feat: add prompts, resources

modelcontextprotocol/prompts/__init__.py

@@ -0,0 +1,24 @@
+"""
+Atlan MCP Server Prompts
+
+This module contains reusable prompt templates for data analysis,
+documentation, and governance workflows.
+"""
+
+from .data_analysis import (
+    data_quality_analysis_prompt,
+    lineage_documentation_prompt,
+    asset_comparison_prompt,
+)
+from .governance import (
+    compliance_assessment_prompt,
+    policy_recommendation_prompt,
+)
+
+__all__ = [
+    "data_quality_analysis_prompt",
+    "lineage_documentation_prompt", 
+    "asset_comparison_prompt",
+    "compliance_assessment_prompt",
+    "policy_recommendation_prompt",
+]

modelcontextprotocol/prompts/data_analysis.py

@@ -0,0 +1,281 @@
+"""
+Data analysis prompts for Atlan assets.
+"""
+
+from typing import Dict, Any, Optional
+from fastmcp import Context
+
+
+async def data_quality_analysis_prompt(
+    asset_type: str,
+    conditions: Optional[Dict[str, Any]] = None,
+    focus_areas: Optional[list] = None,
+    ctx: Optional[Context] = None
+) -> str:
+    """
+    Generate a comprehensive data quality analysis prompt for Atlan assets.
+
+    Args:
+        asset_type: Type of asset to analyze (Table, Column, etc.)
+        conditions: Search conditions for filtering assets
+        focus_areas: Specific quality dimensions to focus on
+        ctx: FastMCP context for logging
+    """
+
+    if ctx:
+        await ctx.info(f"Generating data quality analysis prompt for {asset_type}")
+
+    focus_list = focus_areas or [
+        "Completeness", "Accuracy", "Consistency", "Timeliness", "Validity"
+    ]
+
+    conditions_text = ""
+    if conditions:
+        conditions_text = f"""
+## Search Conditions Applied:
+```json
+{conditions}
+```
+"""
+
+    return f"""# Data Quality Analysis for {asset_type} Assets
+
+You are a data quality expert analyzing {asset_type} assets in Atlan. Please conduct a comprehensive quality assessment.
+
+{conditions_text}
+
+## Analysis Framework
+
+### Focus Areas:
+{chr(10).join(f"- **{area}**" for area in focus_list)}
+
+### Quality Dimensions:
+
+1. **Completeness Assessment**
+   - Identify missing values and null percentages
+   - Check for incomplete records or partial data
+   - Assess coverage across different data segments
+
+2. **Accuracy Validation**
+   - Verify data formats and patterns
+   - Check against business rules and constraints
+   - Identify outliers and anomalies
+
+3. **Consistency Analysis**
+   - Compare data across related assets
+   - Check for referential integrity
+   - Validate standardization across systems
+
+4. **Timeliness Evaluation**
+   - Assess data freshness and update frequencies
+   - Identify stale or outdated information
+   - Check sync delays between systems
+
+5. **Validity Verification**
+   - Ensure compliance with data standards
+   - Validate against business logic
+   - Check for proper data classifications
+
+## Expected Deliverables:
+
+### 1. Executive Summary
+- Overall quality score and trend analysis
+- Critical issues requiring immediate attention
+- Impact assessment on downstream systems
+
+### 2. Detailed Findings
+- Quality metrics by dimension
+- Specific data issues with examples
+- Root cause analysis where possible
+
+### 3. Recommendations
+- Prioritized action items with timelines
+- Process improvements and automation opportunities
+- Monitoring and alerting suggestions
+
+### 4. Implementation Plan
+- Step-by-step remediation approach
+- Resource requirements and ownership
+- Success metrics and validation criteria
+
+Please provide actionable insights that will improve data reliability and trustworthiness across the organization.
+"""
+
+
+async def lineage_documentation_prompt(
+    asset_guid: str,
+    direction: str = "BOTH",
+    include_impact: bool = True,
+    ctx: Optional[Context] = None
+) -> str:
+    """
+    Generate a prompt for creating comprehensive lineage documentation.
+
+    Args:
+        asset_guid: GUID of the asset to document
+        direction: Lineage direction (UPSTREAM, DOWNSTREAM, BOTH)
+        include_impact: Whether to include business impact analysis
+        ctx: FastMCP context for logging
+    """
+
+    if ctx:
+        await ctx.info(f"Creating lineage documentation prompt for asset {asset_guid}")
+
+    impact_section = ""
+    if include_impact:
+        impact_section = """
+### 5. Business Impact Analysis
+- Critical business processes dependent on this data
+- Stakeholder groups and their usage patterns
+- Revenue or operational impact of data issues
+- Change management considerations
+"""
+
+    return f"""# Data Lineage Documentation Request
