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Model Context Protocol (MCP)
What is Model Context Protocol (MCP)?
Introduced by Anthropic in late 2024, Model Context Protocol (MCP) is an open-source standard for connecting AI models to external tools, systems, and data sources. MCP enables business tools to communicate more effectively with AI models—specifically, with LLMs such as Claude and ChatGPT.
For Communication Platform as a Service (CPaaS) providers, MCP synchronizes and routes contextual data between sources to better serve customers and end users. Sources include:
- LLMs (Large Language Models)
- Speech-to-text / Text-to-speech engines
- CRM systems
- Agent dashboards
- Multi-turn chatbots
- Voice AI agents
- Messaging channels (WhatsApp, SMS, RCS, etc.)
Why is MCP important?
Before MCP, integrating an LLM into a business tool or API required a custom-built and often one-way integration. Anthropic’s rollout of MCP provides a “build once, use everywhere” standard that simplifies and improves communication flow.
You can think of MCP like the universal USB-C port for AI. Remember when every device had a different charger, until we aligned on a single, standard port? MCP is the universal standard that developers are rallying around to create a common protocol for connecting AI to valuable tools, from Salesforce to Spotify and beyond.
By enabling access to third-party tools and datasets, MCP enables AI providers and their end users to have more effective, relevant conversations, and unlocks key decision-making and agentic capabilities.
Here are some communications use cases you can accomplish with MCP:
- Create a phone number lookup request to filter out numbers that can’t receive messages so your campaigns reach active phones
- Create alerts based on voice traffic thresholds to proactively monitor and address errors
- Find out the requirements for activating end users in a new country and create a new user with all the necessary criteria
- Protect your users with one-time codes and MFA account verification
MCP’s robust context fidelity turns AI from a ‘bot gimmick’ into a dependable business assistant.
MCP vs. RAG
Retrieval-Augmented Generation (RAG) is another valuable AI tool, but it offers different functionality from MCP. When a user asks a question, the RAG system searches available data sources for relevant information that can inform the AI model’s response. MCP servers take this a step forward by allowing AI to access unstructured data (does not need to be pre-embedded or stored in a vector database) for more dynamic and real-time queries, and perform actions.
While RAG can look up the answers to questions, MCP servers can actually take action to complete a requested task.
These tools are not exclusive and can be combined for sophisticated AI deployment. For example, RAG could be used to query support articles and troubleshoot a user’s issue. MCP servers can be used to open a support ticket on behalf of the user if they aren’t able to self-service a solution.
Benefits of MCP
MCP offers a host of benefits, including:
- Agentic AI: MCP allows developers to build AI agents that can perform multi-step, complex tasks. More than simple chatbots, AI agents built around MCP can interact with customers in a seamless, automated communication workflow.
- Resiliency and Scalability: Because MCP is an open source, vendor-neutral protocol, developers can build platforms that integrate with both current and future LLMs, such as Claude, ChatGPT, and Google Gemini. This flexibility gives businesses access to a resilient and scalable protocol.
- Reduced Hallucinations: MCP servers act as a bridge between customer data and the LLM, allowing the LLM to access real-time, factual data and reduce the possibility of hallucinations.
MCP is the necessary protocol that puts full AI functionality and productivity in the hands of developers.
How is Bandwidth involved with MCP?
Bandwidth’s locally hosted MCP server allows customers to seamlessly integrate their agent of choice with Bandwidth APIs. Customers can download the Bandwidth MCP package and configure it locally for easy, secure use. The APIs and their dev docs are then continuously updated on the customer’s side in real time, ensuring users always have the latest capabilities and features.
Learn more about AI at Bandwidth
See the highlights of how Bandwidth is using MCP and other tools and protocols to make AI-boosted communications possible for enterprises and platforms.
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