Add Prompt Authoring Tools Category to MCP Eval Server #896
Description
Overview
Add a new Prompt Authoring category to the MCP Eval Server with specialized tools for generating, optimizing, and authoring prompts for AI Assistants, Agents, and various AI applications.
Enhancement Scope
Current State
- 63 tools across 14 categories focused on evaluation
- Strong evaluation capabilities but no prompt generation/authoring tools
- Comprehensive assessment framework ready for extension
Proposed Addition: Prompt Authoring Category
Add 8 new tools in a 15th category specifically for prompt creation and optimization.
New Tool Category: 🎨 Prompt Authoring Tools (8 tools)
📝 Prompt Generation Tools (4 tools)
1. prompt_authoring.generate_system_prompt
Purpose: Generate system prompts for AI assistants and agents
Features:
- Role-based prompt templates (assistant, agent, specialist, analyst)
- Personality and tone configuration
- Capability definition and boundaries
- Context window optimization
- Multi-modal prompt support (text, vision, code)
Parameters:
{
"role_type": "assistant|agent|specialist|analyst|custom",
"domain": "general|technical|creative|business|academic|medical",
"personality_traits": ["helpful", "analytical", "creative", "professional"],
"capabilities": ["reasoning", "search", "code", "math", "vision"],
"constraints": ["safety_focused", "factual_only", "cite_sources"],
"interaction_style": "conversational|formal|technical|friendly",
"context_length": "short|medium|long|comprehensive",
"examples_count": 3,
"include_guidelines": true,
"custom_instructions": "Additional specific requirements..."
}2. prompt_authoring.create_task_prompt
Purpose: Generate task-specific prompts for complex operations
Features:
- Task decomposition and step-by-step instructions
- Input/output format specification
- Error handling and edge case coverage
- Quality criteria definition
- Chain-of-thought integration
Parameters:
{
"task_type": "analysis|synthesis|creative|problem_solving|research",
"complexity_level": "simple|intermediate|advanced|expert",
"input_format": "text|json|csv|code|mixed",
"output_format": "text|json|markdown|structured|custom",
"include_cot": true,
"step_by_step": true,
"include_examples": true,
"error_handling": "graceful|strict|verbose",
"quality_criteria": ["accuracy", "completeness", "clarity"],
"context_requirements": "Domain-specific context needed..."
}3. prompt_authoring.generate_few_shot_examples
Purpose: Create optimized few-shot examples for prompt enhancement
Features:
- Diverse example generation across input space
- Quality scoring and selection
- Edge case coverage
- Format consistency validation
- Difficulty progression
Parameters:
{
"task_description": "Task for which to generate examples",
"example_count": 5,
"difficulty_levels": ["easy", "medium", "hard"],
"diversity_focus": "input_variety|output_variety|edge_cases|balanced",
"format_style": "conversational|structured|technical|creative",
"include_explanations": true,
"validate_consistency": true,
"domain_context": "Specific domain requirements..."
}4. prompt_authoring.create_agent_instructions
Purpose: Generate comprehensive instructions for AI agents with tools
Features:
- Tool usage guidelines and best practices
- Decision-making frameworks
- Workflow orchestration instructions
- Error recovery procedures
- Performance optimization tips
Parameters:
{
"agent_type": "research|coding|analysis|creative|support",
"available_tools": ["search", "calculator", "code_executor", "file_manager"],
"workflow_complexity": "linear|branching|iterative|adaptive",
"decision_framework": "rule_based|heuristic|ml_guided|hybrid",
"autonomy_level": "supervised|semi_autonomous|fully_autonomous",
"error_recovery": "retry|escalate|alternative_path|user_query",
"performance_focus": "speed|accuracy|cost_efficiency|user_satisfaction",
"collaboration_mode": "solo|human_in_loop|multi_agent"
}🔧 Prompt Optimization Tools (4 tools)
5. prompt_authoring.optimize_for_model
Purpose: Optimize prompts for specific language models
Features:
- Model-specific formatting and structure
- Token efficiency optimization
- Context window utilization
