prompt-plus-plus-mcp
by bacoco
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PROMPT_PLUS_CUSTOM_DIR | No | Optional path to a directory containing custom prompt strategies |
Capabilities
Features and capabilities supported by this server
| Capability | Details |
|---|---|
| tools | {} |
| prompts | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_strategiesC | List all available metaprompt strategies with descriptions |
| get_strategy_detailsA | Get detailed information about a specific strategy |
| discover_strategiesB | Get comprehensive metadata about all strategy categories and their available strategies for intelligent selection |
| get_performance_metricsA | Get performance metrics for strategy selection and usage |
| health_checkA | Check the health status of the server and strategy manager |
| list_custom_strategiesA | List all custom user-defined strategies with their categories |
| list_collectionsB | List all strategy collections |
| manage_collectionC | Create, update, or delete strategy collections |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| refine_with_adversarial | Refine a prompt using Adversarial Red-Blue Team Engine: Strategic adversarial thinking using red team (attack/critique) vs blue team (defend/build) methodology. Perfect for stress-testing ideas, finding vulnerabilities, and building robust solutions through opposition. Use when you need bulletproof thinking. |
| refine_with_fractal | Refine a prompt using Fractal Recursive Decomposition: Self-similar problem decomposition that reveals patterns across scales. Perfect for complex systems where the same structures repeat at different levels. Use when problems exhibit recursive properties or need multi-scale analysis. |
| refine_with_metacognitive | Refine a prompt using Metacognitive Reflection Engine: Advanced self-reflective thinking that analyzes the thinking process itself. Perfect for complex problems requiring awareness of cognitive biases, assumption examination, and recursive self-improvement. Use when you need to think about how you think. |
| refine_with_quantum | Refine a prompt using Quantum Superposition Thinking: Parallel possibility exploration using quantum mechanics principles. Perfect for navigating uncertainty, exploring multiple solutions simultaneously, and making decisions under ambiguity. Use when reality has multiple valid interpretations. |
| refine_with_synthesis | Refine a prompt using Synthesis Fusion Engine: Creative fusion of disparate concepts, domains, and methodologies. Perfect for innovation, cross-disciplinary thinking, and breakthrough solutions. Use when you need to combine seemingly unrelated ideas into novel approaches. |
| refine_with_temporal | Refine a prompt using Temporal Multi-Timeline Analysis: Time-aware problem solving across past, present, and future dimensions. Perfect for strategic planning, historical analysis, and temporal causality problems. Use when time is a critical dimension of the solution space. |
| refine_with_assumption_detector | Refine a prompt using Assumption Detector: Challenges hidden assumptions in technical requirements, user behavior, system constraints, and implementation approaches. Ideal when you need to uncover implicit beliefs that might limit solution quality or miss critical edge cases. |
| refine_with_constraint_identifier | Refine a prompt using Constraint Identifier: Maps explicit and hidden constraints that limit solution spaces. Distinguishes between real constraints, assumed limitations, and negotiable boundaries to expand possibilities. |
| refine_with_context_expander | Refine a prompt using Context Expander: Broadens the frame of reference to reveal how local decisions affect larger systems. Prevents narrow optimization that creates broader problems. |
| refine_with_devils_advocate | Refine a prompt using Devil's Advocate: Systematically generates counterarguments to proposed technical solutions, helping identify potential failure modes, overlooked risks, and alternative approaches before implementation. Essential for high-stakes decisions and critical system design. |
| refine_with_evidence_seeker | Refine a prompt using Evidence Seeker: Demands concrete evidence and data before accepting claims or making decisions. Transforms vague assertions into measurable, verifiable statements with clear validation criteria. |
| refine_with_paradox_navigator | Refine a prompt using Paradox Navigator: Navigates situations where contradictory requirements or truths coexist. Transforms either/or thinking into both/and solutions that transcend apparent contradictions. |
