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You’re rewiring your AI agent pipeline for the tenth time today—another brittle API integration, another round of manual context-passing just to keep things from breaking. Hardcoding authentication flows, normalizing API responses, stitching together endpoints—this isn’t AI development; it’s integration hell.
Building AI agents that seamlessly pull data from multiple sources should be effortless, but today’s reality is fragmented, repetitive, and difficult to scale. Every tool speaks its own language, forcing you to hack together workarounds instead of creating real automation.
Anthropic is trying to change that with Model Context Protocol (MCP)—a standardized way for AI agents to retrieve and use external data without the never-ending integration nightmare. But does it solve the problem? Let’s break it down.
What is a Protocol?
A protocol is a set of rules and conventions that define how systems communicate and exchange data. Unlike an API, an implementation-specific interface, a protocol establishes a universal standard for interactions. Some well-known examples include:
- HTTP (Hypertext Transfer Protocol) – Defines how web browsers and servers communicate.
- OAuth (Open Authorization Protocol) – A standard for secure authentication across different platforms.
Protocols ensure interoperability—instead of every system reinventing how data should be exchanged, a protocol standardizes the process, reducing complexity and making integrations more scalable.
While protocols are not mandatory or enforced, the adoption of protocols over time can shape the foundation of how systems interact at a global scale—we saw this with HTTP evolving into the more secure and widely accepted HTTPS, fundamentally changing how data is transmitted across the internet.
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard developed by Anthropic to streamline how AI models access and interact with external data sources.
Instead of requiring AI systems to rely on custom API integrations, manually structured requests, and authentication per service, MCP provides a unified framework for AI agents to retrieve, process, and act on structured data in a standardized way.
In simpler terms, MCP defines how AI models should request and consume external data—whether from databases, APIs, cloud storage, or enterprise applications—without needing developers to hardcode API-specific logic for each source.
Why was MCP Created?
AI models, especially LLMs (large language models) and autonomous agents, need access to external tools and databases to generate accurate, contextual responses. However, current AI-to-API interactions are inefficient and create significant overhead for developers.
Today, integrating an AI agent with external systems requires:
- Custom API integrations for each tool (CRM, cloud storage, ticketing systems, etc.).
- Authentication setup per API (OAuth, API keys, session tokens).
- Manual data formatting to make API responses usable for AI models.
- Rate limit management and error handling across different services.
This approach is not scalable. Every new integration requires custom logic, debugging, and maintenance, making AI-driven automation slow, expensive, and fragile.
By defining a common protocol, MCP makes AI models more data-aware without forcing developers to build custom API bridges for every system they interact with.
How does MCP work?
Today, AI agents rely on custom API calls, per-service authentication, and manual response parsing, creating a fragile web of integrations that are difficult to scale.
Rather than forcing AI agents to interact with APIs in isolation, MCP establishes a unified protocol that abstracts the complexity of authentication, request execution, and data formatting—allowing AI systems to focus on reasoning rather than low-level integration logic.
MCP’s Client-Server Architecture
MCP is built on a client-server model that structures how AI models retrieve and interact with external data sources.
- MCP clients are AI agents, applications, or any system that requests structured data.
- MCP servers act as intermediaries, fetching data from various APIs, databases, or enterprise systems and returning it in a consistent format.
Instead of AI models making direct API requests, MCP servers handle the complexity of authentication, data retrieval, and response normalization. This means AI agents no longer need to manage multiple API credentials, different request formats, or inconsistent response structures.
For example, if an AI model needs to pull information from multiple services like Google Drive, Slack, and a database, it does not query each API separately. It sends a single structured request to an MCP server, which processes the request, gathers data from the necessary sources, and returns a well-organized response.
MCP Request-Response Lifecycle
A typical MCP interaction follows a structured request-response cycle that eliminates redundant API calls and standardizes data retrieval.
1. The AI agent sends a structured request to the MCP server. Instead of crafting individual API requests, the agent defines what data it needs in a uniform format.{
"request_id": "xyz-987",
"queries": [
{"source": "github", "action": "get_recent_commits", "repo": "company/project"},
{"source": "slack", "action": "fetch_unread_messages", "channel": "engineering"}
]
}
2. The MCP server processes the request by validating authentication, checking permissions, and determining which external systems to query.
3. Queries are executed in parallel, meaning data from multiple services is retrieved at the same time rather than sequentially, reducing overall latency.
