What is the Model Context Protocol (MCP)?
Anthropic developed the Model Context Protocol (MCP), an open standard that connects AI assistants to systems where data actually lives—content repositories, business tools, development environments, and more.
The idea is simple: instead of building one-off integrations for every data source your AI model wants to access, you can plug into a universal protocol that elegantly handles the flow of context between AI and your systems.
Key Objectives of MCP
- Universal access: Provide a single, open protocol that AI assistants (MCP “clients”) can use to query or retrieve data and context from arbitrary sources.
- Secure, standardized connections: Replace ad hoc API connectors or custom wrappers with a protocol that handles authentication, usage policies, and standardized data formats.
- Sustainability: Foster an ecosystem of reusable connectors (“servers”) so developers can build once and reuse them across multiple LLMs and clients—no more rewriting the same integration in a hundred different ways.
Why is MCP important?
More relevant AI
Even advanced language models are often trained on incomplete or outdated datasets. By connecting them to live data—whether that’s Google Drive documents, official API docs, Slack messages, or an internal database—MCP helps ensure the model’s answers are up-to-date, context-rich, and domain-specific.
Unified data access
Before MCP, a developer might have to juggle separate plugins, tokens, or custom wrappers to give an AI system access to multiple sources. With MCP, you configure one protocol, then the LLM can “see” all registered connectors. It’s a step toward a more uniform, standardized ecosystem.
Long-term maintainability
Ad hoc solutions become a nightmare as your organization adds more data sources. MCP’s open, standardized approach means less breakage and simpler debugging. Instead of rewriting integrations every time you adopt a new platform, you can rely on (and contribute to) a shared library of MCP servers.
MCP core concepts
Servers
A server in MCP terms is anything that exposes resources or tools to the model. For example, you might build a server that provides a “get_forecast” function (tool) or a “/policies/leave-policy.md” resource (file-like content).
Clients
A client is an LLM-based interface or tool (like Claude for Desktop or a code editor like Cursor) that can discover and invoke MCP servers. This is how the user’s text prompts are turned into actual function calls without constantly switching between systems.
Tools, resources, prompts
- Tools: Functions the model can call with user approval (e.g.,
createNewTicket,updateDatabaseEntry). - Resources: File-like data the model can read, such as “company_wiki.md” or a dataset representing financial records.
- Prompts: Templated text that helps the model perform specialized tasks.
Example: weather server in Python
Below is a shortened illustration of an MCP server. It exposes two “tools”—one for weather alerts and one for forecasts:
from mcp.server.fastmcp import FastMCP
import httpx
mcp = FastMCP("weather")
@mcp.tool()
async def get_alerts(state: str) -> str:
"""Get weather alerts for a US state."""
# ...call weather.gov, parse data, return formatted alerts...
return "Active weather alerts..."
@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
"""Get weather forecast for a location."""
# ...call weather.gov, parse data, return forecast...
return "Forecast data..."
if __name__ == "__main__":
mcp.run(transport='stdio')An LLM client can seamlessly invoke get_alerts or get_forecast by referencing the server—there is no need for custom plugin code.
Emerging patterns and future workflows
One of the most compelling aspects of MCP is how it can reshape common development and AI usage patterns as adoption grows:
Agent-led integrations
As LLMs increasingly act as agents—autonomously analyzing tasks and discovering the right tools—they’ll rely on MCP’s standardized directory of capabilities. Imagine a scenario where:
- An LLM tries to accomplish a coding task.
- It consults an MCP server for your Git repo to see recently changed files.
- It then calls a separate “ticket-management” server to update an issue.
- It logs results in Slack or other business tools, all via MCP.
This multi-step, multi-tool workflow becomes simpler because each tool is discovered and invoked through the same protocol, rather than through a patchwork of credentials and ad hoc APIs.
On-demand, scoped access
Because MCP can be configured with scopes (e.g., read vs. write, staging vs. production), developers can let an AI assistant “see” only certain data or only perform read operations.
This drastically reduces the fear of an LLM accidentally overwriting important data—unlocking a safer environment to explore advanced “agentic” behaviors.
Integration-Ready API documentation
Looking forward, as more organizations adopt MCP, we might see them start to publish their APIs as MCP-compliant documentation and connectors. Instead of (or in addition to) providing REST or GraphQL endpoints, companies could automatically generate an MCP server that an LLM can install.
When a developer or agent wants to integrate your platform, they load your official MCP connector, which grants them standardized, documented functions out of the box. This is where companies like Speakeasy come in.
They already generate TypeScript (and other) SDKs from an OpenAPI spec. In the near future, turning on an “MCP” toggle in those generation tools would automatically produce a ready-to-install MCP server that covers your entire API.As more LLM-based clients adopt MCP, it’ll become commonplace for platforms to ship an MCP server that any AI assistant can plug into.
Over time, we could see a new norm: offering “MCP docs” or “MCP endpoints” alongside standard REST or gRPC docs.
Seamless collaboration and knowledge exchange
MCP can unify personal or enterprise knowledge bases. A user might have:
- A local writing knowledge base
- Their corporate Slack logs
- A ticketing system
- A database of product FAQs
An LLM client could access all four via MCP, weaving the data together in a single conversation. Tools like Claude for Desktop already let you spin up multiple MCP servers. As these patterns mature, we’ll see more fluid handoffs between different data sources, bridging siloed systems in a single conversation.
Standardized governance and logging
Enterprises will appreciate that a single protocol can log all AI data access and tool usage. Instead of tracking usage across 10 different custom endpoints, a centralized MCP server can handle authentication, store usage logs, and systematically enforce policy.
This makes compliance and auditing more straightforward, which is critical in regulated industries like finance or healthcare.
Speakeasy: A glimpse of the future
Speakeasy’s approach to generating MCP servers from an OpenAPI spec is a prime example of how these future patterns might evolve. Right now, the code it generates is fairly new, but it lays the groundwork for a world in which:
- Every major API (Stripe, GitHub, Slack, internal microservices) could publish an official MCP connector.
- LLM-based tools (whether code editors or chatbots) automatically discover those connectors, read the built-in descriptions, and know how to call them.
- Developers and even end-users spend less time hand-rolling new integrations, because the entire system is “machine-readable” from day one.
You could imagine a near-future scenario where building a custom integration or writing your own plugin is replaced by installing a verified “MCP toolset” from your vendor—much like installing an NPM package or a Python library today.
Getting started
- Official Anthropic documentation: For a step-by-step introduction, see the MCP quickstart.
- Open-Source connectors: Check out existing servers for Slack, Git, GitHub, Google Drive, Postgres, and Puppeteer.
- Claude Desktop: A convenient way to see MCP in action. Just point your config at a server you built and watch your assistant discover its tools.
- Speakeasy: If you maintain an OpenAPI spec, consider Speakeasy’s new MCP server generation. This can help future-proof your API for AI-driven automation.
Final thoughts
The Model Context Protocol signals a shift from fragmented “integration glue” to a more standardized, open ecosystem where AI assistants can seamlessly fetch, parse, and act on real-world data. As MCP gains adoption, we’re likely to see:
- More agent-driven workflows pulling in data from multiple sources with ease,
- Widespread publication of MCP-compliant docs and servers by API providers,
- Consistent security and logging across AI usage, and
- Streamlined integration that drastically reduces developer overhead.
Embracing MCP now can set you up for a future where context-aware AI is the default—and your data is always just a tool call away.