Quick Navigation
- For Non-Technical Readers: Start Here
- For Technical Readers: How MCP Actually Works
- Why This Matters for Organizations
- The Ecosystem Today
- The Future (3-5 Year Outlook)
- Connecting to the Bigger Picture
- The Bottom Line
For Non-Technical Readers: Start Here
If you're not interested in the technical details, read this section and then skip to "Why This Matters for Organizations."
What is MCP in one sentence?
Model Context Protocol is a standardized way for AI systems to connect to external tools and data sources (like USB ports for AI), letting different services plug in and work together without custom wiring for each connection.
The Problem MCP Solves
Right now, if you want your AI to access your calendar, your CRM, your documentation system, and your project management tool, you need four separate custom integrations.
Each integration is built differently. Each has its own authentication, its own data format, its own quirks. If you switch from Claude to GPT to Gemini, you might need to rebuild all of them.
That's not sustainable.
MCP says: "What if there was a standard connector? One that any AI system could use, and any service could implement?"
Build the connector once. Use it everywhere.
What This Enables
With MCP, your AI assistant can:
- Read your files, not just the ones you paste into the chat
- Query your databases directly when answering questions
- Access real-time information from your business systems
- Execute actions on your behalf (with your permission)
- Connect to hundreds of services without custom integrations
The vision: AI that actually has access to the context it needs to be useful, not just the 10,000 characters you can fit in a prompt.
Where We Are Today
MCP is real, it's being used, and growing rapidly.
Anthropic (creators of Claude) launched Model Context Protocol on November 25, 2024. In just five months, the ecosystem has exploded:
- Over 16,000 MCP servers deployed across organizations
- 8 million+ downloads by April 2025
- Major AI vendors (OpenAI, Microsoft, Google) announced support in Q1-Q2 2025
- Community momentum is accelerating beyond early-adopter phase
Where This Is Going
In 3-5 years, MCP (or something like it) will likely be standard infrastructure.
Just like APIs became standard in the 2010s, context protocols will become standard in the 2020s. Every major service will have an MCP server. Every AI system will support MCP clients.
The ones that don't will be left behind.
For Technical Readers: How MCP Actually Works
Let's get specific.
The Architecture
MCP is a client-server protocol that defines how AI systems (clients) communicate with external resources (servers).
Core Components:
MCP Client - Runs inside your AI application (Claude Desktop, custom chat interface, autonomous agent)
- Discovers available MCP servers
- Sends requests to servers based on AI reasoning
- Handles authentication and permissions
- Manages connection lifecycle
MCP Server - Exposes capabilities from external systems
- Defines available tools/functions
- Implements authentication mechanisms
- Returns structured data to client
- Handles errors and edge cases
Protocol Layer - Standardized communication format
- JSON-RPC 2.0 over stdio, HTTP, or WebSocket transports
- Schema definitions for requests/responses using JSON Schema
- Built-in streaming support for large datasets
- Standardized error codes and handling patterns
The Request Flow
Here's what happens when an AI uses MCP to access your calendar:
-
Discovery: Client asks MCP server "What can you do?"
- Server responds with schema: "I can list_events, create_event, update_event, delete_event"
- Each function includes parameters, types, descriptions
-
AI Reasoning: User asks "What's on my calendar tomorrow?"
- AI determines it needs to call
list_eventswithdate=tomorrow - Constructs MCP request
- AI determines it needs to call
-
Authentication: Client sends credentials to server
- OAuth 2.1 support (added March 2025) for third-party services
- API key authentication for internal systems
- Session tokens with automatic refresh handling
- Server validates permissions and scopes
-
Execution: Server queries actual calendar system
- Retrieves events
- Formats as structured data (JSON)
- Returns to client
-
AI Processing: Client receives data, AI generates response
- "You have 3 meetings tomorrow: 9am standup, 11am product review, 2pm customer call"
All of this happens in milliseconds, and the user just sees the answer.
Example: Building an MCP Server
Let's say you want to expose your internal documentation to AI via MCP.
Pseudocode structure (actual implementation would be Python/Node/etc.):
# Define MCP server for documentation system
class DocsMCPServer:
def get_capabilities(self):
return {
"tools": [
{
"name": "search_docs",
"description": "Search internal documentation",
"parameters": {
"query": "string",
"limit": "integer (optional)"
}
},
{
"name": "get_document",
"description": "Retrieve full document by ID",
"parameters": {
"doc_id": "string"
}
}
]
}
def handle_request(self, tool_name, parameters):
if tool_name == "search_docs":
return self.search(parameters["query"], parameters.get("limit", 10))
elif tool_name == "get_document":
return self.retrieve(parameters["doc_id"])
def search(self, query, limit):
# Your actual search logic here
results = your_search_system.query(query, limit)
return {"results": results}
def retrieve(self, doc_id):
# Your actual retrieval logic
doc = your_doc_system.get(doc_id)
return {"content": doc.content, "metadata": doc.metadata}
That's it. You've created an MCP server. Any MCP-compatible AI can now search and retrieve your docs.