4. Responses from different sources are standardized into a structured format that AI models can easily process.{
"github": {
"recent_commits": [
{"author": "Alice", "message": "Refactored AI pipeline", "timestamp": "2024-03-12T10:15:00Z"}
]
},
"slack": {
"unread_messages": [
{"user": "Bob", "text": "Hey, can you review the PR?", "timestamp": "2024-03-12T09:45:00Z"}
]
}
}
Unlike raw API responses that require manual parsing, MCP ensures that all retrieved data follows a predictable, structured format, making it easier for AI models to understand and utilize.
Query Execution and Response Aggregation
MCP is designed to optimize how AI models interact with external systems by introducing a structured execution process.
- Request validation ensures that the AI model has the necessary permissions before any data is retrieved.
- Query routing determines which external services need to be accessed.
- Parallel execution retrieves data from multiple sources at the same time, reducing delays caused by sequential API requests.
- Response aggregation consolidates structured data into a single response, eliminating the need for AI models to manually process multiple raw API outputs.
By reducing redundant requests, normalizing responses, and handling authentication centrally, MCP eliminates unnecessary API overhead and makes AI-driven automation more scalable.
Limitations of MCP
Model Context Protocol (MCP) is an important step toward making AI models more capable of interacting with external systems in a structured and scalable way. However, like any emerging technology, it comes with limitations that need to be addressed before widespread adoption.
Authentication Challenges
One of the biggest promises of MCP is to make AI agents less dependent on API-specific integrations. However, authentication (AuthN) remains a major challenge.
Today, API authentication is a fragmented process—some services use OAuth, others rely on API keys, and some require session-based authentication. This inconsistency makes onboarding new APIs time-consuming, and MCP currently does not have a built-in authentication framework to handle this complexity.
MCP still requires some external mechanism to authenticate API requests, which means AI agents using MCP must rely on additional solutions, such as Composio, to manage API credentials. Authentication is on the roadmap for MCP, but until it is fully implemented, developers will still need workarounds to handle authentication across multiple systems.
Unclear Identity Management
Another unresolved issue is identity management—who does an external system see when an AI agent makes a request through MCP?
For example, if an AI assistant queries Slack via MCP, should Slack recognize the request as coming from:
- The end user? (Meaning the AI is acting on behalf of a human.)
- The AI agent itself? (Which would require Slack to handle AI-based interactions separately.)
- A shared system account? (Which could introduce security and access control concerns.)
This issue is even more complicated in enterprise environments, where access control policies determine who can retrieve what data. Without clear identity mapping, MCP integrations could face restricted access, security risks, or inconsistencies across different platforms.
OAuth support is planned for MCP, which may help clarify identity handling, but until this is fully implemented, AI models may struggle with permission-based access to third-party services.
Vendor Lock-in and Ecosystem Fragmentation
MCP is currently an Anthropic-led initiative, which raises questions about its long-term standardization. As AI ecosystems evolve, there is a strong possibility that other major players—such as OpenAI or DeepSeek—will develop their own protocols for AI-to-system interactions.
If multiple competing standards emerge, the industry could fragment, forcing developers to choose between different, incompatible approaches. Whether MCP remains the dominant approach or simply becomes one of several competing options remains to be seen.
Will AI providers standardize around MCP?
MCP offers a universal framework to reduce fragmentation in AI integrations, where each connection currently requires custom solutions that increase complexity.
For MCP to become a widely accepted standard, major AI providers need to adopt it. Companies like OpenAI, Google DeepMind, and Meta have yet to commit, leaving its long-term viability uncertain. Without industry-wide collaboration, the risk of multiple competing protocols remains high.
Some companies have already begun using MCP. Replit, Codeium, and Sourcegraph have integrated it to streamline how their AI agents interact with structured data. However, broader adoption is needed for MCP to move beyond early experimentation.
Beyond AI companies, global standardization efforts could influence MCP’s future. Organizations like ISO/IEC JTC 1/SC 42 are working to define AI integration frameworks. National initiatives, such as China’s AI standards committee, highlight the race to shape the next generation of AI protocols.
MCP is still evolving. If the industry aligns around it, AI integrations could become more interoperable and scalable. However, if competing standards emerge, developers may face a fragmented ecosystem rather than a unified solution.
Build AI Agents that Integrate with APIs
MCP simplifies AI interactions, but authentication and structured API access remain key challenges. Botpress offers OAuth and JWT support, allowing AI agents to authenticate securely and interact with Slack, Google Calendar, Notion, and more.
With the Autonomous Node, AI agents can make LLM-driven decisions and execute tasks dynamically. Botpress provides a structured way to build AI agents that connect across multiple systems.
Start building today—It’s free.
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