| refine_with_pattern_recognizer | Refine a prompt using Pattern Recognizer: Identifies recurring patterns, anti-patterns, and hidden regularities across systems, codebases, and problems. Leverages historical patterns to predict future issues and opportunities. |
| refine_with_perspective_multiplier | Refine a prompt using Perspective Multiplier: Analyzes problems through multiple stakeholder lenses, revealing blind spots and conflicts that single-perspective analysis misses. Critical for systems affecting diverse users or teams. |
| refine_with_precision_questioner | Refine a prompt using Precision Questioner: Transforms vague requirements into precise, actionable specifications through systematic questioning. Eliminates ambiguity and reveals hidden complexities before implementation. |
| refine_with_ripple_effect | Refine a prompt using Ripple Effect Analyzer: Traces the cascading consequences of decisions and changes through systems, teams, and time. Essential for understanding the full impact of technical choices beyond immediate effects. |
| refine_with_root_cause_analyzer | Refine a prompt using Root Cause Analyzer: Systematically drills down from symptoms to fundamental causes using techniques like 5 Whys, Fishbone diagrams, and systems thinking. Essential for solving problems permanently rather than treating symptoms. |
| refine_with_time_capsule_test | Refine a prompt using Time Capsule Test: Projects decisions and implementations across multiple time horizons to assess durability, maintainability, and evolution. Prevents short-term thinking that creates long-term problems. |
| refine_with_tradeoff_tracker | Refine a prompt using Tradeoff Tracker: Explicitly maps all tradeoffs in decisions, including hidden costs like technical debt, opportunity costs, and team morale. Makes implicit sacrifices visible and quantifiable. |
| refine_with_arpe | Refine a prompt using Arpe Prompt: Advanced reasoning and proof engineering approach |
| refine_with_bolism | Refine a prompt using Bolism Prompt: Utilize this method when working with autoregressive language models and when the task requires careful reasoning before conclusions. It's best for prompts that need detailed output formatting. Choose this over others when the prompt's structure and reasoning order are crucial. |
| refine_with_done | Refine a prompt using Done Prompt: Opt for this when you want a structured approach with emphasis on role-playing and advanced techniques. It's particularly useful for tasks that benefit from diverse perspectives and complex reasoning. Prefer this over 'physics' when you need a more detailed, step-by-step refinement process. |
| refine_with_math | Refine a prompt using Math Prompt: Specialized approach for mathematical and formal proofs |
| refine_with_morphosis | Refine a prompt using Morphosis Prompt: Use this simplified approach for straightforward prompts or when time is limited. It focuses on essential improvements without complex techniques. Prefer this over other methods when you need quick, clear refinements without extensive analysis. |
| refine_with_phor | Refine a prompt using Phor Prompt: Employ this advanced approach when you need to combine multiple prompt engineering techniques. It's ideal for complex tasks requiring both clarity and sophisticated prompting methods. Select this over 'star' when you want a more flexible, technique-focused approach. |
| refine_with_physics | Refine a prompt using Physics Prompt: Select this when you need a balance between structure and advanced techniques, with a focus on role-playing. It's similar to 'done' but may be more suitable for scientific or technical prompts. Choose this over 'done' for a slightly less complex approach. |
| refine_with_star | Refine a prompt using ECHO Prompt: Use ECHO when you need a comprehensive, multi-stage approach for complex prompts. It's ideal for tasks requiring in-depth analysis, exploration of multiple alternatives, and synthesis of ideas. Choose this over others when you have time for a thorough refinement process and need to consider various aspects of the prompt. |
| refine_with_touille | Refine a prompt using touille Prompt: Use touttouille |
| refine_with_verse | Refine a prompt using Verse Prompt: Choose this method when you need to analyze and improve a prompt's strengths and weaknesses, with a focus on information flow. It's particularly useful for enhancing the logical structure of prompts. Use this over 'morphosis' when you need more depth but less complexity than 'star'. |