How MCP Differs from Alternatives
MCP vs. OpenAI Function Calling
OpenAI's approach:
- Define functions in API request payload
- AI decides when to call them
- Your application executes the function
- Return results to AI in next request
Key difference: Function calling is request-scoped. You define functions per API call. MCP is persistent: the server exists independently, AI can discover it dynamically.
MCP vs. LangChain Tools
LangChain:
- Framework for chaining LLM calls with tools
- Tools are Python/JS code your app runs locally
- Tight coupling between AI logic and tool implementation
MCP:
- Protocol, not a framework
- Tools run on separate servers (can be remote)
- Loose coupling: swap AI systems without changing tools
MCP vs. Traditional APIs
Traditional REST API:
- You write custom integration code for each service
- AI has no standard way to discover what's available
- Different auth patterns, error handling, data formats per service
MCP:
- Standardized discovery, auth, error handling
- AI can reason about capabilities from schema
- One integration pattern for all MCP servers
Performance Considerations
Real-world data from Twilio's MCP implementation shows approximately 25-30% overhead compared to direct API calls. This includes:
- Protocol negotiation and discovery (~50-100ms initial connection)
- Schema validation and type checking
- Additional network hops for remote servers
For most use cases, this overhead is negligible compared to AI inference time (which dominates latency). Organizations report that the benefits of standardization far outweigh the minor performance cost.
The Technical Challenges
MCP isn't perfect. Here's what's hard:
1. Authentication Complexity
Different services have different auth requirements. MCP servers need to handle:
- OAuth 2.1 flows for third-party services (standardized as of March 2025)
- API keys for internal systems
- Session management and token refresh
- Permission scoping (what can this AI actually access?)
The MCP specification recommends a pluggable auth pattern where servers declare supported authentication methods during discovery. The OAuth 2.1 integration added in March 2025 provides a standard flow for most third-party services, significantly reducing implementation complexity.
2. Schema Evolution
What happens when your MCP server adds a new function or changes parameters?
- Do existing clients break?
- How do you version the schema?
- How do clients discover new capabilities?
This is an unsolved problem in the ecosystem.
3. Error Handling
AI systems need rich error information to recover gracefully:
- Rate limits hit
- Authentication failures
- Malformed requests
- Server timeouts
MCP defines error codes, but implementations vary widely in quality.
4. Streaming and Large Data
Some operations return large datasets (search results, log files, database dumps).
- How do you stream data to the AI?
- How do you prevent overwhelming the AI's context window?
- Do you paginate? Summarize? Truncate?
The MCP specification includes native streaming support via Server-Sent Events (SSE) for responses. Real implementations typically use a hybrid approach:
- Small results (<10KB): Return complete response
- Medium results (10KB-1MB): Paginate with continuation tokens
- Large results (>1MB): Stream chunks with metadata, allowing AI to decide when to stop consuming
- Very large datasets: Server-side summarization before transmission
Best practice: Let the AI request additional data incrementally rather than overwhelming its context window upfront.
Why This Matters for Organizations
Whether you're technical or not, here's what you need to understand.
The Strategic Shift
For the last 20 years, integration strategy was: "Buy SAAS tools that integrate with each other."
You'd check: Does this CRM integrate with our email platform? Does our project management tool connect to Slack?
That approach is dying.
Siloed Information: How SAAS Companies Protect Their Moat explains why: SAAS vendors have no incentive to make integration easy. It weakens their lock-in.
MCP changes the game.
Instead of asking "Do these tools integrate?", you ask "Do they support MCP?"
If yes, they integrate with everything. Including AI systems that don't exist yet.
The Competitive Advantage
Organizations that implement MCP infrastructure early will have a 2-3 year advantage.
Here's why:
Today: Most organizations use AI by copying and pasting information into chat interfaces.
- Limited context (what you can fit in a prompt)
- Manual data gathering
- No access to real-time information
- Can't take actions, only give advice
With MCP: AI has direct access to your systems.
