MCP Prompt Optimizer is an MCP server that enhances prompt quality using research-backed prompt engineering strategies and standardized domain templates. It connects language models and MCP clients directly to a local Python engine that evaluates, rewrites, and refines prompts across varying task complexities. AI developers, prompt engineers, and content creators use it to eliminate ambiguous instructions, structure outputs, and apply complex reasoning methods to their workflows. The server provides both core optimization techniques—such as chain of thought, specificity constraints, role assignment, and few-shot formatting—and advanced frameworks, including Tree of Thoughts, Constitutional AI, Medprompt, and Self-Refine. In addition to dynamic rewrites, it supplies pre-built domain templates across fields such as business analysis, software development, legal compliance, and product management. By integrating these optimization methods into client interfaces like Claude Desktop, users can automatically assess prompt weaknesses and generate higher-performing instructions without leaving their conversation environment.
Category: AI & LLM Tooling
Tags: llm-performance, optimization, prompt-engineering, prompt-refinement
bash git clone https://github.com/Bubobot-Team/mcp-prompt-optimizer cd mcp-prompt-optimizer 2. Set up a Python 3.8+ virtual environment: bash python3 -m venv venv source venv/bin/activate 3. Install dependencies by executing ./install.sh or running pip install -r requirements.txt. 4. Run the automated configuration helper python3 setup_interactive.py, or manually edit your Claude Desktop configuration file (such as ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows): json { "mcpServers": { "prompt-optimizer": { "command": "python3", "args": ["/path/to/mcp-prompt-optimizer/prompt_optimizer.py"], "env": {} } } } 5. Restart Claude Desktop to load the new server.analyze_prompt to detect ambiguity, lack of constraints, and missing structural requirements. * Rewriting system instructions with tree_of_thoughts or chain_of_thought to navigate multi-step reasoning and complex architecture tasks. * Implementing safety, alignment, and self-critique parameters on sensitive text using the constitutional_ai strategy tool. * Generating production-ready templates for code reviews, SOPs, and user research via the get_domain_template command. * Selecting optimization strategies automatically through auto_optimize to refine draft prompts for general everyday tasks.Part of MCP Servers
Clone the repository from GitHub, set up a Python 3.8+ virtual environment, and install dependencies using install.sh or pip install -r requirements.txt. Then add the server command pointing to prompt_optimizer.py in your Claude Desktop configuration file.
The server includes core tools like analyze_prompt, optimize_prompt, auto_optimize, and get_prompt_template. It also provides advanced tools including advanced_optimize, get_domain_template, and list_domain_templates for accessing research frameworks and production templates.
Basic strategies include clarity, specificity, chain of thought, few-shot examples, structured output, and role-based framing. Advanced strategies include Tree of Thoughts, Constitutional AI, Medprompt, Self-Refine, Automatic Prompt Engineer, Meta-Prompting, and TEXTGRAD.
Yes, MCP Prompt Optimizer is open source software. It is distributed under the MIT License, and the source code is maintained on GitHub.