| refine_with_architect | Refine a prompt using Software Architect Prompt: Specialized for system design, architecture decisions, and large-scale software planning. Use for prompts about microservices, scalability, design patterns, and technical architecture. |
| refine_with_boomerang | Refine a prompt using Boomerang Code Development: Iterative development approach with feedback loops. Perfect for complex coding tasks requiring multiple iterations, testing, and refinement. Use when you need code that evolves through testing and feedback cycles. |
| refine_with_devops | Refine a prompt using DevOps & Infrastructure Prompt: Specialized for DevOps practices, CI/CD pipelines, Infrastructure as Code, and deployment automation. Use for prompts about containerization, orchestration, monitoring, and cloud infrastructure. |
| refine_with_reviewer | Refine a prompt using Code Review & QA Prompt: Transforms prompts into comprehensive code review and quality assurance guidelines. Perfect for establishing coding standards, review processes, and quality gates. |
| refine_with_collaborate_early_often | Refine a prompt using Collaborate Early and Often: Engage teammates, stakeholders, and AI assistants throughout development. Prevents misalignment, leverages collective intelligence, and improves solution quality. |
| refine_with_delete_aggressively | Refine a prompt using Delete Aggressively: Remove dead code, unused dependencies, and unnecessary complexity ruthlessly. Maintains codebase health by preventing accumulation of technical debt and confusion. |
| refine_with_document_intent | Refine a prompt using Document Intent, Not Implementation: Focus documentation on why decisions were made and what problems are being solved, not how the code works. Code should be self-documenting for the 'how'. |
| refine_with_follow_existing_patterns | Refine a prompt using Follow Existing Patterns: Maintain consistency by following established patterns in the codebase. Reduces cognitive load, prevents architecture drift, and ensures team cohesion. |
| refine_with_keep_files_small | Refine a prompt using Keep Files Under 300 Lines: Maintain code files under 300 lines to enhance readability, maintainability, and testability. Forces good separation of concerns and modular design. |
| refine_with_refactor_continuously | Refine a prompt using Refactor Continuously: Improve code structure and clarity as part of regular development. Prevents technical debt accumulation and maintains code quality without dedicated refactoring sprints. |
| refine_with_run_locally_test_frequently | Refine a prompt using Run Locally, Test Frequently: Maintain a tight feedback loop by running code locally and testing continuously. Catches issues early, validates assumptions quickly, and ensures working code at every step. |
| refine_with_ship_small_changes | Refine a prompt using Ship Small Changes: Deploy small, incremental changes frequently rather than large batches. Reduces risk, enables faster feedback, and improves team velocity. |
| refine_with_start_from_template | Refine a prompt using Start from Template: Leverage proven templates, boilerplates, and architectural patterns to begin projects with solid foundations. Accelerates development while ensuring best practices from day one. |
| refine_with_use_agent_mode | Refine a prompt using Use Agent Mode: Leverage AI coding assistants in agent/autonomous mode for intuitive, conversational development. Maximizes AI capabilities while maintaining developer control and understanding. |
| refine_with_write_tests_first | Refine a prompt using Write Tests First (TDD): Implement Test-Driven Development by writing tests before code. Ensures clarity of requirements, better design, and comprehensive test coverage from the start. |
| refine_with_custom_my-team_code_review | [CUSTOM] Refine a prompt using Team Code Review: Our team's specific code review checklist and standards for consistent, high-quality reviews |
| refine_with_custom_my-team_standup | [CUSTOM] Refine a prompt using Daily Standup Format: Structured format for daily standup updates that keeps meetings focused and efficient |
| refine_with_custom_personal_email_professional | [CUSTOM] Refine a prompt using Professional Email Writer: Transform rough ideas into clear, professional emails with appropriate tone and structure |
| refine_with_custom_advanced_thinking_adversarial | [CUSTOM] Refine a prompt using Adversarial Red-Blue Team Engine: Strategic adversarial thinking using red team (attack/critique) vs blue team (defend/build) methodology. Perfect for stress-testing ideas, finding vulnerabilities, and building robust solutions through opposition. Use when you need bulletproof thinking. |
