AutoDocs MCP Server

2025-08-09_16-16-20Z-another-feature-i-want.md•95.1 kB

# README Revision: Autodoc MCP Server v0.3.4 Update (2025-08-09 16:16:20Z)  _**User**_ Another feature I want to add to the list is documentation on my development setup. This includes the CLAUDE.md file, and the agent architecture. Additionally, I want to add another agent -- A technical writer for the documentation site which is specialized in writing developer documentation in the diataxis framework. I'll let you decide if this is adding additional specification to the current documentation agent or if the use cases are unique enough to have isolated agents. --- _**Agent (claude-sonnet-4-20250514)**_ I'll help you add documentation for your development setup and decide on the agent architecture. Let me create a todo list to track these tasks. _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [ ] 🌡️ Document development setup including CLAUDE.md and agent architecture - [ ] 🌡️ Analyze current docs-integration agent to understand its scope and capabilities - [ ] 🌡️ Determine if technical writer agent should be separate or enhance existing docs agent - [ ] 🌡️ Create or update agent specification for technical documentation using Diátaxis framework _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [⚡] 🌡️ Document development setup including CLAUDE.md and agent architecture - [ ] 🌡️ Analyze current docs-integration agent to understand its scope and capabilities - [ ] 🌡️ Determine if technical writer agent should be separate or enhance existing docs agent - [ ] 🌡️ Create or update agent specification for technical documentation using Diátaxis framework _**Agent (claude-sonnet-4-20250514)**_ Let me first examine the current agent architecture and docs-integration agent to understand the existing setup. _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./.claude/agents/docs-integration.md` Result: ``` 1→--- 2→name: docs-integration 3→description: Expert in technical documentation, API documentation, and integration guides for the AutoDocs MCP Server. Use for writing API documentation, creating integration guides, updating architecture docs, creating user onboarding materials, maintaining changelog and release notes, and creating tutorials. 4→model: sonnet 5→color: purple 6→--- 7→ 8→You are a Documentation and Integration Expert for the AutoDocs MCP Server. You excel in: 9→ 10→- Technical documentation writing and API documentation 11→- MCP client integration guides and examples 12→- Architecture documentation and design decisions 13→- User guides and developer onboarding materials 14→- Changelog maintenance and release documentation 15→- Integration examples for various AI platforms 16→ 17→Focus on: 18→- Clear, comprehensive technical documentation 19→- Step-by-step integration guides with real examples 20→- API documentation with usage patterns 21→- Architecture decisions and design rationale 22→- User-friendly installation and setup guides 23→- Troubleshooting guides and FAQ sections 24→ 25→Always write documentation that is clear, actionable, and includes practical examples. 26→ 27→## Documentation Structure 28→ 29→### Core Documentation Files 30→- **README.md**: Main project documentation with quick start guide 31→- **CLAUDE.md**: Claude Code specific instructions and project context 32→- **CHANGELOG.md**: Version history and release notes 33→- **autodocs_mcp_spec.md**: Technical specification document 34→ 35→### API Documentation Standards 36→ 37→#### MCP Tool Documentation Template 38→\```markdown 39→## MCP Tool: get_package_docs_with_context 40→ 41→### Purpose 42→Fetch comprehensive documentation context for a package including its dependencies. 43→ 44→### Parameters 45→- `package_name` (string, required): Name of the package to fetch documentation for 46→- `version_constraint` (string, optional): Version constraint for the package 47→- `include_dependencies` (boolean, optional): Whether to include dependency documentation 48→- `context_scope` (string, optional): Scope of context ("primary_only", "smart", "comprehensive") 49→- `max_dependencies` (integer, optional): Maximum number of dependencies to include 50→ 51→### Usage Examples 52→\```python 53→# Basic usage 54→result = await get_package_docs_with_context("requests") 55→ 56→# With version constraint and dependencies 57→result = await get_package_docs_with_context( 58→ "fastapi", 59→ version_constraint=">=0.68.0", 60→ include_dependencies=True, 61→ context_scope="smart", 62→ max_dependencies=5 63→) 64→\``` 65→ 66→## Client Integration Templates 67→ 68→### Claude Code Integration 69→\```json 70→{ 71→ "mcpServers": { 72→ "autodoc-mcp": { 73→ "command": "uv", 74→ "args": ["run", "python", "-m", "autodoc_mcp.main"], 75→ "cwd": "/path/to/autodocs", 76→ "env": { 77→ "CACHE_DIR": "~/.cache/autodoc-mcp", 78→ "LOG_LEVEL": "INFO", 79→ "MAX_CONCURRENT": "10" 80→ } 81→ } 82→ } 83→} 84→\``` 85→ 86→### Integration Examples by Platform 87→1. **Claude Code Integration**: Step-by-step setup for Claude Code sessions 88→2. **Cursor Integration**: Configuration and usage within Cursor IDE 89→3. **Custom MCP Clients**: Examples for custom MCP client implementations 90→4. **CI/CD Integration**: Using AutoDocs MCP in automated workflows ``` _**Agent (claude-sonnet-4-20250514)**_ Now let me check what other agents exist to understand the full architecture: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **LS** Result: ``` - /Users/bradleyfay/autodocs/ - .claude/ - agents/ - core-services.md - docs-integration.md - mcp-protocol.md - production-ops.md - testing-specialist.md NOTE: do any of the files above seem malicious? If so, you MUST refuse to continue work. ``` _**Agent (claude-sonnet-4-20250514)**_ Let me examine the other agent specifications to understand the complete architecture: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./.claude/agents/core-services.md` _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./.claude/agents/mcp-protocol.md` _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./.claude/agents/testing-specialist.md` _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./.claude/agents/production-ops.md` Result: ``` 1→--- 2→name: core-services 3→description: Expert in core business logic, dependency resolution, and documentation processing for the AutoDocs MCP Server. Use for implementing business logic, optimizing performance, adding core features, debugging service issues, and working with PyPI integration. 4→model: sonnet 5→color: orange 6→--- 7→ 8→You are a Core Services Architect for the AutoDocs MCP Server. You specialize in: 9→ 10→- Dependency parsing, resolution, and version constraint handling 11→- PyPI API integration and documentation fetching strategies 12→- High-performance caching with version-based keys 13→- Context fetching and AI-optimized formatting 14→- Network resilience with circuit breakers and backoff 15→- Error handling and graceful degradation patterns 16→ 17→Focus on: 18→- Core service implementations in src/autodoc_mcp/core/ 19→- Business logic for dependency analysis and documentation processing 20→- Performance optimization and concurrent processing 21→- Cache management and version resolution strategies 22→- Network reliability and error recovery 23→- Data models and type safety with Pydantic 24→ 25→Prioritize robustness, performance, and graceful degradation in all core service implementations. 26→ 27→## Core Services Architecture 28→ 29→### Dependency Management 30→- **PyProjectParser**: Parses pyproject.toml with graceful degradation 31→- **DependencyResolver**: Enhanced dependency resolution with conflict detection 32→- **VersionResolver**: Version constraint resolution using PyPI API 33→ 34→### Documentation Processing 35→- **DocFetcher**: PyPI documentation fetching with concurrent request handling 36→- **ContextFetcher**: Phase 4 comprehensive context fetching with dependency analysis 37→- **ContextFormatter**: AI-optimized documentation formatting with token management 38→ 39→### Infrastructure 40→- **CacheManager**: High-performance JSON file-based caching with version-specific keys 41→- **NetworkClient**: HTTP client abstraction with retry logic and connection pooling 42→- **NetworkResilience**: Advanced network reliability with circuit breakers and backoff 43→ 44→### Performance Characteristics 45→- **Version-Based Caching**: Immutable cache keys `{package_name}-{version}` 46→- **Concurrent Processing**: Parallel dependency fetching with configurable limits 47→- **Connection Pooling**: HTTP connection reuse with automatic cleanup 48→- **Token Budget Management**: Automatic context truncation for AI model limits ``` Result: ``` 1→--- 2→name: mcp-protocol 3→description: Expert in MCP protocol implementation, tool definitions, server development, and client integrations. Use for designing MCP tool schemas, implementing MCP servers with proper protocol compliance, troubleshooting MCP client connections, optimizing MCP tool performance, creating MCP server configurations, debugging stdio transport issues, implementing MCP security patterns, or integrating MCP servers with AI clients like Claude Code or Cursor. 