What if your AI could simply... connect? What if, like USB promised for hardware, there was a universal way for AI to plug into any data source, any tool, any system?
What if, instead of building fifty bridges, you built one protocol?
What if that 3 AM debugging session never needed to happen because the connection just... worked?
This is where our story truly begins. Because in late 2024, a group of engineers at Anthropic looked at this mess and said five beautiful words: "There has to be a better way."
But what if? What if your AI could simply... connect? What if, like USB promised for hardware, there was a universal way for AI to plug into any data source, any tool, any system?
What if, instead of building fifty bridges, you built one protocol?
What if that 3 AM debugging session never needed to happen because the connection just... worked?
This is where our story truly begins. Because in late 2024, a group of engineers at Anthropic looked at this mess and said five beautiful words: "There has to be a better way."
1.6 The Vision
1.6 The Vision