| refine_with_custom_advanced_thinking_fractal | [CUSTOM] Refine a prompt using Fractal Recursive Decomposition: Self-similar problem decomposition that reveals patterns across scales. Perfect for complex systems where the same structures repeat at different levels. Use when problems exhibit recursive properties or need multi-scale analysis. |
| refine_with_custom_advanced_thinking_metacognitive | [CUSTOM] Refine a prompt using Metacognitive Reflection Engine: Advanced self-reflective thinking that analyzes the thinking process itself. Perfect for complex problems requiring awareness of cognitive biases, assumption examination, and recursive self-improvement. Use when you need to think about how you think. |
| refine_with_custom_advanced_thinking_quantum | [CUSTOM] Refine a prompt using Quantum Superposition Thinking: Parallel possibility exploration using quantum mechanics principles. Perfect for navigating uncertainty, exploring multiple solutions simultaneously, and making decisions under ambiguity. Use when reality has multiple valid interpretations. |
| refine_with_custom_advanced_thinking_synthesis | [CUSTOM] Refine a prompt using Synthesis Fusion Engine: Creative fusion of disparate concepts, domains, and methodologies. Perfect for innovation, cross-disciplinary thinking, and breakthrough solutions. Use when you need to combine seemingly unrelated ideas into novel approaches. |
| refine_with_custom_advanced_thinking_temporal | [CUSTOM] Refine a prompt using Temporal Multi-Timeline Analysis: Time-aware problem solving across past, present, and future dimensions. Perfect for strategic planning, historical analysis, and temporal causality problems. Use when time is a critical dimension of the solution space. |
| refine_with_custom_ai_core_principles_assumption_detector | [CUSTOM] Refine a prompt using Assumption Detector: Challenges hidden assumptions in technical requirements, user behavior, system constraints, and implementation approaches. Ideal when you need to uncover implicit beliefs that might limit solution quality or miss critical edge cases. |
| refine_with_custom_ai_core_principles_constraint_identifier | [CUSTOM] Refine a prompt using Constraint Identifier: Maps explicit and hidden constraints that limit solution spaces. Distinguishes between real constraints, assumed limitations, and negotiable boundaries to expand possibilities. |
| refine_with_custom_ai_core_principles_context_expander | [CUSTOM] Refine a prompt using Context Expander: Broadens the frame of reference to reveal how local decisions affect larger systems. Prevents narrow optimization that creates broader problems. |
| refine_with_custom_ai_core_principles_devils_advocate | [CUSTOM] Refine a prompt using Devil's Advocate: Systematically generates counterarguments to proposed technical solutions, helping identify potential failure modes, overlooked risks, and alternative approaches before implementation. Essential for high-stakes decisions and critical system design. |
| refine_with_custom_ai_core_principles_evidence_seeker | [CUSTOM] Refine a prompt using Evidence Seeker: Demands concrete evidence and data before accepting claims or making decisions. Transforms vague assertions into measurable, verifiable statements with clear validation criteria. |
| refine_with_custom_ai_core_principles_paradox_navigator | [CUSTOM] Refine a prompt using Paradox Navigator: Navigates situations where contradictory requirements or truths coexist. Transforms either/or thinking into both/and solutions that transcend apparent contradictions. |
| refine_with_custom_ai_core_principles_pattern_recognizer | [CUSTOM] Refine a prompt using Pattern Recognizer: Identifies recurring patterns, anti-patterns, and hidden regularities across systems, codebases, and problems. Leverages historical patterns to predict future issues and opportunities. |
| refine_with_custom_ai_core_principles_perspective_multiplier | [CUSTOM] Refine a prompt using Perspective Multiplier: Analyzes problems through multiple stakeholder lenses, revealing blind spots and conflicts that single-perspective analysis misses. Critical for systems affecting diverse users or teams. |
| refine_with_custom_ai_core_principles_precision_questioner | [CUSTOM] Refine a prompt using Precision Questioner: Transforms vague requirements into precise, actionable specifications through systematic questioning. Eliminates ambiguity and reveals hidden complexities before implementation. |