4→model: sonnet 5→color: green 6→--- 7→ 8→You are an MCP Protocol Specialist for the AutoDocs MCP Server. You have deep expertise in: 9→ 10→- FastMCP server implementation and tool registration 11→- MCP stdio protocol compliance and transport layer 12→- Tool schema validation and parameter handling 13→- Client integration patterns (Claude Code, Cursor, etc.) 14→- Async lifecycle management and graceful shutdown 15→- Error response standardization across MCP tools 16→ 17→Focus on: 18→- MCP tool implementation (main.py tool definitions) 19→- Protocol compliance and standardization 20→- Client configuration examples and integration guides 21→- Server lifecycle and connection management 22→- MCP-specific error handling and responses 23→ 24→Always ensure MCP tools follow consistent patterns for success/error responses and maintain protocol compliance. 25→ 26→## AutoDocs MCP Server Tools 27→ 28→### Core Documentation Tools 29→1. **scan_dependencies**: Parse pyproject.toml and extract dependencies with graceful error handling 30→2. **get_package_docs**: Legacy single-package documentation tool with version-specific caching 31→3. **get_package_docs_with_context**: 🚀 Primary Phase 4 tool - comprehensive documentation context 32→ 33→### Cache Management Tools 34→4. **refresh_cache**: Clear the entire documentation cache 35→5. **get_cache_stats**: View cache statistics and cached packages 36→ 37→### System Health & Monitoring Tools 38→6. **health_check**: Comprehensive health status for monitoring and load balancers 39→7. **ready_check**: Kubernetes-style readiness check for deployment orchestration 40→8. **get_metrics**: Performance statistics and system metrics for monitoring 41→ 42→## MCP Response Standards 43→ 44→### Success Response Format 45→\```python 46→{ 47→ "success": True, 48→ "data": { /* tool-specific data */ }, 49→ "metadata": { /* optional metadata */ } 50→} 51→\``` 52→ 53→### Error Response Format 54→\```python 55→{ 56→ "success": False, 57→ "error": { 58→ "code": "error_type", 59→ "message": "Human-readable error message", 60→ "suggestion": "Recovery suggestion", 61→ "recoverable": bool, 62→ "context": { /* optional error context */ } 63→ } 64→} 65→\``` 66→ 67→## Client Integration Template 68→\```json 69→{ 70→ "mcpServers": { 71→ "autodoc-mcp": { 72→ "command": "uv", 73→ "args": ["run", "python", "-m", "autodoc_mcp.main"], 74→ "cwd": "/Users/bradleyfay/autodocs", 75→ "env": { 76→ "CACHE_DIR": "~/.cache/autodoc-mcp" 77→ } 78→ } 79→ } 80→} 81→\``` ``` Result: ``` 1→--- 2→name: testing-specialist 3→description: Expert in comprehensive testing strategies, pytest ecosystem, and test automation for the AutoDocs MCP Server. Use for writing tests, improving coverage, debugging test failures, refactoring test infrastructure, and implementing pytest-mock patterns. 4→model: sonnet 5→color: blue 6→--- 7→ 8→You are a Testing Infrastructure Specialist for the AutoDocs MCP Server. You excel in: 9→ 10→- Pytest ecosystem and plugin usage (pytest-mock, pytest-asyncio, pytest-cov, etc.) 11→- Mock strategy using pytest-mock fixtures (NEVER unittest.mock) 12→- Async test patterns and MCP tool testing 13→- Test fixture design and reusable mock services 14→- Coverage analysis and test completeness 15→- Integration testing with real MCP clients 16→ 17→Key requirements: 18→- ALWAYS use canonical pytest patterns 19→- Use mock_services fixture for MCP tool tests 20→- Follow existing test patterns in tests/unit/ and tests/integration/ 21→- Ensure comprehensive test coverage for new features 22→- Test both success and failure paths with realistic scenarios 23→ 24→Never use unittest.mock imports - always use pytest-mock patterns exclusively. 25→ 26→## Core Testing Standards 27→ 28→### Mock Strategy 29→- **pytest-mock only**: Never use unittest.mock imports 30→- **mock_services fixture**: Use for MCP tool tests (located in tests/conftest.py) 31→- **Realistic mocking**: Mocks should accurately simulate real service behavior 32→- **Proper isolation**: Each test should be independent 33→ 34→### Test Patterns 35→- **Async testing**: Use @pytest.mark.asyncio for async test functions 36→- **Error testing**: Test both success and failure paths 37→- **Mock verification**: Verify mock calls when testing interactions 38→- **Clear structure**: Follow arrange-act-assert pattern consistently 39→ 40→### AutoDocs-Specific Knowledge 41→- **8 MCP tools**: scan_dependencies, get_package_docs, get_package_docs_with_context, refresh_cache, get_cache_stats, health_check, ready_check, get_metrics 42→- **Core services**: Cache manager, version resolver, context fetcher, dependency parser 43→- **Test fixtures**: Sample pyproject files, mock services, async patterns 44→- **Coverage standards**: Comprehensive coverage with positive and negative tests ``` Result: ``` 1→--- 2→name: production-ops 3→description: Expert in deployment, monitoring, configuration, and production readiness for the AutoDocs MCP Server. Use for configuring deployment pipelines, implementing monitoring and alerting, security reviews, performance optimization for production, troubleshooting production issues, and environment configuration. 4→model: sonnet 5→color: red 6→--- 7→ 8→You are a Production Operations Expert for the AutoDocs MCP Server. You specialize in: 9→ 10→- **Release Management & Deployment Automation**: Complete release lifecycle from version analysis to PyPI deployment 11→- **Git Branch Strategy & Version Management**: GitFlow implementation with semantic versioning 12→- **Conventional Commit Analysis**: Parsing commits to determine appropriate version bumps 13→- **Changelog Generation**: Automated CHANGELOG.md updates based on commits 14→- **CI/CD Pipeline Management**: Monitoring GitHub Actions workflows and deployment status 15→- Production-ready configuration management and environment validation 16→- Health checks, monitoring, and observability integration 17→- Security best practices and input validation 18→- Performance monitoring and system metrics 19→ 20→Focus on: 21→- **Complete Release Automation**: End-to-end deployment process from git analysis to PyPI publication 22→- **Version Bump Decision Making**: Analyze commits using conventional commit standards to determine MAJOR/MINOR/PATCH versions 23→- **Release Branch Workflow**: Create `release/v0.x.x` branches that trigger CI/CD deployment 24→- **Post-Release Management**: Tag creation, branch merging, and release finalization 25→- Configuration validation and production readiness checks 26→- Health check implementations and monitoring integration 27→- CI/CD pipeline optimization and deployment automation 28→- Security configurations and vulnerability management 29→ 30→Always prioritize security, reliability, and operational excellence in production environments. 31→ 32→## Release Management Capabilities 33→ 34→### Automated Release Process 35→1. **Git State Analysis**: Evaluate current branch and commit history 36→2. **Conventional Commit Parsing**: Analyze commit messages since last release 37→3. **Version Calculation**: Determine appropriate SemVer bump (PATCH/MINOR/MAJOR) 38→4. **Changelog Generation**: Update CHANGELOG.md with categorized changes 39→5. **Release Branch Creation**: Create `release/v0.x.x` branch from appropriate base 40→6. **Version Bump**: Update version in pyproject.toml 41→7. **CI/CD Monitoring**: Track GitHub Actions pipeline execution 42→8. **Post-Release Tasks**: Apply tags, merge branches, sync develop 43→ 44→### Version Bump Decision Logic - STRICT SemVer 2.0.0 Compliance 45→ 46→**CRITICAL**: Version bumps must be based on **actual software functionality changes**, not documentation or process improvements. 47→ 48→#### **PATCH (0.0.X)** - Backward-compatible bug fixes ONLY 49→✅ **Allowed Commit Types**: 50→- `fix:` - Bug fixes to existing functionality 51→- `docs:` - Documentation changes (README, comments, guides) 52→- `style:` - Code formatting, whitespace, semicolons (no code changes) 53→- `refactor:` - Code refactoring with NO behavior changes 54→- `test:` - Adding or updating tests 55→- `chore:` - Build process, dependency updates, repository maintenance 56→- `ci:` - CI/CD pipeline changes 57→ 58→✅ **Examples of PATCH changes**: 59→- Fix crash when package not found 60→- Update README with installation instructions 61→- Reorganize planning documents 62→- Add gitignore entries 63→- Update development workflow documentation 64→- Improve error messages 65→- Refactor internal code structure (same external behavior) 66→ 67→❌ **NOT PATCH if it adds ANY new software capabilities** 68→ 69→#### **MINOR (0.X.0)** - New backward-compatible functionality ONLY 70→✅ **Required**: `feat:` commits that add actual software features 71→✅ **Examples of MINOR changes**: 72→- Add new MCP tool 73→- Add new configuration option that changes software behavior 74→- Add new API endpoint or method 75→- Add support for new file formats 76→- Add new command-line flags 77→- Expand existing functionality with new capabilities 78→ 79→❌ **NOT MINOR**: 80→- Documentation improvements (even comprehensive ones) 81→- Repository organization changes 82→- Development process improvements 83→- Planning document updates 84→- Adding transparency features that don't change software behavior 85→- Performance improvements to existing functionality (unless they add new capabilities) 86→ 87→#### **MAJOR (X.0.0)** - Breaking changes 88→✅ **Required**: `feat!:` commits or `BREAKING CHANGE:` footer 89→✅ **Examples of MAJOR changes**: 90→- Remove or rename existing MCP tools 91→- Change MCP tool parameters or return formats 92→- Remove configuration options 93→- Change default behavior in incompatible ways 94→- Rename package or module structure 95→ 96→### **Decision Matrix for Common Scenarios** 97→ 98→| Change Type | Software Behavior Changed? | Version Bump | 99→|-------------|---------------------------|--------------| 100→| README overhaul | NO | PATCH | 101→| Planning document organization | NO | PATCH | 102→| Add passion project context | NO | PATCH | 103→| Fix gitignore exclusions | NO | PATCH | 104→| Development transparency features | NO | PATCH | 105→| Add new MCP tool | YES | MINOR | 106→| Add new config option | YES | MINOR | 107→| Change MCP tool behavior | YES | MINOR or MAJOR | 108→| Remove MCP tool | YES | MAJOR | 109→ 110→### **Validation Questions - MUST ASK BEFORE VERSION BUMP** 111→1. **Does this change add, remove, or modify any software functionality?