Skip to main content

CodePrism API Reference

Available Tools​

CodePrism offers 23 production-ready tools for code analysis, navigation, and workflow orchestration, plus comprehensive parser development tools. Below is a detailed reference of the available tools and their usage.

Core Navigation & Understanding (4 Tools)​

  • repository_stats: Provides a comprehensive overview and statistics of the repository.
  • explain_symbol: Offers detailed analysis of a symbol with context, accepting semantic names like "UserManager".
  • trace_path: Finds execution paths between code elements.
  • find_dependencies: Analyzes what a symbol or file depends on.

Search & Discovery (4 Tools)​

  • search_symbols: Performs advanced symbol search with regex and inheritance filtering.
  • search_content: Conducts full-text search across all repository content.
  • find_files: Discovers files using glob and regex pattern support.
  • content_stats: Provides detailed content and complexity statistics.

Analysis Tools (11 Tools)​

Production-Ready Milestone 2 Tools (4 Tools)​

  • find_unused_code: PRODUCTION-READY v2.0.0 - Graph-based unused code detection with confidence scoring and potential savings metrics.
  • analyze_security: PRODUCTION-READY v2.0.0 - Advanced vulnerability detection with CVSS scoring and OWASP mapping.
  • analyze_performance: PRODUCTION-READY v2.0.0 - Performance analysis with time complexity and memory usage detection.
  • analyze_api_surface: PRODUCTION-READY v2.0.0 - API surface analysis with versioning compliance and breaking change detection.

Core Analysis Tools (7 Tools)​

  • analyze_complexity: Measures code complexity and maintainability.
  • trace_data_flow: PRODUCTION-READY - Bidirectional data flow tracing with transformation tracking.
  • analyze_transitive_dependencies: PRODUCTION-READY - Complete dependency chain analysis with cycle detection.
  • detect_patterns: Recognizes architectural and design patterns.
  • trace_inheritance: PRODUCTION-READY - Python inheritance hierarchy analysis with metaclass and MRO support.
  • analyze_decorators: Comprehensive Python decorator usage with framework detection.
  • find_duplicates: Duplicate code pattern detection with similarity scoring.

Workflow & Orchestration (4 Tools)​

  • suggest_analysis_workflow: Provides intelligent analysis guidance for specific goals.
  • batch_analysis: Executes multiple tools in parallel with result aggregation.
  • optimize_workflow: Optimizes workflow based on usage patterns.
  • find_references: Conducts complete reference analysis across the codebase.

Parser Development Tools (codeprism-dev-tools)​

  • AST Visualizer: Pretty-print syntax trees with multiple formats (Tree, List, JSON, S-Expression, Compact).
  • Parser Validator: Comprehensive validation including span overlap detection and edge consistency checking.
  • GraphViz Exporter: Export ASTs to DOT format for visual analysis with configurable styling.
  • Performance Profiler: Real-time parsing performance metrics with bottleneck identification.
  • AST Diff Comparison: Compare parse results between parser versions with detailed change analysis.
  • Development REPL: Interactive command-line interface for parser development and testing.

Testing Coverage: 22 comprehensive unit tests covering all development tool functionality.

Example Usage​

Repository Analysis​

// Get repository overview
{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "repository_stats", "arguments": {}}}

// Analyze specific symbol  
{"jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": {"name": "explain_symbol", "arguments": {"symbol_id": "UserManager"}}}

// Search for patterns
{"jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": {"name": "search_symbols", "arguments": {"pattern": "^Agent.*", "symbol_types": ["class"]}}}

Production Analysis Tools​

// Find unused code with high confidence
{"jsonrpc": "2.0", "id": 4, "method": "tools/call", "params": {"name": "find_unused_code", "arguments": {"analyze_types": ["functions", "classes"], "confidence_threshold": 0.9}}}

// Security vulnerability analysis
{"jsonrpc": "2.0", "id": 5, "method": "tools/call", "params": {"name": "analyze_security", "arguments": {"vulnerability_types": ["injection", "xss"], "severity_threshold": "medium"}}}

// Performance bottleneck detection
{"jsonrpc": "2.0", "id": 6, "method": "tools/call", "params": {"name": "analyze_performance", "arguments": {"analysis_types": ["time_complexity", "hot_spots"], "complexity_threshold": "medium"}}}

Python-Specific Analysis​

// Trace inheritance hierarchies
{"jsonrpc": "2.0", "id": 7, "method": "tools/call", "params": {"name": "trace_inheritance", "arguments": {"class_id": "Agent", "include_mro_analysis": true}}}

// Analyze decorator usage
{"jsonrpc": "2.0", "id": 8, "method": "tools/call", "params": {"name": "analyze_decorators", "arguments": {"scope": "global", "framework_detection": true}}}

// Detect metaprogramming patterns
{"jsonrpc": "2.0", "id": 9, "method": "tools/call", "params": {"name": "detect_patterns", "arguments": {"pattern_types": ["design_patterns", "anti_patterns"]}}}

Workflow Orchestration​

// Get analysis recommendations
{"jsonrpc": "2.0", "id": 10, "method": "tools/call", "params": {"name": "suggest_analysis_workflow", "arguments": {"goal": "understand_codebase"}}}

// Run multiple tools in parallel
{"jsonrpc": "2.0", "id": 11, "method": "tools/call", "params": {"name": "batch_analysis", "arguments": {"tool_calls": [{"tool_name": "repository_stats"}, {"tool_name": "content_stats"}], "execution_strategy": "parallel"}}}

Tool Response Format​

All tools return responses in the standardized MCP format:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Tool response content in JSON format"
      }
    ],
    "isError": false
  }
}

Additional Resources​

Total Test Coverage: 425 tests across all crates with 100% tool success rate.