"Techniques for improving prompt effectiveness" matching MCP servers:

A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

Retrieval-grounded prompt refinement engine that repairs and polishes prompts via a single LLM call, exposing a refine_prompt tool for integration with MCP-compatible hosts.

Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.

A server that transforms a standard Language Model into a dynamic multi-agent system where the model simulates both a Conductor (project manager) and Experts (specialized agents) to tackle complex problems through a collaborative workflow.

Provides custom prompts for Amazon Q by serving markdown files from the local filesystem as prompt templates through the Model Context Protocol.

Provides AI-powered selection and generation of specialized system prompts from a database of over 66 templates for Claude Code. It uses semantic search to find the best matching template and can adapt it to fit specific user tasks and contexts.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

Provides pre-defined prompt templates for AI assistants to generate comprehensive plans for TypeScript projects, API architectures, and GitHub workflows.

A proof of concept MCP server for Apache OFBiz

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A lightweight server that provides curated, high-quality prompts for common development tasks like UI/UX design, project setup, and debugging to enhance AI-powered development workflows.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.

Facilitates access and management of Langfuse prompts through the Model Context Protocol, enabling prompt discovery, retrieval, and integration within clients like Claude Desktop and Cursor.

This MCP server provides a cost-optimized personal-knowledge brain, enabling AI agents to query imported documents via hybrid search and synthesis at minimal monthly spend.

Turns any static website into an MCP-searchable knowledge base by deploying a Cloudflare Worker that provides full-text search tools, enabling AI assistants to search and retrieve content from your site.

An MCP server that provides custom prompts for guiding bot interactions, specifically to avoid using brackets in descriptions.

A Model Context Protocol server that helps users create, validate, manage, and optimize prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).