- Query databases in real-time
- Search documentation automatically
- Pull data from multiple sources simultaneously
- Execute approved actions (create tickets, send emails, update records)
The difference in capability is massive.
Example: Customer support agent asks AI "Why is this customer's order delayed?"
Without MCP: AI says "I don't have access to your order system. Can you check and paste the details?"
With MCP: AI queries order database, shipping API, inventory system, and customer history in parallel. Returns: "Order 12345 is delayed because the item went out of stock on Oct 15. Restocking is scheduled for Oct 23. Customer has had 2 previous delays in the last 6 months; consider offering expedited shipping on this order."
That's not a 10% improvement. That's a completely different level of capability.
What Organizations Should Do Now
If you're thinking strategically about AI, here's the path:
Step 1: Identify High-Value Data Sources
Which systems contain information that would make AI dramatically more useful?
- CRM (customer data, interaction history)
- Documentation (internal wikis, process docs, troubleshooting guides)
- Databases (product info, inventory, analytics)
- Communication platforms (Slack, email, meeting notes)
Prioritize based on: How often is this information needed + How painful is it to access manually?
Step 2: Implement MCP Servers for Priority Systems
Start with 1-2 high-value sources. Build (or buy) MCP servers that expose them.
The ecosystem now includes both open-source implementations and commercial providers. Major platforms like Block have reported dramatic efficiency gains: their MCP-integrated development tools are saving engineers 50-75% of time on routine tasks, now used by thousands of developers daily. Most organizations start with open-source MCP servers for common services (GitHub, databases, documentation systems) and build custom servers for proprietary systems.
Step 3: Build Internal AI Interfaces That Use MCP
Custom Chat Interfaces explores this, but the short version: don't rely on third-party chat UIs.
Build your own internal interface that:
- Connects to your MCP servers
- Enforces your permission and governance rules
- Logs all AI interactions with your systems
- Allows employees to leverage your data safely
Step 4: Expand Coverage
Once you have 1-2 MCP servers working, expand to more systems. The infrastructure is reusable; each additional server gets easier.
Step 5: Enable Cross-System Workflows
This is where it gets powerful. With multiple MCP servers, AI can orchestrate across systems.
Example workflow: "Prepare a report on Q3 customer issues"
- Query CRM for support tickets (MCP server 1)
- Pull relevant documentation updates (MCP server 2)
- Check product release notes (MCP server 3)
- Cross-reference with sales data (MCP server 4)
- Generate summary report with citations
One prompt. Four systems. Automated.
The Ecosystem Today
Let's ground this in reality. Where is MCP actually being used?
Anthropic's Role
Anthropic created MCP and is actively promoting it. Claude Desktop (their native app) supports MCP out of the box, with official MCP servers for:
- Local filesystem access (read/write files with permission controls)
- GitHub integration (repositories, issues, pull requests)
- Google Drive (document access and search)
- Slack (channel history, message posting)
- PostgreSQL and SQLite (database querying with safety guardrails)
They've positioned MCP as an open standard, encouraging the ecosystem to build on it. The strategy is working: the 16,000+ deployed servers represent massive community adoption in just five months.
Community Implementations
The open-source community has built MCP servers for hundreds of use cases:
- Development tools: Jira, Linear, GitLab, Bitbucket
- Documentation: Confluence, Notion, Obsidian, ReadTheDocs
- Databases: MySQL, MongoDB, Redis, Elasticsearch
- Cloud platforms: AWS (S3, Lambda, CloudWatch), Azure, GCP
- Communication: Discord, Microsoft Teams, Zoom
- Analytics: Datadog, Grafana, Mixpanel
The GitHub MCP topic shows 400+ repositories, with several gaining thousands of stars.
Enterprise Adoption
Enterprises are adopting MCP faster than expected. Block's case study is particularly compelling: their engineering team integrated MCP-based tools into their development workflow, resulting in 50-75% time savings on common tasks like code review preparation, documentation lookup, and environment debugging. These tools are now used by thousands of engineers daily.
Other early adopters include fintech companies using MCP for secure database access, healthcare organizations connecting AI to EHR systems (with appropriate HIPAA controls), and SaaS companies building MCP into their product APIs to enable customer AI integrations.
Competing Standards
MCP started as one option among many, but is rapidly becoming the standard.
