This post breaks down why MCP servers fail, six best practices for building ones that work, and how Skills and MCP complement each other. It emphasizes designing MCP servers as user interfaces for AI agents, focusing on outcomes, flattened arguments, clear instructions, curation, discoverable naming, and pagination.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
This article provides a comprehensive guide on implementing the Model Context Protocol (MCP) with Ollama and Llama 3, covering practical implementation steps and use cases.
A Model Context Protocol (MCP) service that provides access to Ansible Automation Platform (AAP) APIs through OpenAPI specifications.
This article details seven pre-built n8n workflows designed to streamline common data science tasks, including data extraction, cleaning, model training, and deployment.
A guide to setting up local LLMs on Linux using LLaMA.cpp, llama-server, llama-swap, and QwenCode for various workflows like chat, coding, and data analysis.
This section details how to load and use multiple models with the llama.cpp server. It covers configuring the server to handle multiple models, the model path format, and considerations for memory usage.
APIs let you get at fascinating and useful treasure troves of data. Here’s a look at the wide world of APIs for finding and manipulating data in your applications.
This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
The Model Context Protocol (MCP) is a new open protocol that allows AI models to interact with external systems in a standardized, extensible way. In this tutorial, you’ll install MCP, explore its client-server architecture, and work with its core concepts: prompts, resources, and tools.
Fly.io provides a secure and fast platform for deploying AI workflows and LLM-generated code using ephemeral, kernel-isolated virtual machines (Fly Machines). It offers features like secure sandboxing, fast startup times, a clean slate for each run, a simple API, and support for whole applications, not just code snippets.
