Model Context Protocol

Earendil has published a blog post titled 'You Said No MCP', which is drawing strong discussion on Hacker News. The piece appears to address the Model Context Protocol, the emerging standard for connecting AI assistants to external tools and data, and the company's position on adopting it. Readers are debating the arguments in the comments.

A new blog post from networking company Earendil, titled 'You Said No MCP,' has drawn heavy attention on Hacker News, gathering over 500 points and nearly 300 comments. The piece appears to address decisions around adopting or rejecting the Model Context Protocol, an emerging standard for connecting AI systems to tools and data, prompting lively argument among developers about when saying no to MCP makes sense.

Earendil has published a new post explaining why its Pi.dev product does not support MCP, the Model Context Protocol that has become a common standard for connecting AI assistants to external tools. The piece defends the decision to skip the protocol, and the argument is drawing attention and debate among developers following the discussion.

The Model Context Protocol project's servers repository, written in TypeScript, is gaining traction among developers. It hosts reference server implementations for MCP, an open standard for connecting AI applications to external data sources and tools. Interest appears tied to the broader boom in AI tooling and agent integrations.

Mario Zechner, creator of the pi.dev coding assistant, spent over a year publicly refusing to support Anthropic's Model Context Protocol, calling the stance a matter of philosophy rather than an unfinished feature. The pi.dev homepage once declared outright that Pi does not support MCP. That position has now changed, with MCP support added despite the earlier dismissals.

Earendil's Pi, an AI coding assistant, previously declined to support the Model Context Protocol on its own website but is now integrating it into its core. The shift has prompted discussion of what MCP is and how the assistant's position on the open protocol changed, drawing interest from developers following AI tooling standards.

Engineers are discussing a core challenge in agentic AI: while connecting an AI agent to a database is technically straightforward, making the data genuinely usable and reliable for the agent is far more difficult. One write-up describes building an agentic data factory using Parquet, DuckDB and the Model Context Protocol, arguing that data engineering foundations matter more than credentials or access alone.

Model Context Protocol, or MCP, is being framed as the standard that lets AI agents plug into a company's APIs and tools. But commentary from The New Stack stresses that the protocol only provides access — it does not govern what an agent should or should not see once connected. That means questions of permissions, filtering and data exposure still fall on developers and platform owners rather than on MCP itself.

A developer has published a guide for building a Model Context Protocol (MCP) server that serves documented basketball player data through a typed Sportmicro API integration, written in TypeScript. The tutorial walks through exposing the player insights endpoint so MCP clients can query structured basketball statistics. It is drawing attention from developers interested in connecting sports data to AI tooling.