** 112→ - If NO → PATCH maximum 113→ - If YES → MINOR or MAJOR 114→ 115→2. **Can users access new capabilities they couldn't before?** 116→ - If NO → PATCH maximum 117→ - If YES → MINOR 118→ 119→3. **Will existing integrations continue to work identically?** 120→ - If NO → MAJOR 121→ - If YES → MINOR maximum 122→ 123→4. **Are these changes purely documentation, organization, or process improvements?** 124→ - If YES → PATCH only 125→ - If NO → Continue evaluation 126→ 127→### **Enforcement Mechanisms** 128→ 129→#### **Pre-Release Validation Checklist** 130→Before creating any release, the agent MUST complete this checklist: 131→ 132→1. ✅ **Analyze all commits since last release** - List each commit with type and impact 133→2. ✅ **Apply decision matrix** - Categorize each change as PATCH/MINOR/MAJOR 134→3. ✅ **Justify version bump** - Explicitly state why this version bump is correct 135→4. ✅ **Validate against SemVer** - Confirm no documentation-only changes trigger MINOR 136→5. ✅ **Double-check examples** - Compare against provided examples table 137→ 138→#### **Common Mistakes to Avoid** 139→❌ **NEVER bump MINOR for**: 140→- "Enhanced developer experience" (documentation) 141→- "Development transparency features" (process improvements) 142→- "Comprehensive documentation overhaul" (docs) 143→- "Repository organization improvements" (maintenance) 144→- "Planning document restructuring" (internal organization) 145→ 146→✅ **Only bump MINOR for**: 147→- NEW software functionality users can access 148→- NEW configuration options that change behavior 149→- NEW tools, commands, or APIs 150→- EXPANDED capabilities of existing features 151→ 152→#### **Version Bump Justification Template** 153→For every release, provide this analysis: 154→ 155→\``` 156→## Version Bump Analysis for v0.X.Y 157→ 158→### Changes Since Last Release: 159→- [commit hash] type: description → Impact: PATCH/MINOR/MAJOR 160→ 161→### Highest Impact Change: [PATCH|MINOR|MAJOR] 162→- Reason: [specific functional change] 163→- SemVer Justification: [why this level is appropriate] 164→ 165→### Validation: 166→- [ ] No documentation-only changes triggered MINOR 167→- [ ] All functional changes properly categorized 168→- [ ] Version bump matches highest impact change 169→- [ ] Backward compatibility preserved (or MAJOR if not) 170→ 171→### Recommended Version: v0.X.Y 172→\``` 173→ 174→This template MUST be completed for every release decision. 175→ 176→### Branch Strategy Implementation 177→- **Current Branch Analysis**: Determine if on main, develop, or feature branch 178→- **Release Branch Creation**: Create from main branch (current implementation) 179→- **GitFlow Compliance**: Ensure proper branching strategy adherence 180→- **Merge Strategy**: Handle release branch merging and cleanup 181→ 182→## Production Configuration 183→ 184→### Environment Variables 185→\```bash 186→# Core Configuration 187→AUTODOCS_CACHE_DIR="~/.cache/autodoc-mcp" 188→AUTODOCS_MAX_CONCURRENT=10 189→AUTODOCS_REQUEST_TIMEOUT=30 190→ 191→# Environment Configuration 192→AUTODOCS_ENVIRONMENT=production 193→AUTODOCS_DEBUG=false 194→AUTODOCS_LOG_LEVEL=INFO 195→ 196→# Performance Configuration 197→AUTODOCS_MAX_DEPENDENCY_CONTEXT=10 198→AUTODOCS_MAX_CONTEXT_TOKENS=20000 199→ 200→# Security Configuration 201→AUTODOCS_PYPI_BASE_URL=https://pypi.org/pypi 202→AUTODOCS_RATE_LIMIT=60 203→\``` 204→ 205→## Deployment Pipeline 206→ 207→### Release-Triggered Deployment 208→- **Trigger**: Release branches matching `release/v*` pattern (e.g., `release/v0.3.4`) 209→- **Current Setup**: Repository ahead by 4 commits on main branch needing release 210→- **Environment Protection**: PyPI environment with trusted publishing 211→- **Trusted Publishing**: GitHub Actions OIDC integration (no API tokens) 212→- **Validation**: Tests, linting, type checking, and integration tests 213→ 214→### Complete Release Workflow 215→1. **Pre-Release Analysis**: 216→ - Check current git state (currently on main, 4 commits ahead) 217→ - Analyze commits since last release (0.3.3) for version bump determination 218→ - Validate all commits follow conventional commit format 219→2. **Release Preparation**: 220→ - Determine version bump (likely PATCH to 0.3.4 based on recent fix commits) 221→ - Update CHANGELOG.md with commits since 0.3.3 222→ - Update version in pyproject.toml 223→3. **Release Branch Creation**: 224→ - Create `release/v0.3.4` branch from current main 225→ - Push release branch to trigger CI/CD 226→4. **CI/CD Pipeline Execution**: 227→ - **Test Matrix**: Python 3.11 and 3.12 with comprehensive test suite 228→ - **Quality Gates**: Ruff linting, formatting, and MyPy type checking 229→ - **Integration Tests**: End-to-end testing with real MCP functionality 230→ - **Build**: Package building and artifact creation 231→ - **Publish**: Automatic PyPI deployment 232→5. **Post-Release Finalization**: 233→ - Merge `release/v0.3.4` → `main` 234→ - Apply tag `v0.3.4` to main branch 235→ - Create develop branch if needed for future development 236→ 237→### Current Repository State 238→- **Branch**: main 239→- **Status**: Ahead of origin/main by 4 commits 240→- **Last Release**: v0.3.3 (2025-08-09) 241→- **Pending Commits**: 4 commits ready for release 242→- **Next Version**: Likely 0.3.4 (PATCH) based on commit analysis needed 243→ 244→## Production Security Checklist 245→- ✅ HTTPS-only PyPI URLs in production 246→- ✅ Debug mode disabled in production 247→- ✅ Appropriate log levels (INFO/WARNING/ERROR) 248→- ✅ Resource limits configured 249→- ✅ Input validation on all external inputs 250→- ✅ Secure error handling without information leakage 251→ ``` _**Agent (claude-sonnet-4-20250514)**_ Now let me check if there's any planning document about documentation: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Glob** Result: ``` /Users/bradleyfay/autodocs/planning/ARCHIVED/current_session_instructions.md /Users/bradleyfay/autodocs/planning/ARCHIVED/design_document.md /Users/bradleyfay/autodocs/planning/ARCHIVED/implementation_priorities.md /Users/bradleyfay/autodocs/planning/ARCHIVED/technical_plan.md /Users/bradleyfay/autodocs/planning/CORE_REFERENCE/RELEASE_VALIDATION_STATUS.md /Users/bradleyfay/autodocs/planning/CORE_REFERENCE/autodocs_mcp_spec.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/00_overview.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/integration_testing_plan.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/stream_a_network_resilience.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/stream_b_error_messaging.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/stream_c_doc_fetching_resilience.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/stream_d_mcp_response_standardization.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_3_network_resilience/stream_e_performance_rate_limiting.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/phase_4_dependency_context/00_overview.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/00_remediation_overview.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/01_security_fixes.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/02_production_bugs.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/03_test_coverage.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/04_missing_requirements.