Results for “tool-design”

Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

Design multi-agent system architectures, generate tool schemas for Anthropic and OpenAI formats, and evaluate execution logs for cost, latency, and failure bottlenecks.

Design n8n AI agents with best practices for node selection, sub-node wiring, tool design, structured output, and memory management.

Manage design systems in Stitch using MCP tools. Includes retrieval of assets, creating/updating design systems in Stitch, and applying them to screens.

Manage design systems in Stitch using MCP tools, including retrieval of assets, creating or updating design systems, and applying them to screens.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Automate Plasmic design and development tasks through Composio's toolkit via Rube MCP, with dynamic tool discovery and connection management.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, and refinement for Python or Node/TypeScript.

MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

Automate Flexisign design and production tasks through Composio's Flexisign toolkit via Rube MCP.

Guides the creation of MCP servers that enable LLMs to interact with external services through well-designed tools.

Design antennas, arrays, and PCB antennas using MATLAB Antenna Toolbox. Covers catalog antenna design and pattern analysis, custom antenna construction (customAntenna + shape.*), PCB antenna design (pcbStack + antenna.*), finite and infinite array design, AI-accelerated design exploration (AIAntenna, patternFromAI), and optimization (SADEA/TR-SADEA). Use when the user wants to design, create, model, analyze, optimize, or fabricate an antenna or array.

Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests. Use when creating a new MCP server, exposing an API or data source through MCP, reviewing an MCP server design, adding or revising MCP tools, or preparing an MCP server for production.

Expert photography prompt engineer specializing in crafting detailed, evocative prompts for AI image generation. Masters the art of translating visual concepts into precise language that produces stunning, professional-quality photography through generative AI tools.

Run computational biology tools for protein structure prediction, design, docking, and molecular dynamics on managed cloud GPUs via REST API or MCP server.

Model software around the business domain using bounded contexts, aggregates, and ubiquitous language, with scoring and diagnostic tools for evaluating domain model quality.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation across multiple programming languages.

Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.

Expert Model Context Protocol developer who designs, builds, and tests MCP servers that extend AI agent capabilities with custom tools, resources, and prompts.

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.

Designs collaborative AI agent teams with CrewAI, covering agent roles, task definitions, crew orchestration, process types, memory, tools, and flows.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate exte...

Build or refactor CLI tools designed for AI agent consumption: non-interactive, flag-driven, idempotent, with --json output and --dry-run preview. Use when creating a new script the agent will call, adding agent-friendly flags to an existing tool, or debugging why an agent keeps failing to use your CLI.

Use when designing or building any terminal user interface — choosing a layout paradigm, keybindings/interaction model, color system, data visualization, or motion. Framework-agnostic universal patterns that work with Ratatui, Ink, Textual, Bubbletea, or any TUI toolkit. For the repo's own pythinker-code TUI, use write-tui instead.

Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

Design and ship production-ready MCP servers from OpenAPI contracts instead of hand-written tool wrappers. Supports Python and TypeScript, schema validation, and safe evolution.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.