+
+Create comprehensive documentation for the data lineage of asset: `{asset_guid}`
+
+## Documentation Scope
+- **Lineage Direction**: {direction}
+- **Include Business Impact**: {include_impact}
+- **Focus**: End-to-end data flow with business context
+
+## Required Documentation Sections:
+
+### 1. Executive Summary
+- High-level data flow overview (source → transformations → targets)
+- Key business processes supported by this data
+- Critical dependencies and potential failure points
+- Data ownership and stewardship information
+
+### 2. Source Systems Analysis
+- Origin systems and data extraction methods
+- Data collection frequency and timing
+- Source data quality and reliability
+- Access patterns and security considerations
+
+### 3. Transformation Logic
+- Detailed transformation rules and business logic
+- Data cleansing and validation steps
+- Aggregation and calculation methods
+- Error handling and data recovery processes
+
+### 4. Target Systems and Usage
+- Destination systems and consumption patterns
+- Data format requirements and constraints
+- Performance considerations and SLAs
+- Monitoring and alerting mechanisms
+{impact_section}
+### 6. Technical Specifications
+- Data schemas and field mappings
+- Integration patterns and protocols
+- Scalability and performance characteristics
+- Security and compliance requirements
+
+### 7. Operational Procedures
+- Deployment and rollback procedures
+- Monitoring and troubleshooting guides
+- Disaster recovery and business continuity
+- Maintenance and update schedules
+
+## Documentation Standards:
+- Use clear, business-friendly language
+- Include visual diagrams where helpful
+- Provide specific examples and use cases
+- Format as structured markdown
+- Include version control and change tracking
+
+Please ensure the documentation serves both technical teams and business stakeholders effectively.
+"""
+
+
+async def asset_comparison_prompt(
+    asset_guids: list,
+    comparison_criteria: Optional[list] = None,
+    ctx: Optional[Context] = None
+) -> str:
+    """
+    Generate a prompt for comparing multiple Atlan assets.
+
+    Args:
+        asset_guids: List of asset GUIDs to compare
+        comparison_criteria: Specific aspects to compare
+        ctx: FastMCP context for logging
+    """
+
+    if ctx:
+        await ctx.info(f"Creating asset comparison prompt for {len(asset_guids)} assets")
+
+    criteria_list = comparison_criteria or [
+        "Schema structure", "Data quality", "Usage patterns", 
+        "Performance metrics", "Business context"
+    ]
+
+    assets_text = "\n".join(f"- {guid}" for guid in asset_guids)
+
+    return f"""# Asset Comparison Analysis
+
+Compare the following Atlan assets across multiple dimensions to identify similarities, differences, and optimization opportunities.
+
+## Assets to Compare:
+{assets_text}
+
+## Comparison Framework:
+
+### Analysis Dimensions:
+{chr(10).join(f"- {criteria}" for criteria in criteria_list)}
+
+### 1. Structural Comparison
+- Schema differences and commonalities
+- Field types, constraints, and relationships
+- Indexing and partitioning strategies
+- Data volume and growth patterns
+
+### 2. Quality Assessment
+- Data quality scores and trends
+- Common quality issues across assets
+- Completeness and accuracy metrics
+- Validation rules and compliance status
+
+### 3. Usage Analysis
+- Access patterns and frequency
+- User communities and stakeholders
+- Query performance and optimization
+- Integration points and dependencies
+
+### 4. Business Context
+- Purpose and business value
+- Ownership and stewardship models
+- Criticality and risk assessment
+- Compliance and governance requirements
+
+### 5. Technical Performance
+- Storage utilization and costs
+- Processing times and bottlenecks
+- Scalability and resource requirements
+- Maintenance overhead and complexity
+
+## Expected Deliverables:
+
+### Comparison Matrix
+- Side-by-side feature comparison
+- Strengths and weaknesses analysis
+- Gap identification and recommendations
+
+### Consolidation Opportunities
+- Potential for asset merger or standardization
+- Redundancy elimination strategies
+- Resource optimization recommendations
+
+### Best Practices
+- Lessons learned from high-performing assets
+- Standards and patterns to replicate
+- Governance and quality improvements
+
+### Action Plan
+- Prioritized improvement initiatives
+- Resource requirements and timelines
+- Success metrics and validation criteria
+
+Focus on actionable insights that can drive standardization, improve efficiency, and reduce technical debt.
+"""