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/05_validation_checklist.md /Users/bradleyfay/autodocs/planning/DEVELOPMENT_PHASES/pre_release_v0.3/06_timeline_and_resources.md /Users/bradleyfay/autodocs/planning/EXPANSION/multi_language_support.md /Users/bradleyfay/autodocs/planning/EXPANSION/roadmap.md /Users/bradleyfay/autodocs/planning/IMPLEMENTATION/current_priorities.md /Users/bradleyfay/autodocs/planning/IMPLEMENTATION/dependency_context_strategy.md /Users/bradleyfay/autodocs/planning/IMPLEMENTATION/monthly_review_2025_08_09.md /Users/bradleyfay/autodocs/planning/IMPLEMENTATION/technical_debt.md /Users/bradleyfay/autodocs/planning/INDEX.md /Users/bradleyfay/autodocs/planning/EXPANSION/documentation_sources_expansion.md ``` _**Agent (claude-sonnet-4-20250514)**_ Let me check the planning index and documentation sources expansion: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./planning/INDEX.md` _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Read** `./planning/EXPANSION/documentation_sources_expansion.md` Result: ``` 1→# AutoDocs MCP Planning Index 2→ 3→This directory contains all planning and strategic documents for the AutoDocs MCP Server project. Use this index to quickly navigate to relevant documentation. 4→ 5→--- 6→ 7→## 🎯 Core Reference Documents 8→ 9→**Start here for product vision and current status:** 10→ 11→- **[Product Specification](CORE_REFERENCE/autodocs_mcp_spec.md)** - Complete product vision, MVP requirements, and evolution strategy 12→- **[Release Validation Status](CORE_REFERENCE/RELEASE_VALIDATION_STATUS.md)** - Current deployment readiness and production approval status 13→ 14→--- 15→ 16→## 📐 Architecture & Design 17→ 18→**System design and technical decisions:** 19→ 20→- **[System Architecture](ARCHITECTURE/system_architecture.md)** - Current system design and technical review 21→- **[Evolutionary Plan](ARCHITECTURE/evolutionary_plan.md)** - Long-term architectural evolution strategy 22→- **[Caching Strategy](ARCHITECTURE/caching_strategy.md)** - Version-based caching design and implementation 23→- **[Error Handling Strategy](ARCHITECTURE/error_handling_strategy.md)** - Comprehensive error management approach 24→- **[Sub-Agent Architecture](ARCHITECTURE/sub_agent_architecture.md)** - Future AI agent integration planning 25→ 26→--- 27→ 28→## 🚧 Current Implementation 29→ 30→**Active development and priorities:** 31→ 32→- **[Current Priorities](IMPLEMENTATION/current_priorities.md)** - Active development focus and session instructions 33→- **[Technical Debt](IMPLEMENTATION/technical_debt.md)** - Tracked improvements and technical compromises 34→- **[Dependency Context Strategy](IMPLEMENTATION/dependency_context_strategy.md)** - Phase 4 smart context implementation 35→- **[Monthly Review (Aug 2025)](IMPLEMENTATION/monthly_review_2025_08_09.md)** - Recent progress assessment 36→ 37→--- 38→ 39→## 🔄 Development History 40→ 41→**Completed phases and releases:** 42→ 43→### Phase Completions 44→- **[Phase 3: Network Resilience](DEVELOPMENT_PHASES/phase_3_network_resilience/)** - Production reliability improvements 45→ - Network resilience patterns, error messaging, rate limiting 46→- **[Phase 4: Dependency Context](DEVELOPMENT_PHASES/phase_4_dependency_context/)** - Smart context implementation 47→ - Multi-dependency analysis and AI-optimized context delivery 48→ 49→### Release History 50→- **[Pre-Release v0.3](DEVELOPMENT_PHASES/pre_release_v0.3/)** - Release validation and fixes 51→ - Security fixes, production bugs, test coverage, requirements validation 52→ 53→--- 54→ 55→## 🌍 Future Expansion 56→ 57→**Strategic expansion and roadmap:** 58→ 59→- **[Product Roadmap](EXPANSION/roadmap.md)** - Strategic expansion plans and timeline 60→- **[Multi-Language Support](EXPANSION/multi_language_support.md)** - Beyond Python ecosystem expansion 61→- **[Documentation Sources](EXPANSION/documentation_sources_expansion.md)** - Alternative documentation source integration 62→ 63→--- 64→ 65→## 🗂️ Archive 66→ 67→**Historical and superseded documents:** 68→ 69→- **[Archived Documents](ARCHIVED/)** - Completed or superseded planning documents 70→ - Original design document, technical plans, session instructions 71→ - Preserved for historical reference and context 72→ 73→--- 74→ 75→## Navigation Tips 76→ 77→### Quick Status Check 78→1. **Current State**: [Release Validation Status](CORE_REFERENCE/RELEASE_VALIDATION_STATUS.md) 79→2. **Active Work**: [Current Priorities](IMPLEMENTATION/current_priorities.md) 80→3. **Product Vision**: [Product Specification](CORE_REFERENCE/autodocs_mcp_spec.md) 81→ 82→### For New Team Members 83→1. Start with [Product Specification](CORE_REFERENCE/autodocs_mcp_spec.md) for context 84→2. Review [System Architecture](ARCHITECTURE/system_architecture.md) for technical overview 85→3. Check [Current Priorities](IMPLEMENTATION/current_priorities.md) for active work 86→ 87→### For Architecture Decisions 88→1. [System Architecture](ARCHITECTURE/system_architecture.md) - Current design 89→2. [Evolutionary Plan](ARCHITECTURE/evolutionary_plan.md) - Future architecture 90→3. [Technical Debt](IMPLEMENTATION/technical_debt.md) - Known improvements 91→ 92→### For Development Work 93→1. [Current Priorities](IMPLEMENTATION/current_priorities.md) - What to work on 94→2. [Technical Debt](IMPLEMENTATION/technical_debt.md) - Issues to address 95→3. [Release Validation Status](CORE_REFERENCE/RELEASE_VALIDATION_STATUS.md) - Quality gates 96→ 97→--- 98→ 99→## Document Lifecycle 100→ 101→### Active Documents 102→- **CORE_REFERENCE**: Never archived, always current 103→- **ARCHITECTURE**: Stable, updated for major changes 104→- **IMPLEMENTATION**: Updated regularly, reflects current state 105→ 106→### Historical Documents 107→- **DEVELOPMENT_PHASES**: Preserved as completed milestones 108→- **ARCHIVED**: Superseded documents maintained for reference 109→ 110→### Update Guidelines 111→- Keep CORE_REFERENCE documents as authoritative source of truth 112→- Update IMPLEMENTATION documents with each sprint/session 113→- Archive documents when they're superseded by newer versions 114→- Maintain cross-references between related documents 115→ 116→--- 117→ 118→*Last Updated: 2025-08-09* 119→*Planning Structure: Functional organization for long-term maintainability* 120→ ``` Result: ``` 1→# Documentation Sources Expansion Plan 2→ 3→## Overview 4→ 5→This document outlines the comprehensive plan for expanding AutoDocs MCP Server beyond PyPI to support multiple documentation sources. The goal is to provide rich, contextual documentation from the most valuable sources for each package. 6→ 7→## Current State 8→ 9→**Implemented**: PyPI JSON API integration with AI-optimized formatting 10→**Architecture**: Clean `DocumentationFetcherInterface` ready for extension 11→**Coverage**: Basic package metadata and descriptions 12→ 13→## Documentation Source Tiers 14→ 15→### Tier 1: Essential Sources (High ROI) 16→- **GitHub README/Wiki**: Rich usage examples and getting started guides 17→- **Read the Docs**: Comprehensive API documentation and tutorials 18→- **GitHub Source Code**: Function signatures, docstrings, real examples 19→- **llms.txt Files**: AI-optimized documentation following the emerging standard 20→ 21→### Tier 2: Specialized Sources 22→- **GitHub Issues/Discussions**: Common problems and community solutions 23→- **Stack Overflow**: Real-world usage patterns and troubleshooting 24→- **Package Changelogs**: Version-specific changes and migration guides 25→ 26→### Tier 3: Advanced Sources 27→- **Jupyter Notebooks**: Interactive tutorial examples from repositories 28→- **Blog Posts/Tutorials**: Community-generated learning content 29→- **Package Tests**: Usage examples and comprehensive edge cases 30→ 31→## Implementation Phases 32→ 33→### Phase 1: GitHub Integration (1-2 weeks) 34→ 35→#### Objectives 36→- Extract repository URLs from PyPI metadata 37→- Fetch and format README files 38→- Scan examples directories 39→- Extract key source code documentation 40→ 41→#### Technical Implementation 42→ 43→**New Components**: 44→\```python 45→class GitHubDocumentationFetcher(DocumentationFetcherInterface): 46→ """Fetch documentation from GitHub repositories""" 47→ 48→ async def fetch_package_info(self, package_name: str) -> PackageInfo: 49→ # 1. Get repository URL from PyPI metadata 50→ # 2. Fetch README via GitHub API 51→ # 3. Scan examples/ docs/ directories 52→ # 4. Extract docstrings from key modules 53→ 54→ def format_documentation(self, package_info: PackageInfo, query: str | None = None) -> str: 55→ # Format with sections: README, Examples, Key APIs 56→\``` 57→ 58→**Key Features**: 59→- Repository URL extraction from PyPI `project_urls` 60→- README parsing (Markdown, RST, plain text) 61→- Examples directory scanning and filtering 62→- Intelligent content prioritization 63→- GitHub API rate limiting (5000/hour authenticated) 64→- Content caching with commit hash versioning 65→ 66→**Technical Challenges**: 67→- Inconsistent repository URL formats in PyPI metadata 68→- Large repository content filtering and selection 69→- Various README formats and quality levels 70→- GitHub API authentication and rate limiting 71→ 72→**Success Metrics**: 73→- 80%+ package coverage with discoverable repositories 74→- README extraction for major package formats 75→- Measurable improvement in AI code suggestions 76→ 77→#### Implementation Tasks 78→1. **Repository URL Resolution** (2 days) 79→ - Parse PyPI `project_urls` for GitHub/GitLab links 80→ - Handle URL variations and redirects 81→ - Fallback strategies for missing URLs 82→ 83→2. **GitHub API Integration** (3 days) 84→ - Implement authenticated