Topic
MCP
28 posts tagged “MCP”.
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Securing the Agentic Lakehouse Gateway: Preventing Prompt Injection and Data Exfiltration
Agentic lakehouse gateways face prompt injection and exfiltration through query results. A threat model and defenses for the layer in front of data.
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The Five Layers of an Agentic Lakehouse
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
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Securing the Agentic Lakehouse Gateway: A Threat Model for Prompt Injection, Exfiltration, and the Firewall That Reads Sentences
A threat model for the agentic lakehouse gateway covering prompt injection, exfiltration, and the firewall that reads sentences.
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Building Stateless AI Tool Gateways with FastMCP, the 2026 MCP Spec, and Kubernetes
How to build stateless AI tool gateways with FastMCP, the 2026 MCP specification, and Kubernetes, and why statelessness finally makes MCP scale.
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The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits
The five layers of an agentic lakehouse and where the MCP server sits: storage, catalog, semantic layer, MCP gateway, and agent surface, plus identity.
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Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
Why agentic AI needs a governed semantic layer behind the Model Context Protocol: metric consistency, access control, Apache Ossie for portable.
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Moving From Supply Chain Dashboards to Decision Loops With the Model Context Protocol
Moving from supply chain dashboards to decision loops with MCP: sense, decide, act, and verify, with typed action tools, idempotency keys, and graduated.
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Defending the Lakehouse Gateway Against Prompt Injection and Data Exfiltration
Defending the lakehouse gateway against prompt injection and data exfiltration: per-user identity, no-SQL tool surfaces, volume bounds, and detection.
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Wiring an AI Agent to Apache Polaris with the Model Context Protocol
The catalog is the right attachment point for AI agents working against a lakehouse. Here's how to wire the official Polaris MCP Server and add the read.
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GSA MCP Servers for Open Data AI Agents
An in-depth exploration of gsa mcp servers for open data ai agents
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MCP Stateless Gateways for Data Agents
An in-depth exploration of mcp stateless gateways for data agents
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The State of Agentic AI Standards in 2026: MCP, A2A, WebMCP, OSI, and the Protocol Stack Taking Shape
The agentic AI protocol stack is solidifying in 2026, MCP for tools, A2A for agents, WebMCP for the web, OSI for semantics, payments, identity.
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Conversational AI on Managed Iceberg: Exposing Amazon S3 Tables through MCP
The most interesting part of conversational analytics is not the chat box. The chat box is just the surface area. The harder question is what happe...
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Designing Your Own AI Harness: A Deep Dive Into the Architecture of Agent Loops, Tools, Context, and Control
A deep dive into custom AI harness architecture: model layers, tool design, context management, permissions, control budgets, persistence, orchestration.
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The 2026-07-28 Model Context Protocol Release Candidate: What the Stateless Spec Means for Data Platforms
The date in this topic matters. Today is July 6, 2026. A release candidate dated July 28, 2026 is still in the future. That means this article shou...
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Implementing MCP in the Lakehouse
How to build a Model Context Protocol (MCP) server that exposes lakehouse tables and semantic views as AI-accessible tools, with Python implementation.
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Data Platform Native AI Agent Tooling in 2026
A comprehensive comparison of AI agent tooling across Dremio, Snowflake, Databricks, Microsoft Fabric, AWS, Google Cloud, ClickHouse, VeloDB, SpiceAI.
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A Journey from AI to LLMs and MCP - 10 - Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer
Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer
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A Journey from AI to LLMs and MCP - 9 - Tools in MCP – Giving LLMs the Power to Act
Tools in MCP – Giving LLMs the Power to Act
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A Journey from AI to LLMs and MCP - 8 - Resources in MCP – Serving Relevant Data Securely to LLMs
Resources in MCP – Serving Relevant Data Securely to LLMs
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A Journey from AI to LLMs and MCP - 7 - Under the Hood – The Architecture of MCP and Its Core Components
Under the Hood – The Architecture of MCP and Its Core Components
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Journey from AI to LLMs and MCP - 6 - Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents
Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents
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A Journey from AI to LLMs and MCP - 5 - AI Agent Frameworks – Benefits and Limitations
AI Agent Frameworks – Benefits and Limitations
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A Journey from AI to LLMs and MCP - 4 - What Are AI Agents – And Why They're the Future of LLM Applications
What Are AI Agents – And Why They're the Future of LLM Applications
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A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
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A Journey from AI to LLMs and MCP - 2 - How LLMs Work – Embeddings, Vectors, and Context Windows
How LLMs Work – Embeddings, Vectors, and Context Windows
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A Journey from AI to LLMs and MCP - 1 - What Is AI and How It Evolved Into LLMs
What Is AI and How It Evolved Into LLMs
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Building a Basic MCP Server with Python
The Basics of Building a Basic MCP Server