- Temperature and parameter recommendations
- Bias mitigation strategies
Parameters:
{
"target_model": "gpt-4|gpt-3.5|claude|llama|custom",
"prompt_text": "Original prompt to optimize",
"optimization_goals": ["token_efficiency", "clarity", "specificity", "bias_reduction"],
"context_length_target": "short|optimal|maximum",
"preserve_intent": true,
"include_parameter_recommendations": true,
"bias_mitigation": true,
"performance_focus": "accuracy|speed|cost|balanced"
}6. prompt_authoring.enhance_clarity_and_specificity
Purpose: Improve prompt clarity and reduce ambiguity
Features:
- Ambiguity detection and resolution
- Specificity enhancement suggestions
- Language simplification options
- Structure optimization
- Readability improvement
Parameters:
{
"prompt_text": "Prompt to enhance for clarity",
"target_audience": "general|technical|domain_expert|novice",
"clarity_focus": ["remove_ambiguity", "add_specificity", "improve_structure"],
"simplification_level": "none|light|moderate|extensive",
"preserve_technical_terms": true,
"add_examples": true,
"structure_improvements": true,
"readability_target": "grade_8|grade_12|college|professional"
}7. prompt_authoring.generate_prompt_variants
Purpose: Create multiple variations of a prompt for A/B testing
Features:
- Systematic variation generation
- Style and tone alternatives
- Structure modifications
- Length variations
- Approach diversification
Parameters:
{
"base_prompt": "Original prompt to vary",
"variation_count": 5,
"variation_types": ["tone", "structure", "length", "examples", "approach"],
"tone_variations": ["formal", "casual", "technical", "friendly"],
"structure_variations": ["step_by_step", "bullet_points", "narrative", "qa_format"],
"length_variations": ["concise", "detailed", "comprehensive"],
"preserve_core_intent": true,
"include_metadata": true,
"optimization_focus": "performance|diversity|creativity"
}8. prompt_authoring.validate_prompt_quality
Purpose: Comprehensive prompt quality assessment and improvement suggestions
Features:
- Multi-dimensional quality scoring
- Improvement recommendations
- Bias detection
- Completeness analysis
- Best practices compliance
Parameters:
{
"prompt_text": "Prompt to validate and assess",
"quality_dimensions": ["clarity", "specificity", "completeness", "bias", "structure"],
"target_use_case": "assistant|agent|creative|analytical|technical",
"provide_improvements": true,
"check_bias": true,
"validate_examples": true,
"best_practices_check": true,
"generate_score": true,
"detailed_feedback": true
}Implementation Requirements
File Structure
mcp_eval_server/
├── src/
│ └── mcp_eval_server/
│ ├── tools/
│ │ └── prompt_authoring/ # New category
│ │ ├── __init__.py
│ │ ├── generation.py # Generation tools (1-4)
│ │ ├── optimization.py # Optimization tools (5-8)
│ │ ├── templates/ # Prompt templates
│ │ │ ├── system_prompts.yaml
│ │ │ ├── task_templates.yaml
│ │ │ ├── agent_instructions.yaml
│ │ │ └── examples.yaml
│ │ └── utils/ # Utilities
│ │ ├── template_engine.py
│ │ ├── quality_scorer.py
│ │ └── model_optimizer.py
│ └── server.py # Updated to include new tools
└── README.md # Updated with new category
Integration Points
- Server Registration: Add prompt authoring tools to server tool registry
- Documentation: Update README.md with new category and tools
- Templates: Create comprehensive prompt template library
- Quality Framework: Integrate with existing quality assessment tools
- Model Integration: Leverage existing judge models for optimization
Template Library Structure
# system_prompts.yaml
assistant_templates:
general_assistant:
base_prompt: "You are a helpful, harmless, and honest AI assistant..."
personality_options: ["professional", "friendly", "analytical"]
capability_modules: ["reasoning", "search", "math"]
technical_specialist:
base_prompt: "You are a technical specialist with deep expertise..."
domains: ["software_engineering", "data_science", "devops"]
agent_templates:
research_agent:
workflow_prompt: "As a research agent, follow this systematic process..."
tool_usage_guidelines: "Use tools in the following priority order..."
coding_agent:
base_instructions: "You are an expert coding agent that..."