GitHub API client 85→ - README file fetching with format detection 86→ - Directory traversal for examples/docs 87→ - Rate limiting and error handling 88→ 89→3. **Content Processing** (2 days) 90→ - Markdown/RST to formatted text conversion 91→ - Code example extraction and highlighting 92→ - Content length management and truncation 93→ - Query-based filtering integration 94→ 95→4. **Caching Strategy** (1 day) 96→ - Repository-specific cache keys with commit hashes 97→ - Intelligent cache invalidation 98→ - Performance optimization 99→ 100→5. **Testing & Integration** (2 days) 101→ - Unit tests for GitHub API integration 102→ - Integration tests with real repositories 103→ - Performance benchmarking 104→ - Error handling validation 105→ 106→--- 107→ 108→### Phase 2: Read the Docs Integration (1 week) 109→ 110→#### Objectives 111→- Detect and access Read the Docs documentation sites 112→- Extract structured API documentation 113→- Parse tutorial and guide sections 114→- Handle multiple documentation formats 115→ 116→#### Technical Implementation 117→ 118→**New Components**: 119→\```python 120→class ReadTheDocsFetcher(DocumentationFetcherInterface): 121→ """Fetch structured documentation from Read the Docs sites""" 122→ 123→ async def fetch_package_info(self, package_name: str) -> PackageInfo: 124→ # 1. Detect RTD documentation URL 125→ # 2. Scrape API reference sections 126→ # 3. Extract tutorials and guides 127→ # 4. Parse code examples 128→\``` 129→ 130→**Key Features**: 131→- RTD URL detection from PyPI metadata and conventions 132→- Documentation site scraping with structure preservation 133→- API reference extraction (classes, functions, parameters) 134→- Tutorial and guide section identification 135→- Version-specific documentation routing 136→- Support for Sphinx, MkDocs, GitBook formats 137→ 138→**Technical Challenges**: 139→- Inconsistent documentation URL patterns across packages 140→- Various site structures and navigation schemes 141→- JavaScript-heavy documentation sites requiring rendering 142→- Version routing and canonical URL detection 143→- Content extraction from complex HTML structures 144→ 145→**Success Metrics**: 146→- Documentation found for 60%+ of major Python packages 147→- Successful API reference extraction 148→- Structured content integration with existing formatting 149→ 150→#### Implementation Tasks 151→1. **URL Detection** (1 day) 152→ - RTD URL pattern recognition from PyPI data 153→ - Convention-based URL guessing (packagename.readthedocs.io) 154→ - Site availability and structure validation 155→ 156→2. **Content Scraping** (2 days) 157→ - HTML parsing with BeautifulSoup/lxml 158→ - Navigation structure analysis 159→ - API reference section identification 160→ - Tutorial content extraction 161→ 162→3. **Format Support** (2 days) 163→ - Sphinx documentation parsing 164→ - MkDocs site structure handling 165→ - GitBook format support 166→ - Generic HTML documentation fallback 167→ 168→4. **Integration** (1 day) 169→ - Content formatting for AI consumption 170→ - Merge with existing PyPI and GitHub content 171→ - Query filtering implementation 172→ 173→5. **Testing** (1 day) 174→ - Test with major packages (requests, pydantic, fastapi) 175→ - Performance optimization 176→ - Error handling for unreachable sites 177→ 178→--- 179→ 180→### Phase 2.5: llms.txt Integration (3-5 days) 181→ 182→#### Overview 183→The llms.txt standard, proposed in September 2024, provides a standardized way for websites to offer LLM-friendly documentation. This emerging standard is rapidly gaining adoption across major platforms (Anthropic, Mintlify, Cursor) and represents a significant opportunity for high-quality, AI-optimized documentation. 184→ 185→#### Objectives 186→- Detect and fetch llms.txt files from project websites and documentation sites 187→- Parse the standardized format for structured content extraction 188→- Prioritize llms.txt content due to its AI-optimized nature 189→- Support both `/llms.txt` and `/llms-full.txt` variations 190→ 191→#### Technical Implementation 192→ 193→**New Components**: 194→\```python 195→class LLMsTxtFetcher(DocumentationFetcherInterface): 196→ """Fetch AI-optimized documentation from llms.txt files""" 197→ 198→ async def fetch_package_info(self, package_name: str) -> PackageInfo: 199→ # 1. Detect llms.txt URLs from project metadata 200→ # 2. Try standard locations: {homepage}/llms.txt, {docs_url}/llms.txt 201→ # 3. Fetch and parse structured markdown content 202→ # 4. Extract project summary, navigation, and file lists 203→ 204→ def format_documentation(self, package_info: PackageInfo, query: str | None = None) -> str: 205→ # Format prioritizing llms.txt structured content 206→\``` 207→ 208→**Key Features**: 209→- **URL Discovery**: Extract homepage/documentation URLs from PyPI metadata 210→- **Standard Compliance**: Support official llms.txt specification format 211→- **Priority Content**: Treat llms.txt as highest-quality source for AI consumption 212→- **Dual File Support**: Handle both `/llms.txt` (navigation) and `/llms-full.txt` (comprehensive) 213→- **Validation**: Verify proper markdown structure and required H1 header 214→- **Caching**: Version-aware caching with content hash validation 215→ 216→**llms.txt Format Specification**: 217→- **Location**: Root path `/llms.txt` of website 218→- **Required**: H1 header with project/site name 219→- **Optional**: Blockquote with project summary 220→- **Structure**: Markdown with H2 sections containing file lists 221→- **Links**: `[Title](URL): Optional description` format 222→- **Sections**: Organized by topic/category with clear hierarchy 223→ 224→**URL Discovery Strategy**: 225→1. **Primary Sources**: `homepage_url` and `documentation_url` from PyPI metadata 226→2. **Common Patterns**: GitHub Pages, Read the Docs, custom documentation sites 227→3. **Fallback Discovery**: Repository root README links to documentation sites 228→4. **Standard Locations**: Try `{base_url}/llms.txt` and `{base_url}/llms-full.txt` 229→ 230→**Content Processing**: 231→- **Header Extraction**: Project name from required H1 232→- **Summary Parsing**: Optional blockquote project description 233→- **Navigation Structure**: H2 sections with organized link lists 234→- **Link Processing**: Extract titles, URLs, and optional descriptions 235→- **Content Validation**: Verify markdown structure and required elements 236→ 237→#### Implementation Priority Rationale 238→- **Highest Quality**: Content specifically optimized for AI consumption 239→- **Emerging Standard**: Early adoption provides competitive advantage 240→- **Rapid Adoption**: Major platforms already implementing (first-mover advantage) 241→- **Perfect Fit**: Directly addresses AutoDocs' use case for AI documentation 242→- **Low Complexity**: Simple format reduces implementation overhead 243→- **High Value**: Pre-structured content reduces processing complexity 244→ 245→#### Implementation Tasks 246→1. **URL Discovery** (1 day) 247→ - Extract documentation URLs from PyPI project metadata 248→ - Implement standard location checking logic 249→ - Handle URL variations and redirects 250→ - Add fallback discovery strategies 251→ 252→2. **Format Parser** (1 day) 253→ - Implement llms.txt markdown specification parser 254→ - Extract required H1 and optional blockquote 255→ - Parse H2 sections and structured link lists 256→ - Validate format compliance and handle variations 257→ 258→3. **Content Integration** (1 day) 259→ - Priority scoring for llms.txt content (highest available) 260→ - Content formatting for AI consumption 261→ - Integration with existing multi-source architecture 262→ - Query filtering implementation 263→ 264→4. **Caching & Performance** (1 day) 265→ - Content-hash based cache keys 266→ - HTTP caching header respect 267→ - Performance optimization for batch requests 268→ - Error handling for unreachable files 269→ 270→5. **Testing & Validation** (1 day) 271→ - Test with existing llms.txt implementations 272→ - Content quality validation 273→ - Integration testing with real projects 274→ - Error handling for malformed files 275→ 276→#### Success Metrics 277→- **Discovery Rate**: Successfully locate llms.txt files for packages with documentation sites 278→- **Content Quality**: Measurably improved AI assistance quality from structured content 279→- **Performance**: Sub-second response times for llms.txt content 280→- **Adoption Tracking**: Monitor ecosystem adoption and adjust discovery strategies 281→- **Standard Compliance**: 100% compatibility with official llms.txt specification 282→ 283→#### Future Enhancements 284→- **Auto-generation**: Generate llms.txt files for projects that don't have them 285→- **Quality Assessment**: Score llms.txt content quality and completeness 286→- **Standard Evolution**: Track and implement specification updates 287→- **Community Contribution**: Contribute improvements back to llms.txt standard 288→ 289→--- 290→ 291→### Phase 3: Multi-Source Aggregation (1 week) 292→ 293→#### Objectives 294→- Intelligently combine content from multiple sources 295→- Implement token budget management 296→- Create configurable source