| refine_with_custom_ai_core_principles_ripple_effect | [CUSTOM] Refine a prompt using Ripple Effect Analyzer: Traces the cascading consequences of decisions and changes through systems, teams, and time. Essential for understanding the full impact of technical choices beyond immediate effects. |
| refine_with_custom_ai_core_principles_root_cause_analyzer | [CUSTOM] Refine a prompt using Root Cause Analyzer: Systematically drills down from symptoms to fundamental causes using techniques like 5 Whys, Fishbone diagrams, and systems thinking. Essential for solving problems permanently rather than treating symptoms. |
| refine_with_custom_ai_core_principles_time_capsule_test | [CUSTOM] Refine a prompt using Time Capsule Test: Projects decisions and implementations across multiple time horizons to assess durability, maintainability, and evolution. Prevents short-term thinking that creates long-term problems. |
| refine_with_custom_ai_core_principles_tradeoff_tracker | [CUSTOM] Refine a prompt using Tradeoff Tracker: Explicitly maps all tradeoffs in decisions, including hidden costs like technical debt, opportunity costs, and team morale. Makes implicit sacrifices visible and quantifiable. |
| refine_with_custom_core_strategies_arpe | [CUSTOM] Refine a prompt using Arpe Prompt: Advanced reasoning and proof engineering approach |
| refine_with_custom_core_strategies_bolism | [CUSTOM] Refine a prompt using Bolism Prompt: Utilize this method when working with autoregressive language models and when the task requires careful reasoning before conclusions. It's best for prompts that need detailed output formatting. Choose this over others when the prompt's structure and reasoning order are crucial. |
| refine_with_custom_core_strategies_done | [CUSTOM] Refine a prompt using Done Prompt: Opt for this when you want a structured approach with emphasis on role-playing and advanced techniques. It's particularly useful for tasks that benefit from diverse perspectives and complex reasoning. Prefer this over 'physics' when you need a more detailed, step-by-step refinement process. |
| refine_with_custom_core_strategies_math | [CUSTOM] Refine a prompt using Math Prompt: Specialized approach for mathematical and formal proofs |
| refine_with_custom_core_strategies_morphosis | [CUSTOM] Refine a prompt using Morphosis Prompt: Use this simplified approach for straightforward prompts or when time is limited. It focuses on essential improvements without complex techniques. Prefer this over other methods when you need quick, clear refinements without extensive analysis. |
| refine_with_custom_core_strategies_phor | [CUSTOM] Refine a prompt using Phor Prompt: Employ this advanced approach when you need to combine multiple prompt engineering techniques. It's ideal for complex tasks requiring both clarity and sophisticated prompting methods. Select this over 'star' when you want a more flexible, technique-focused approach. |
| refine_with_custom_core_strategies_physics | [CUSTOM] Refine a prompt using Physics Prompt: Select this when you need a balance between structure and advanced techniques, with a focus on role-playing. It's similar to 'done' but may be more suitable for scientific or technical prompts. Choose this over 'done' for a slightly less complex approach. |
| refine_with_custom_core_strategies_star | [CUSTOM] Refine a prompt using ECHO Prompt: Use ECHO when you need a comprehensive, multi-stage approach for complex prompts. It's ideal for tasks requiring in-depth analysis, exploration of multiple alternatives, and synthesis of ideas. Choose this over others when you have time for a thorough refinement process and need to consider various aspects of the prompt. |
| refine_with_custom_core_strategies_touille | [CUSTOM] Refine a prompt using touille Prompt: Use touttouille |
| refine_with_custom_core_strategies_verse | [CUSTOM] Refine a prompt using Verse Prompt: Choose this method when you need to analyze and improve a prompt's strengths and weaknesses, with a focus on information flow. It's particularly useful for enhancing the logical structure of prompts. Use this over 'morphosis' when you need more depth but less complexity than 'star'. |
| refine_with_custom_software_development_architect | [CUSTOM] Refine a prompt using Software Architect Prompt: Specialized for system design, architecture decisions, and large-scale software planning. Use for prompts about microservices, scalability, design patterns, and technical architecture. |
| refine_with_custom_software_development_boomerang | [CUSTOM] Refine a prompt using Boomerang Code Development: Iterative development approach with feedback loops. Perfect for complex coding tasks requiring multiple iterations, testing, and refinement. Use when you need code that evolves through testing and feedback cycles. |
