5Simon Willison's Weblog·
Simon Willison on stateless MCP and new tools mcp-explorer and datasette-mcp
Simon Willison writes about renewed interest in the Model Context Protocol following developments around stateless MCP, and announces two new tools: mcp-explorer and datasette-mcp. The post reflects on how stateless operation changes the practical appeal of MCP for tool integration. This is a practitioner-level signal about MCP adoption patterns and tooling ecosystem growth.
Related guides (3)
Related events (8)
Simon Willison releases llm-mcp-client 0.1a0, an MCP client for the LLM CLI tool
Simon Willison published an early alpha release (0.1a0) of llm-mcp-client, a plugin that adds Model Context Protocol client support to his LLM command-line tool. The release extends the LLM ecosystem to interoperate with MCP servers, enabling tool use via the standardized protocol. This is an early-stage but concrete addition to the growing MCP tooling ecosystem.
MCP is Dead? — Community Debate on Model Context Protocol's Viability
A blog post from Quandri's engineering team provocatively questions whether the Model Context Protocol (MCP) is failing or already obsolete, generating significant community discussion on Hacker News with 236 points and 206 comments. The piece appears to critically examine MCP's adoption trajectory and potential shortcomings as a standard for AI agent tool integration. The high engagement suggests meaningful disagreement or concern in the practitioner community about MCP's future as an interoperability layer.
MCP Inspector: Visual testing tool for MCP servers gains traction on GitHub
The Model Context Protocol Inspector is an open-source TypeScript tool for visually testing MCP servers, hosted under the official modelcontextprotocol GitHub organization. It has accumulated 10,321 stars with modest daily growth (+15 today). As an official companion tool to the MCP standard, it is relevant to the growing ecosystem of MCP-compatible servers and clients.
Simon Willison walks through adding a custom MCP server to Claude and ChatGPT
Simon Willison documents a practical walkthrough for configuring a custom MCP (Model Context Protocol) server with both Claude and ChatGPT. The post covers the concrete steps required to integrate a self-hosted MCP server into two major AI assistant platforms. This is a practitioner-level guide relevant to the growing MCP ecosystem and cross-platform tool-use patterns.
mcp-use: open-source fullstack MCP framework for building MCP apps and servers
mcp-use is a TypeScript framework on GitHub for developing MCP (Model Context Protocol) applications targeting ChatGPT and Claude, as well as MCP servers for AI agents. The project has accumulated over 10,000 stars, indicating meaningful community adoption. It represents a tooling layer in the growing MCP ecosystem for agent-tool integration.
MCP Server Architecture Patterns: Five recurring patterns and four anti-patterns catalogued from production deployments
An industry experience paper catalogues five recurring architectural patterns for Model Context Protocol (MCP) servers—Resource Gateway, Tool Orchestrator, Stateful Session Server, Proxy Aggregator, and Domain-Specific Adapter—drawn from 15 servers including five production deployments on the ANSYR voice AI platform and ten from the official MCP registry. The paper also documents four anti-patterns and cross-cutting concerns around authentication, versioning, and observability. A quantitative evaluation includes inter-rater reliability (Cohen's kappa = 0.76 on 54 held-out servers), transport overhead measurements, and a tool-count study showing tool-selection accuracy drops below 90% between 10–15 tools for Claude Haiku 4.5 and between 20–30 tools for Claude Sonnet 4. Code, corpus, and prompts are released as a replication package.
Anthropic Open-Sources the Model Context Protocol (MCP)
Anthropic has released the Model Context Protocol (MCP), an open standard enabling secure, two-way connections between AI assistants and external data sources such as business tools, content repositories, and development environments. The protocol introduces a client-server architecture with SDKs, local MCP server support in Claude Desktop, and a repository of pre-built connectors for systems like GitHub, Slack, Google Drive, and Postgres. Early adopters include Block and Apollo, with development tool companies Zed, Replit, Codeium, and Sourcegraph integrating MCP into their platforms. The goal is to replace fragmented, per-source integrations with a single universal protocol, improving context availability for AI agents.
MCP for Research: How to Connect AI to Research Tools
Hugging Face published a blog post explaining how the Model Context Protocol (MCP) can be used to connect AI agents to research tools and data sources. The post covers practical patterns for integrating AI with academic and scientific workflows using MCP as a standardized interface layer. This is a commentary/tutorial piece aimed at researchers looking to extend AI agent capabilities into domain-specific tooling.