MCP - Model Context Protocol
🧱 TL;DR
The Model Context Protocol (MCP) is a powerful and emerging standard designed to enhance how AI systems—especially large language models (LLMs)—interact with external data, tools, and environments. The protocol is built on top of JSON-RPC 2.0.
The rapid enhancements made to the MCP protocol (OAuth 2.1/Transport Protocol) make it very compelling and very easy to use to build AI applications that need to leverage Enterprise data.
🛆 Radar Status
| Field | Value | |
|---|---|---|
| 1 | Technology/Topic Name | MCP - Model Context Protocol |
| 2 | Radar Category | Adopt |
| 3 | Category Rationale | Protocol |
| 4 | Date Evaluated | 2025-08-01 |
| 5 | Version | MCP Protocol Revision 2025-06-18 |
| 6 | Research Owner | Gilles Eveloy |
💡 Why It Matters
- The MCP protocol and its associated SDKs make it super easy to provide an agent (LLM) access to tools or resources. Developers only need to specify MCP server URLs.
- Building applications where an agent (LLM) can handle tasks, query data via tools, and fetch knowledge via resources is key to building useful AI applications.
- MCP is a standard that significantly simplifies the development of agentic applications that act on data.
📊 Summary Assessment
| Category | Status (✅ / ⚠️ / ❌) | Explanation | |
|---|---|---|---|
| 1 | Maturity Level | ✅ | Recent changes make the protocol much more compelling for AI Corporate apps. |
| 2 | Innovation Value | ✅ | Lowers the bar to publish & consume MCP servers. |
| 3 | Integration Readiness | ✅ | Enterprise MCP Servers can be hosted serverlessly and consumed by AI apps. |
| 4 | Documentation & Dev Experience | ✅ | Comprehensive docs, tutorials, and community support available. |
| 5 | Tooling & Ecosystem | ✅ | Major SDKs/language support actively developed & maintained. |
| 6 | Security & Privacy | ⚠️ | OAuth 2.1 authentication added, but tool poisoning and trust remain issues. |
| 7 | Commercial & Licensing Viability | ✅ | Open source, backed by Anthropic/Microsoft/AWS. |
| 8 | Use Case Fit | ✅ | Easier implementation of agentic corporate AI applications. |
| 9 | Performance & Benchmarking | Not evaluated | Not evaluated |
| 10 | Community & Adoption | ✅ | Strong adoption. |
| 11 | Responsible AI | ✅ | Enhancements support secure tool calls and data privacy. |
🛠️ Example Use Cases
- An agentic (LLM) corporate app providing flight data based on a user query like: “I would like to fly to New York City this fall...”
- The agent uses a Flight MCP Server to call the
get-flightstool with relevant parameters. - The agent formats the returned JSON data into a responsive HTML table card.
📌 Key Findings
(Section placeholder for key findings)
🧷 Resources
| Type | Link | |
|---|---|---|
| 1 | Official Website | https://modelcontextprotocol.io/ |
| 2 | GitHub Repo | Model Context Protocol |
| 3 | Documentation | https://modelcontextprotocol.io/ |
| 4 | Benchmark Results | |
| 5 | Sample Notebook | |
| 6 | Radar Entry |
🧠 Recommendation
Tailored advice for specific audiences: - Consultants: - Engineers: - Product Teams:
🔁 Follow-ups / Watchlist
Let's keep an eye on MCP's protocol enhancements around Security and whether it will include a more comprehensive pattern for agent-to-agent communication OR need to be used in conjunction with A2A or ACP.