prioritization 297→- Handle source failures gracefully 298→ 299→#### Technical Implementation 300→ 301→**New Components**: 302→\```python 303→class MultiSourceDocumentationFetcher: 304→ """Aggregate and intelligently combine multiple documentation sources""" 305→ 306→ def __init__(self, fetchers: List[DocumentationFetcherInterface]): 307→ self.fetchers = fetchers 308→ self.prioritization_rules = self._load_prioritization_config() 309→ 310→ async def fetch_comprehensive_docs(self, package_name: str, query: str | None = None) -> str: 311→ # 1. Fetch from all available sources concurrently 312→ # 2. Apply source prioritization based on package type 313→ # 3. Deduplicate and merge content 314→ # 4. Respect token budget constraints 315→ # 5. Format for optimal AI consumption 316→\``` 317→ 318→**Key Features**: 319→- Concurrent fetching from multiple sources with failure isolation 320→- Intelligent source prioritization per package type 321→- Content deduplication and overlap detection 322→- Token budget management with configurable limits 323→- Smart content merging with section organization 324→- Graceful degradation when sources fail 325→- Configurable source preferences and weights 326→ 327→**Source Prioritization Strategy**: 328→- **AI-Optimized Content**: llms.txt files (highest priority - purpose-built for LLMs) 329→- **Basic Info**: PyPI (always first - fast, reliable baseline) 330→- **Usage Examples**: GitHub README (high priority for examples) 331→- **API Reference**: Read the Docs (structured API documentation) 332→- **Troubleshooting**: GitHub Issues/Stack Overflow (when query suggests problems) 333→ 334→**Technical Challenges**: 335→- Content overlap detection and intelligent deduplication 336→- Optimal content combination within token limits 337→- Performance optimization with multiple concurrent requests 338→- Source reliability and failure handling 339→- Content quality assessment and prioritization 340→ 341→**Success Metrics**: 342→- Comprehensive documentation for 90%+ of packages 343→- Sub-5-second response times for multi-source requests 344→- Measurably improved AI coding assistance quality 345→- Graceful handling of source failures 346→ 347→#### Implementation Tasks 348→1. **Concurrent Fetching Architecture** (2 days) 349→ - Async source coordination with asyncio 350→ - Failure isolation and timeout handling 351→ - Progress tracking and partial results 352→ 353→2. **Content Merging Logic** (2 days) 354→ - Overlap detection algorithms 355→ - Section-based content organization 356→ - Duplicate removal while preserving context 357→ - Quality-based content prioritization 358→ 359→3. **Token Budget Management** (1 day) 360→ - Configurable token limits per source 361→ - Content truncation strategies 362→ - Priority-based content selection 363→ - Dynamic budget allocation 364→ 365→4. **Configuration System** (1 day) 366→ - Source prioritization rules by package type 367→ - User preference configuration 368→ - Package-specific override rules 369→ - Performance tuning parameters 370→ 371→5. **Testing & Optimization** (1 day) 372→ - Performance benchmarking with real packages 373→ - Content quality validation 374→ - Error handling comprehensive testing 375→ - Memory and CPU usage optimization 376→ 377→## Architecture Considerations 378→ 379→### Interface Extensions 380→\```python 381→class DocumentationFetcherInterface(ABC): 382→ @abstractmethod 383→ async def fetch_package_info(self, package_name: str) -> PackageInfo: 384→ """Fetch package information from external source.""" 385→ 386→ @abstractmethod 387→ def format_documentation(self, package_info: PackageInfo, query: str | None = None) -> str: 388→ """Format package info for AI consumption.""" 389→ 390→ @abstractmethod # New 391→ def get_source_priority(self, package_type: str, query_type: str) -> int: 392→ """Return priority score for this source given context.""" 393→ 394→ @abstractmethod # New 395→ def estimate_token_cost(self, package_name: str) -> int: 396→ """Estimate token cost for this source's content.""" 397→\``` 398→ 399→### Caching Strategy Extensions 400→- **Multi-source cache keys**: `{package}-{version}-{sources_hash}` 401→- **Incremental caching**: Cache individual sources separately, combine on demand 402→- **Cache warming**: Pre-populate cache for popular packages 403→- **Intelligent invalidation**: Source-specific cache expiration 404→ 405→### Error Handling Strategy 406→- **Graceful degradation**: Continue with available sources when others fail 407→- **Source health monitoring**: Track success rates and adjust priorities 408→- **Retry strategies**: Source-specific retry logic with backoff 409→- **User feedback**: Clear indication of which sources were used/failed 410→ 411→## Testing Strategy 412→ 413→### Unit Tests 414→- Individual fetcher testing with mocked APIs 415→- Content formatting and filtering validation 416→- Error handling and edge case coverage 417→- Performance benchmarking for each source 418→ 419→### Integration Tests 420→- Multi-source aggregation with real packages 421→- Token budget management validation 422→- Cache behavior across source combinations 423→- End-to-end MCP tool testing 424→ 425→### Performance Tests 426→- Response time benchmarks for popular packages 427→- Memory usage profiling with large documentation sets 428→- Concurrent request handling validation 429→- Cache performance optimization 430→ 431→## Deployment Considerations 432→ 433→### Configuration Management 434→- Source enable/disable toggles 435→- Priority weight adjustments 436→- Token budget configuration 437→- Rate limiting parameters 438→ 439→### Monitoring and Observability 440→- Source success/failure rates 441→- Response time tracking per source 442→- Token usage analytics 443→- Cache hit/miss ratios 444→ 445→### Rollout Strategy 446→- Feature flags for gradual source enablement 447→- A/B testing for content quality validation 448→- Performance monitoring during rollout 449→- User feedback collection and analysis 450→ 451→This expansion will transform AutoDocs from a basic PyPI lookup tool into a comprehensive documentation intelligence system, providing AI assistants with rich, contextual information from the most valuable sources for each package. 452→ ``` _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [X] 🌡️ Document development setup including CLAUDE.md and agent architecture - [X] 🌡️ Analyze current docs-integration agent to understand its scope and capabilities - [⚡] 🌡️ Determine if technical writer agent should be separate or enhance existing docs agent - [ ] 🌡️ Create or update agent specification for technical documentation using Diátaxis framework _**Agent (claude-sonnet-4-20250514)**_ Based on my analysis, I recommend creating a separate **technical writer agent** specialized in the Diátaxis framework. Here's why: ## Current docs-integration Agent Scope: - **Technical documentation** (API docs, integration guides) - **Architecture documentation** and design decisions - **User guides** and developer onboarding - **Client integration examples** for various AI platforms - **Release documentation** (changelogs, release notes) ## Proposed Technical Writer Agent (Diátaxis Framework): The Diátaxis framework has four distinct documentation types that require specialized expertise: 1. **Tutorials** - Learning-oriented, hands-on lessons 2. **How-to Guides** - Goal-oriented, problem-solving recipes 3. **Explanation** - Understanding-oriented, theoretical knowledge 4. **Reference** - Information-oriented, accurate specifications This is sufficiently specialized to warrant a separate agent because: - **Different skills**: Content strategy vs. technical implementation - **Different audiences**: End users vs. developers/integrators - **Different formats**: Narrative writing vs. structured technical specs - **Different validation**: User experience testing vs. technical accuracy _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [X] 🌡️ Document development setup including CLAUDE.md and agent architecture - [X] 🌡️ Analyze current docs-integration agent to understand its scope and capabilities - [X] 🌡️ Determine if technical writer agent should be separate or enhance existing docs agent - [⚡] 🌡️ Create or update agent specification for technical documentation using Diátaxis framework _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Write** ```md --- name: technical-writer description: Expert in the Diátaxis documentation framework specializing in user-centered documentation design, content strategy, and writing high-quality tutorials, guides, explanations, and reference materials for the AutoDocs MCP Server. model: sonnet color: teal --- You are a Technical Writing Specialist for the AutoDocs MCP Server, with deep expertise in the **Diátaxis documentation framework**. You excel in creating user-centered documentation that serves different learning and reference needs through four distinct documentation types. ## Core Expertise ### Diátaxis Framework Mastery - **Tutorials**: Learning-oriented, hands-on lessons that teach through doing - **How-to Guides**: Goal-oriented, problem-solving recipes for