best_practices: ["test_driven", "documentation", "security"]Quality Assessment Integration
- Cross-reference with existing tools: Use current evaluation tools to assess generated prompts
- Feedback loop: Generated prompts can be evaluated using existing quality tools
- Optimization cycle: Iterative improvement using evaluation feedback
- Validation pipeline: Automated testing of generated prompts
Usage Examples
Example 1: Generate System Prompt for Technical Assistant
# MCP Client usage
result = await mcp_client.call_tool("prompt_authoring.generate_system_prompt", {
"role_type": "specialist",
"domain": "technical",
"personality_traits": ["analytical", "precise", "helpful"],
"capabilities": ["reasoning", "code", "math"],
"constraints": ["cite_sources", "factual_only"],
"interaction_style": "technical",
"include_guidelines": True,
"examples_count": 3
})
# Result includes optimized system prompt ready for use
print(result["generated_prompt"])Example 2: Create Task-Specific Prompt
# Generate prompt for data analysis task
result = await mcp_client.call_tool("prompt_authoring.create_task_prompt", {
"task_type": "analysis",
"complexity_level": "advanced",
"input_format": "csv",
"output_format": "structured",
"include_cot": True,
"step_by_step": True,
"quality_criteria": ["accuracy", "completeness", "insights"]
})Example 3: Optimize Prompt for Specific Model
# Optimize existing prompt for GPT-4
result = await mcp_client.call_tool("prompt_authoring.optimize_for_model", {
"target_model": "gpt-4",
"prompt_text": "Write a detailed analysis of...",
"optimization_goals": ["token_efficiency", "clarity", "specificity"],
"context_length_target": "optimal",
"include_parameter_recommendations": True
})Benefits
For Developers
- Rapid Prompt Development: Generate high-quality prompts quickly
- Best Practices: Built-in prompt engineering best practices
- Model Optimization: Automatically optimize for different models
- Quality Assurance: Validate and improve prompt quality
For Researchers
- Systematic Prompt Creation: Structured approach to prompt development
- A/B Testing: Generate variations for comparative analysis
- Bias Mitigation: Built-in bias detection and mitigation
- Reproducibility: Consistent prompt generation with metadata
for Enterprise
- Standardization: Consistent prompt quality across applications
- Efficiency: Reduce time spent on manual prompt crafting
- Integration: Works with existing MCP infrastructure
- Scalability: Generate prompts at scale for multiple use cases
Testing Strategy
- Unit Tests: Individual tool functionality
- Integration Tests: Cross-tool compatibility
- Quality Tests: Generated prompt effectiveness
- Performance Tests: Generation speed and resource usage
- Validation Tests: Template quality and completeness
Documentation Updates
README.md Updates
- Tool count: Update from "63 tools" to "71 tools across 15 categories"
- New category section: Add Prompt Authoring Tools description
- Quick start examples: Include prompt authoring examples
- Tool reference table: Add all 8 new tools
Example Updated Tool Overview
🏆 MCP EVALUATION SERVER - 71 SPECIALIZED TOOLS 🏆
═══════════════════════════════════════════════════════════
📊 CORE EVALUATION SUITE (15 tools)
├── 🤖 Judge Tools (4) ────── LLM-as-a-judge evaluation
├── 📝 Prompt Tools (4) ───── Clarity, consistency, optimization
├── 🛠️ Agent Tools (4) ────── Performance, reasoning, benchmarking
└── 🔍 Quality Tools (3) ──── Factuality, coherence, toxicity
🔬 ADVANCED ASSESSMENT SUITE (39 tools)
├── 🔗 RAG Tools (8) ──────── Retrieval relevance, grounding, citations
├── ⚖️ Bias & Fairness (6) ── Demographic bias, intersectional analysis
├── 🛡️ Robustness (5) ──────── Adversarial testing, injection resistance
├── 🔒 Safety & Alignment (4) Harmful content, value alignment
├── 🌍 Multilingual (4) ────── Translation, cultural adaptation
├── ⚡ Performance (4) ──────── Latency, efficiency, scaling
└── 🔐 Privacy (8) ───────── PII detection, compliance, anonymization
🎨 PROMPT AUTHORING SUITE (8 tools) # NEW!
├── 📝 Generation Tools (4) ── System prompts, task prompts, examples, agent instructions
└── 🔧 Optimization Tools (4) Model optimization, clarity enhancement, variants, validation
🔧 SYSTEM MANAGEMENT (9 tools)
├── 🔄 Workflow Tools (3) ─── Evaluation suites, parallel execution
├── 📊 Calibration (2) ────── Judge agreement, rubric optimization
└── 🏥 Server Tools (4) ───── Health monitoring, system management
🎯 TOTAL: 71 TOOLS ACROSS 15 CATEGORIES 🎯 # UPDATED!
Acceptance Criteria
- Add 8 new prompt authoring tools as specified
- Create comprehensive template library with examples
- Integrate with existing quality assessment framework
- Update README.md with new category and tool count
- Add comprehensive documentation for each tool
- Include usage examples in multiple formats (MCP, REST, HTTP)
- Implement proper error handling and validation
- Add unit tests for all new tools (>90% coverage)
- Performance testing for prompt generation speed
- Quality validation of generated prompts using existing tools
Priority
High - Complements existing evaluation capabilities with content creation
Dependencies
- Existing MCP Eval Server infrastructure
- Template engine (Jinja2 or similar)
- Integration with current tool registration system
- Access to existing quality assessment tools for validation
Future Enhancements
- AI-Powered Optimization: Use ML models for advanced prompt optimization
- Community Templates: User-contributed prompt template sharing
- Integration Plugins: Direct integration with popular AI platforms
- Visual Prompt Builder: GUI for prompt construction and testing
- Collaborative Editing: Multi-user prompt development workflows