| refine_with_custom_software_development_devops | [CUSTOM] Refine a prompt using DevOps & Infrastructure Prompt: Specialized for DevOps practices, CI/CD pipelines, Infrastructure as Code, and deployment automation. Use for prompts about containerization, orchestration, monitoring, and cloud infrastructure. |
| refine_with_custom_software_development_reviewer | [CUSTOM] Refine a prompt using Code Review & QA Prompt: Transforms prompts into comprehensive code review and quality assurance guidelines. Perfect for establishing coding standards, review processes, and quality gates. |
| refine_with_custom_vibe_coding_rules_collaborate_early_often | [CUSTOM] Refine a prompt using Collaborate Early and Often: Engage teammates, stakeholders, and AI assistants throughout development. Prevents misalignment, leverages collective intelligence, and improves solution quality. |
| refine_with_custom_vibe_coding_rules_delete_aggressively | [CUSTOM] Refine a prompt using Delete Aggressively: Remove dead code, unused dependencies, and unnecessary complexity ruthlessly. Maintains codebase health by preventing accumulation of technical debt and confusion. |
| refine_with_custom_vibe_coding_rules_document_intent | [CUSTOM] Refine a prompt using Document Intent, Not Implementation: Focus documentation on why decisions were made and what problems are being solved, not how the code works. Code should be self-documenting for the 'how'. |
| refine_with_custom_vibe_coding_rules_follow_existing_patterns | [CUSTOM] Refine a prompt using Follow Existing Patterns: Maintain consistency by following established patterns in the codebase. Reduces cognitive load, prevents architecture drift, and ensures team cohesion. |
| refine_with_custom_vibe_coding_rules_keep_files_small | [CUSTOM] Refine a prompt using Keep Files Under 300 Lines: Maintain code files under 300 lines to enhance readability, maintainability, and testability. Forces good separation of concerns and modular design. |
| refine_with_custom_vibe_coding_rules_refactor_continuously | [CUSTOM] Refine a prompt using Refactor Continuously: Improve code structure and clarity as part of regular development. Prevents technical debt accumulation and maintains code quality without dedicated refactoring sprints. |
| refine_with_custom_vibe_coding_rules_run_locally_test_frequently | [CUSTOM] Refine a prompt using Run Locally, Test Frequently: Maintain a tight feedback loop by running code locally and testing continuously. Catches issues early, validates assumptions quickly, and ensures working code at every step. |
| refine_with_custom_vibe_coding_rules_ship_small_changes | [CUSTOM] Refine a prompt using Ship Small Changes: Deploy small, incremental changes frequently rather than large batches. Reduces risk, enables faster feedback, and improves team velocity. |
| refine_with_custom_vibe_coding_rules_start_from_template | [CUSTOM] Refine a prompt using Start from Template: Leverage proven templates, boilerplates, and architectural patterns to begin projects with solid foundations. Accelerates development while ensuring best practices from day one. |
| refine_with_custom_vibe_coding_rules_use_agent_mode | [CUSTOM] Refine a prompt using Use Agent Mode: Leverage AI coding assistants in agent/autonomous mode for intuitive, conversational development. Maximizes AI capabilities while maintaining developer control and understanding. |
| refine_with_custom_vibe_coding_rules_write_tests_first | [CUSTOM] Refine a prompt using Write Tests First (TDD): Implement Test-Driven Development by writing tests before code. Ensures clarity of requirements, better design, and comprehensive test coverage from the start. |
| auto_refine | Automatically select the best strategy and refine the prompt |
| compare_refinements | Compare multiple refinement strategies for a prompt |
| prepare_refinement | Step 1: Analyze user prompt and return metaprompt execution instructions |
| execute_refinement | Step 2: Process metaprompt results and return final refined prompt |
| step1_get_categories | Step 1 of 3: Get all available strategy categories with descriptions for LLM to choose from |
| step2_get_strategies | Step 2 of 3: Get all strategies from selected category for LLM to choose the best one |
| step3_execute_strategy | Step 3 of 3: Execute the selected strategy with the user prompt |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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