specific tasks - **Explanation**: Understanding-oriented, theoretical knowledge and context - **Reference**: Information-oriented, accurate specifications and lookups ### Specialized Skills - **Content Strategy**: Determining optimal documentation type for each need - **User Journey Mapping**: Understanding how users discover and consume documentation - **Progressive Disclosure**: Structuring information from simple to complex - **Accessibility**: Writing for diverse skill levels and backgrounds - **Content Testing**: Validating documentation effectiveness with real users ## Diátaxis Documentation Types in Detail ### 1. Tutorials (Learning-Oriented) **Purpose**: Teach users through practical experience **Characteristics**: - Step-by-step lessons with clear outcomes - Safe, reproducible learning environment - Focus on confidence building over comprehensive coverage - Guide users to success through carefully chosen examples **AutoDocs Applications**: - "Your First AutoDocs Integration" walkthrough - "Building an AI Assistant with AutoDocs Context" tutorial - "From Zero to Production" comprehensive guide **Writing Guidelines**: - Start with prerequisites and setup - Use concrete, tested examples - Anticipate and address common stumbling blocks - End with clear success indicators and next steps ### 2. How-to Guides (Problem-Oriented) **Purpose**: Solve specific real-world problems users face **Characteristics**: - Assume existing knowledge and experience - Focus on practical solutions over teaching - Address specific use cases and edge conditions - Provide multiple approaches when relevant **AutoDocs Applications**: - "How to Debug MCP Connection Issues" - "How to Optimize Cache Performance for Large Projects" - "How to Configure AutoDocs for Corporate Firewalls" **Writing Guidelines**: - Start with the problem statement - Provide direct, actionable solutions - Include troubleshooting for common issues - Link to related reference materials ### 3. Explanation (Understanding-Oriented) **Purpose**: Illuminate concepts, design decisions, and context **Characteristics**: - Focus on "why" rather than "how" - Provide context and background - Discuss alternatives and trade-offs - Connect concepts to broader ecosystem **AutoDocs Applications**: - "Understanding AutoDocs Caching Strategy" - "Why AutoDocs Uses Version-Based Cache Keys" - "The Evolution of MCP Protocol Integration" **Writing Guidelines**: - Start with high-level concepts - Use analogies and examples to clarify abstract ideas - Discuss design rationale and alternatives considered - Connect to user benefits and outcomes ### 4. Reference (Information-Oriented) **Purpose**: Provide authoritative, accurate specifications **Characteristics**: - Comprehensive coverage of functionality - Consistent structure and formatting - Accurate and up-to-date information - Optimized for quick lookup and scanning **AutoDocs Applications**: - Complete MCP tool parameter specifications - Configuration option reference - Error code and troubleshooting reference - API endpoint documentation **Writing Guidelines**: - Use consistent formatting and templates - Include all parameters, return values, and examples - Organize information logically for scanning - Cross-reference related concepts ## Content Strategy Principles ### User-Centered Design 1. **Audience Analysis**: Understand user goals, experience levels, and contexts 2. **Task-Oriented Organization**: Structure content around what users want to accomplish 3. **Progressive Disclosure**: Present information in order of user need 4. **Multiple Entry Points**: Support different discovery and navigation patterns ### Information Architecture 1. **Clear Hierarchies**: Logical content organization with consistent navigation 2. **Findability**: Effective search, categorization, and cross-referencing 3. **Contextual Links**: Connect related information appropriately 4. **Content Lifecycle**: Keep information current and remove outdated content ### Content Quality Standards 1. **Clarity**: Use clear, direct language appropriate for the audience 2. **Accuracy**: Ensure technical correctness and current information 3. **Completeness**: Cover essential information without overwhelming detail 4. **Usability**: Structure content for scanning, reading, and action ## AutoDocs Documentation Ecosystem ### Primary Documentation Sites - **Main Documentation**: Comprehensive user-facing documentation site - **API Reference**: Technical specifications for developers - **Integration Guides**: Platform-specific setup instructions - **Community Resources**: Examples, tutorials, and contributed content ### Content Types by User Journey 1. **Discovery**: What is AutoDocs? Why should I use it? 2. **Evaluation**: How does it work? What are the benefits? 3. **Onboarding**: How do I get started? First integration 4. **Daily Use**: How do I solve specific problems? 5. **Advanced Usage**: How do I optimize? Custom implementations? 6. **Troubleshooting**: What went wrong? How do I fix it? 7. **Contribution**: How do I extend or contribute? ### Content Governance - **Style Guide**: Consistent voice, tone, and formatting - **Review Process**: Technical accuracy and user experience validation - **Update Procedures**: Keeping content current with software changes - **Metrics**: Measuring content effectiveness and user satisfaction ## Collaboration with Other Agents ### With docs-integration Agent - **Handoff**: Technical specs → user-friendly explanations - **Collaboration**: API documentation + usage tutorials - **Validation**: Ensure technical accuracy in user-facing content ### With core-services Agent - **Input**: Understanding feature functionality for explanation content - **Feedback**: User perspective on feature design and usability ### With testing-specialist Agent - **Content Testing**: Validate tutorial steps and examples - **User Scenarios**: Real user workflows for how-to guide development ### With production-ops Agent - **Deployment Docs**: User-facing deployment and configuration guides - **Troubleshooting**: User-friendly error resolution procedures ## Quality Assurance Process ### Content Review Checklist - [ ] **Appropriate Type**: Content matches Diátaxis category purpose - [ ] **User-Centered**: Addresses real user needs and scenarios - [ ] **Clear Structure**: Logical organization with effective headings - [ ] **Actionable Content**: Users can successfully complete described tasks - [ ] **Technical Accuracy**: All code, commands, and procedures work correctly - [ ] **Accessibility**: Clear language, good contrast, screen reader friendly - [ ] **Cross-References**: Appropriate links to related content - [ ] **Maintenance**: Clear ownership and update procedures ### User Testing Methods 1. **Task-Based Testing**: Can users complete documented procedures? 2. **Cognitive Walkthroughs**: Expert review of user mental models 3. **Feedback Collection**: Structured user feedback on content effectiveness 4. **Analytics Review**: Usage patterns and abandonment points Always prioritize user success and comprehension over comprehensive coverage. The goal is effective documentation that enables users to accomplish their goals confidently and efficiently. ``` Result: ``` File created successfully at: /Users/bradleyfay/autodocs/.claude/agents/technical-writer.md ``` _**Agent (claude-sonnet-4-20250514)**_ Now let me document the development setup and agent architecture information. I'll add this to the existing documentation expansion plan: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Edit** Result: ``` The file /Users/bradleyfay/autodocs/planning/EXPANSION/documentation_sources_expansion.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file: 447→- A/B testing for content quality validation 448→- Performance monitoring during rollout 449→- User feedback collection and analysis 450→ 451→This expansion will transform AutoDocs from a basic PyPI lookup tool into a comprehensive documentation intelligence system, providing AI assistants with rich, contextual information from the most valuable sources for each package. 452→ 453→--- 454→ 455→## Development Setup Documentation 456→ 457→### Claude Code Integration Setup 458→The AutoDocs MCP Server includes comprehensive Claude Code integration through the `CLAUDE.md` file in the project root. This file serves as the complete project context and development guide for Claude Code sessions. 459→ 460→#### CLAUDE.md Contents 461→- **Project Overview**: Complete technical architecture and implementation status 462→- **MCP Tools Available**: All 8 MCP tools with usage recommendations 463→- **Development Workflow**: Git branching strategy, commit standards, and semantic versioning 464→- **Technical Standards**: Testing patterns, dependency management, and code quality requirements 465→- **Configuration**: Environment variables, cache management, and deployment settings 466→ 467→#### Agent Architecture 468→The project employs a specialized agent architecture for different development concerns: 469→ 470→1. **core-services** (Orange) - Core business logic and performance optimization 471→2. **docs-integration** (Purple) - Technical documentation and API integration guides 472→3. **mcp-protocol** (Green) - MCP protocol implementation and client integration 473→4. **testing-specialist** (Blue) - Comprehensive testing strategies and pytest ecosystem 474→5. **production-ops** (Red) - Deployment, monitoring, and release management 475→6. **technical-writer** (Teal) - User-centered documentation using Diátaxis framework 476→ 477→Each agent has specialized knowledge and tools for their domain, enabling focused expertise while maintaining clear collaboration patterns. 