Initial landscape (late 2024):
- OpenAI's approach: Function calling + plugins (plugins were deprecated, but function calling is core)
- LangChain/LlamaIndex: Framework-specific tool integrations
- Microsoft: Copilot connectors (proprietary)
- Google: Extensions for Gemini (proprietary)
The Convergence (2025)
Something remarkable happened in Q1-Q2 2025: the major vendors announced MCP support:
- OpenAI (March 2025): Added MCP client support to GPT-4 and ChatGPT Enterprise, while maintaining their function calling API for backward compatibility
- Google (April 2025): Gemini integrated MCP alongside their proprietary extensions, citing developer demand
- Microsoft (May 2025): Copilot for Microsoft 365 gained MCP support, allowing enterprises to connect internal systems
This isn't full convergence (each vendor maintains their proprietary approaches), but MCP is emerging as the interoperability layer. The XKCD "14 competing standards" risk is being avoided through MCP's open, vendor-neutral positioning.
The Future: Where MCPs Are Heading
Speculative, but informed.
Scenario 1: MCP Becomes the Standard (85% Probability - Happening Now)
What's happening:
- Major AI platforms have adopted MCP (OpenAI, Google, Microsoft as of Q1-Q2 2025)
- 16,000+ MCP servers deployed, 8M+ downloads by April 2025
- Developer tooling rapidly maturing (frameworks, testing tools, monitoring)
- Security and governance standards emerging for MCP deployments
What's next:
- Major SAAS vendors will implement MCP servers (Salesforce, Atlassian, ServiceNow, etc.)
- Open-source AI models integrate MCP clients as default
- Enterprise adoption accelerates as major vendors validate the standard
- "MCP integration specialist" becomes a recognized job title
Implications:
- Integration becomes commodity: "Does it have MCP?" is table stakes
- SAAS vendors forced to compete on features, not lock-in (see [LINK: The SAAS Reckoning])
- Organizations can mix and match AI systems without rebuilding integrations
- Early adopters (like Block) maintain 12-18 month competitive advantage
Scenario 2: Coexistence with Proprietary Options (14% Probability)
What happens:
- MCP becomes the standard for interoperability and open integration
- Each vendor maintains proprietary enhancements for platform-specific features
- Developers use MCP for cross-platform tools, vendor APIs for specialized capabilities
- Market splits: commodity integrations via MCP, premium features via proprietary APIs
Implications:
- Higher complexity but manageable with good abstraction layers
- MCP handles 80% of use cases, proprietary APIs handle edge cases
- Organizations maintain flexibility while accessing advanced features
- Standards coexist rather than one winner eliminating others
Scenario 3: Something Better Emerges (1% Probability)
What happens:
- A fundamentally new approach (not yet invented) solves context access more elegantly
- Could be AI-native APIs, semantic web evolution, or something unexpected
- MCP becomes the "RSS of AI" (good idea, early, but not the winner)
Implications:
- Early MCP adopters need to migrate, but learned valuable lessons
- Ecosystem benefits from rapid iteration and competition
Reality Check:
Given the major vendor adoption in early 2025, Scenario 1 is actively happening. The window for alternative standards has largely closed. MCP (or a direct evolution of it) is becoming infrastructure.
The momentum is undeniable: when OpenAI, Google, and Microsoft all adopt the same protocol within three months, that's not a coincidence; that's the market deciding.
Connecting to the Bigger Picture
MCP doesn't exist in isolation. It's part of how organizations become intelligent systems.
Siloed Information (Siloed Information) MCP is the technical solution to breaking silos. If every system has an MCP server, AI can access everything (with proper permissions).
SAAS Evolution ([LINK: The SAAS Reckoning]) SAAS vendors that embrace MCP thrive. Those that resist get left behind. MCP is the forcing function.
Custom Chat Interfaces (Custom Chat Interfaces) You can't leverage MCP fully through third-party chat UIs. You need control over the client side.
Cloud Diversification ([LINK: Multi-Cloud Strategy]) MCP enables switching between AI providers (Claude, GPT, Gemini) without rebuilding integrations. This is critical for avoiding lock-in.
Metadata Matters (Metadata Matters) MCP servers return data, but that data needs metadata to be useful. Rich, well-structured metadata makes MCP responses actionable.
The Bottom Line
Model Context Protocols are infrastructure.
Not flashy. Not exciting. But foundational.
The organizations that build MCP infrastructure now will have AI systems that can actually access the context they need to be useful.
The ones that wait will be stuck copying and pasting into chat boxes while their competitors' AI operates autonomously.
This is a 12-18 month window. After that, you're not early; you're catching up.
The protocol is real. The ecosystem is here. Major vendors have validated it. Block and other early adopters are already seeing 50-75% efficiency gains.
The question isn't whether MCP will become standard infrastructure. The question is whether you're building on it now or waiting until everyone else has a head start.
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