mcp

The author of CRMy, a customer context engine for AI agents, seeks feedback on its architecture and value proposition for OpenClaw workflows. The tool aims to solve agent context retention and data integrity issues by providing a typed, auditable state layer rather than a traditional CRM interface.

A new book by Gigi Sayfan guides readers on building multi-agent AI systems from scratch using Python, MCP, and A2A protocols, focusing on custom orchestration rather than third-party frameworks.

The article discusses the gap between pilot and production AI agents, emphasizing that production systems require strict tool access controls, clear contracts, and verification gates to prevent compounding errors.

The article compares 25 open-source AI PPT generation skill tools, recommending automated solutions like guizang-ppt-skill that integrate MCP and Claude Code, covering various styles such as Consulting Style, HTML deck, and more.

The author argues that GraphRAG is fundamentally a data modeling problem rather than just a retrieval algorithm, proposing a five-component architecture using ontologies, knowledge graphs, and an MCP server for unified agent memory.

Arkon is a self-hostable enterprise AI knowledge hub that automatically compiles company documents into a cross-linked knowledge Wiki. Via the MCP protocol, employees' AI clients (such as Claude Desktop) can automatically retrieve relevant context based on their permissions — no manual document pasting required.

The author recommends a modern AI development stack combining autonomous agents with the Model Context Protocol (MCP), Markdown, and HTML, emphasizing a "files over apps" architectural philosophy.

This paper introduces MCP-Cosmos, a framework that integrates generative world models into the Model Context Protocol ecosystem to enhance agent planning and execution through predictive simulation in latent space.

Rhys Sullivan is building Executor, an open-source integration layer for AI agents that provides a unified tool catalog with access controls, approval flows for destructive actions, and support for MCP, OpenAPI, GraphQL, and more. It aims to standardize tool calling across different agents like Cursor and Claude Code.

lean-ctx is an open-source Rust-based context runtime that reduces token costs for AI coding agents like Claude Code, Cursor, Copilot, and others by 60–95% through file read compression and shell output optimization. It operates as a Shell Hook and MCP Server with 56 tools and multiple read modes.

Applied Compute introduces ACL-Wiki, a continual learning memory system built on their Context Engine that logs coding agent interactions from Cursor, Claude Code, and Codex to build an improving Contextbase, roughly doubling the Critical Memory Rate over two weeks. The system uses a Remember-Refine-Retrieve pipeline exposed via MCP server to give coding agents institutional memory that improves with use.

Fitbit Air launched with a new Google Health API that allows developers to build AI agents and services on top of 31 health data points including sleep, heart rate, and SpO2, with webhooks and granular permissions.

OpenEnv, a platform for reinforcement learning environments, is expanding its tutorials, covering topics like evaluating agents, rewards via rubrics, and connecting agents via MCP.

The article analyzes why smarter AI agents like Claude consume more tokens when interacting with human-centric backends like Supabase due to inefficient context discovery. It introduces InsForge, an open-source backend tool designed for agents that provides structured context to significantly reduce token usage and manual interventions.

CodexSaver is an MCP tool that offloads low-risk coding tasks (tests, docs, lint fixes) from Codex to a cheaper model like DeepSeek, achieving ~48% cost savings with ~6s latency.

Friday Studio is an open-source agent runtime that compiles natural language workflows into explicit, version-controlled YAML state machines. It allows developers to build AI agents via chat but ship them as stable configuration files for reliable production use.

This article outlines 10 principles for designing agent-native Command Line Interfaces (CLIs), drawing from experiences with Cloudflare and HeyGen to improve reliability for AI agents.

Microsoft Foundry's Toolbox feature enables durable long-running agents with a single MCP endpoint, reportedly reducing input tokens by 90% and simplifying agent code at cloud scale.

An open-source workshop repository for building a real-world multi-agent AI system featuring a Deep Research Agent and LinkedIn Writing Workflow using MCP servers, Pydantic structured outputs, and agentic engineering with Claude Code subagents.

Basedash is a new MCP server that functions as a data analyst integration for various AI tools.