David Soria Parra co-created the Model Context Protocol while working at Anthropic, starting from a simple annoyance: constantly switching between Claude Desktop and his code editor to copy-paste context. That frustration became an open standard now supported by ChatGPT, Cursor, Gemini, and VS Code, with over 97 million monthly SDK downloads.
Background
- Member of Technical Staff at Anthropic (joined April 2024)
- Co-creator and Lead Core Maintainer of MCP
- Previously at Meta/Facebook (2013-2023), leading 45+ person org focused on static analysis and developer tools
- Entrepreneur in Residence at Sutter Hill Ventures (2023)
- Core PHP developer who shipped PHP 5.4 and 5.5 releases
- Built Mercurial features: bookmarks, pager, shelving
- Education: Karlsruhe Institute of Technology (2007-2013)
- Based in London
GitHub | Twitter | Site | MCP Docs
MCP Origins
The protocol started at an Anthropic hackathon in 2024. Soria Parra and Justin Spahr-Summers wanted to solve what he calls “the M times N problem”: multiple AI clients needing to connect to multiple data providers. Without a standard, every integration requires custom work.
“MCP tries to enable building AI applications in such a way that they can be extended by everyone.”
Think USB-C: one connector instead of a different cable for every device. MCP provides:
| Component | Purpose |
|---|---|
| Tools | Functions the AI can call (file operations, API requests) |
| Resources | Data the AI can read (files, database schemas) |
| Prompts | Reusable prompt templates servers can expose |
| Sampling | Servers can request LLM completions back from the client |
Architecture
MCP uses a client-server model where the AI host connects to multiple servers, each exposing capabilities:
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/files"]
},
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_TOKEN": "your-token"
}
}
}
}
The protocol supports stdio (local processes) and HTTP with Server-Sent Events (remote servers). Claude Desktop, Cursor, and VS Code all support MCP as clients.
M x N Problem
Before MCP:
- 5 AI clients x 10 data sources = 50 custom integrations
With MCP:
- 5 AI clients implement MCP client once
- 10 data sources implement MCP server once
- Everything connects
This is why adoption spread fast. OpenAI, Microsoft, Google, Docker, and GitHub all added support. Building one MCP server makes your tool accessible to every MCP-supporting AI client.
Underused Features
Soria Parra notes that most developers only use tools, ignoring other primitives:
| Feature | What It Does |
|---|---|
| Sampling | Server requests LLM completions from the client (multi-agent patterns) |
| Resources | Expose readable data with URIs and MIME types |
| Prompts | Server-provided prompt templates users can invoke |
| Notifications | Real-time updates between client and server |
Sampling is the interesting one. An MCP server can request LLM completions from the client, so your server could analyze data, ask Claude for a summary, then return structured results. Multi-agent patterns without managing API keys on the server side.
Open Source Governance
In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation. Co-founded with Block and OpenAI, the foundation also hosts goose (Block’s AI agent) and AGENTS.md (OpenAI’s agent configuration standard).
Soria Parra remains Lead Core Maintainer. The donation means:
- Neutral governance
- No single company controls the spec
- Infrastructure support from Linux Foundation
- Same maintainers making technical decisions
Key Takeaways
| Principle | Implementation |
|---|---|
| Solve your own problem | Copy-paste frustration became a protocol |
| Protocol over products | M x N reduction from standardization |
| Open source from day one | Community contributions shaped the spec |
| Underutilized features matter | Sampling, resources, prompts need documentation |
Related Articles
- Jason McGhee’s WebMCP - Turning websites into MCP servers
- Three-Layer Workflow - Spec, implement, verify patterns
- Context Management - Managing what AI can see