AnthropicWhy we built—and donated—the Model Context Protocol (MCP)
CHAPTERS
Model Context Protocol (MCP): the basic idea
Stuart frames MCP as a way to make language models useful beyond generating text by connecting them to software (and sometimes hardware). David introduces MCP as an open standard that lets AI applications access external capabilities in a consistent way.
The integration problem MCP solves (and why proprietary connectors aren’t enough)
David explains that models used to feel “trapped in a box,” requiring manual copy/paste. MCP aims to let developers write an integration once and reuse it across many apps and model providers instead of rebuilding the same connector repeatedly.
The USB‑C analogy: one connector for many devices
Stuart compares MCP to USB‑C: a common interface that prevents a mess of incompatible cables. David agrees the metaphor isn’t perfect but captures the core idea of a shared “language” enabling interoperability.
How MCP started: internal workflows, Claude Desktop, and ‘CSP’
MCP began as an internal effort to help Anthropic researchers and engineers use Claude in daily work. David describes early discussions with Justin Spahr-Summers, initial names like “Claude Connect” and “Context Server Protocol,” and sketching the protocol on a whiteboard in London.
What made MCP different: open source participation and big-lab bootstrapping
David argues MCP’s novelty wasn’t tool-connection itself, but creating a neutral protocol usable by anyone and running it as a participatory open source project. He also notes it helped that it came from a major lab to seed initial adoption quickly.
Adoption without mandates: from practical use to de facto standard
The conversation compares MCP’s growth to open-science norms and preprint adoption: it spread because it was useful, not because anyone mandated it. David emphasizes focusing on day-to-day practicality over formal standardization early on, while acknowledging future innovation trade-offs.
From internal hackathon to Hacker News: the moment MCP ‘took off’
David recounts an internal hackathon where many employees built MCP services—including playful hardware demos like 3D printer integrations—validating demand. When MCP was open-sourced (late November), it stayed atop Hacker News for days and quickly attracted server builders and client adopters like Cursor.
Why open source was the right call (and the internal dynamics)
Stuart asks whether there was controversy about open-sourcing MCP. David acknowledges typical company tensions but says leadership support—especially from CPO Mike Krieger—enabled them to commit to an open ecosystem without second-guessing.
Donating MCP to the Linux Foundation: preventing the ‘rug pull’
David explains that MCP was owned by Anthropic (including trademarks and some code), which can create trust concerns for a standard. Donating MCP shifts legal ownership and key governance protections to a neutral nonprofit, reassuring the ecosystem that licensing/trademarks won’t be changed unilaterally later.
What changes (and what doesn’t): governance, maintainers, and the registry
Day-to-day project operation remains the same: a small core maintainer group and broader maintainers continue to run MCP. What changes is legal neutrality and stewardship; additionally, the MCP server registry is included in the donation, with potential funding support for its operation.
Agentic AI Foundation: who’s involved and why it exists
David describes the Agentic AI Foundation as a Linux Foundation “sub-foundation,” similar to PyTorch or Rust foundations, meant to host and coordinate agentic AI open-source projects. It includes major stakeholders (Anthropic, Google, Microsoft, Amazon, Bloomberg, Block, Cloudflare) to create shared community space and mutual benefits across projects.
Criticisms and risks: security, prompt injection, and supply-chain realities
The first major criticism discussed is security: MCP can make it easy to ingest tools from unknown sources, creating risks like prompt injection and data exfiltration. David stresses that many mitigations sit with model providers and app developers, though the protocol can offer guardrails (e.g., read vs write tool permissions).
Criticisms and constraints: context bloat, statefulness, and when MCP isn’t ideal
Another criticism is “context bloat” when clients naively stuff long tool lists and results into the model context window. David also notes debates about using MCP vs command-line tools in some environments and acknowledges MCP’s stateful nature can create scaling challenges—areas they expect to improve.
What’s next: scaling, long-running Tasks, richer UI, and how to engage
Looking forward, David highlights community growth (events, participation) and protocol evolution: better scaling approaches, the new ‘Tasks’ concept for long-running work and agent-to-agent patterns, and richer UI via MCP Apps (collaboration spanning community efforts and multiple vendors). He closes with advice: build, prioritize user experience so MCP stays invisible, and engage through community channels like Discord.
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