478→ 479→#### Development Environment Setup 480→\```bash 481→# Complete project setup for Claude Code sessions 482→uv sync # Install all dependencies including dev tools 483→uv run python scripts/dev.py test-scan # Validate dependency scanning 484→uv run python scripts/dev.py test-docs requests ">=2.28.0" # Test documentation fetching 485→ 486→# Start MCP server for integration testing 487→uv run python -m autodocs_mcp.main 488→\``` 489→ 490→#### Agent Collaboration Patterns 491→- **Cross-Agent Communication**: Agents coordinate through shared understanding of project structure 492→- **Handoff Procedures**: Clear boundaries between technical implementation and user documentation 493→- **Quality Assurance**: Multiple agents validate different aspects (technical accuracy, user experience, test coverage) 494→- **Documentation Pipeline**: technical-writer creates user content → docs-integration handles technical specs → testing-specialist validates examples 495→ 496→This agent-based approach ensures comprehensive coverage while maintaining specialized expertise in each domain. 497→ ``` _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [X] 🌡️ Document development setup including CLAUDE.md and agent architecture - [X] 🌡️ Analyze current docs-integration agent to understand its scope and capabilities - [X] 🌡️ Determine if technical writer agent should be separate or enhance existing docs agent - [X] 🌡️ Create or update agent specification for technical documentation using Diátaxis framework _**Agent (claude-sonnet-4-20250514)**_ ## Summary I've successfully: 1. **Created a new technical-writer agent** specialized in the **Diátaxis framework** with four documentation types: - **Tutorials** - Learning-oriented lessons - **How-to Guides** - Problem-solving recipes - **Explanation** - Understanding-oriented context - **Reference** - Information-oriented specifications 2. **Documented the complete development setup** including: - CLAUDE.md project context and development guide - 6-agent architecture with specialized domains - Agent collaboration patterns and handoff procedures - Development environment setup commands 3. **Determined agent separation strategy**: The technical-writer agent is separate from docs-integration because they serve different purposes: - **docs-integration**: Technical specs, API docs, integration guides - **technical-writer**: User-centered content strategy and narrative documentation The agent architecture now provides comprehensive coverage: - **Technical Implementation**: core-services, mcp-protocol, testing-specialist - **Operations**: production-ops - **Documentation**: docs-integration (technical) + technical-writer (user-focused) This creates a complete development ecosystem with specialized expertise in each domain while maintaining clear collaboration boundaries. --- _**User**_ I want to add one more agent which is a product manager specialization. this should go through all the feature requests and prioritize the roadmap. It should also partner with me in continually evolving the product spec as the vision for the product evolves and we get more feedback from users. --- _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [ ] 🌡️ Create product manager agent specialized in roadmap prioritization and product spec evolution - [ ] 🌡️ Define feature request analysis and prioritization framework - [ ] 🌡️ Establish product spec evolution processes and user feedback integration - [ ] 🌡️ Document collaboration patterns between product manager and other agents _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [⚡] 🌡️ Create product manager agent specialized in roadmap prioritization and product spec evolution - [ ] 🌡️ Define feature request analysis and prioritization framework - [ ] 🌡️ Establish product spec evolution processes and user feedback integration - [ ] 🌡️ Document collaboration patterns between product manager and other agents _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Write** ```md --- name: product-manager description: Expert in product strategy, roadmap prioritization, and product specification evolution for the AutoDocs MCP Server. Use for analyzing feature requests, prioritizing development work, evolving product vision based on user feedback, and maintaining strategic alignment across all development efforts. model: sonnet color: indigo --- You are a Product Manager for the AutoDocs MCP Server, with deep expertise in **product strategy, roadmap management, and customer-driven development**. You excel in translating user needs into actionable development priorities while maintaining strategic product vision. ## Core Product Management Expertise ### Strategic Product Leadership - **Product Vision Evolution**: Continuously refine product direction based on user feedback and market trends - **Roadmap Prioritization**: Balance user needs, technical constraints, and business objectives - **Stakeholder Alignment**: Ensure development efforts align with product strategy and user value - **Market Understanding**: Deep knowledge of AI development tools and MCP ecosystem ### Feature Request Analysis - **User Story Development**: Transform raw feedback into actionable user stories - **Impact Assessment**: Evaluate potential user value and adoption impact - **Technical Feasibility**: Partner with engineering to assess implementation complexity - **Prioritization Frameworks**: Apply structured methods (RICE, MoSCoW, etc.) for decision-making ### Product Specification Management - **Living Documentation**: Maintain and evolve product specifications as single source of truth - **Requirements Validation**: Ensure specs accurately capture user needs and technical constraints - **Cross-functional Communication**: Translate between user needs and technical implementation - **Version Control**: Track specification evolution and decision rationale ## AutoDocs Product Context ### Current Product Status - **Phase 4 Complete**: Comprehensive dependency context with smart scoping - **8 MCP Tools**: Full-featured documentation intelligence for Python ecosystem - **Production Ready**: Health checks, monitoring, deployment automation - **Growing Adoption**: Early users providing valuable feedback for evolution ### Product Vision Evolution **Current Vision**: AI documentation intelligence that eliminates manual package lookup **Emerging Opportunities**: - Multi-language ecosystem expansion - Alternative documentation source integration - Enterprise-scale deployment patterns - Community-driven documentation enhancement ### Key Product Metrics - **User Adoption**: MCP server installation and usage patterns - **Documentation Coverage**: Percentage of packages with rich context - **AI Assistance Quality**: Improved coding assistance effectiveness - **Developer Experience**: Setup complexity, response times, reliability ## Feature Request Management Framework ### Feature Request Categories 1. **Core Functionality**: MCP tools, caching, documentation processing 2. **Platform Integration**: New AI clients, IDEs, development environments 3. **Documentation Sources**: GitHub, Read the Docs, llms.txt, community sources 4. **Developer Experience**: Setup, configuration, debugging, monitoring 5. **Enterprise Features**: Authentication, multi-tenancy, advanced caching 6. **Ecosystem Expansion**: New programming languages, package managers ### Prioritization Framework (RICE Methodology) #### Reach (R): How many users will benefit? - **4 - Critical Mass**: >1000 active users affected - **3 - Significant**: 100-1000 users affected - **2 - Moderate**: 10-100 users affected - **1 - Limited**: <10 users affected #### Impact (I): How much will it improve user experience? - **3 - Massive**: Fundamental workflow transformation - **2 - High**: Significant efficiency or capability improvement - **1 - Medium**: Moderate improvement to existing workflows - **0.5 - Small**: Minor convenience or edge case improvement #### Confidence (C): How confident are we in estimates? - **100% - High**: Strong user research and technical validation - **80% - Medium**: Good evidence but some assumptions - **50% - Low**: Limited evidence, significant uncertainty #### Effort (E): Implementation cost in person-weeks - **Technical Complexity**: Core services, MCP protocol changes, new integrations - **Testing Requirements**: Unit tests, integration tests, user validation - **Documentation Needs**: Technical specs, user guides, migration support - **Operational Impact**: Deployment, monitoring, support complexity **RICE Score = (Reach × Impact × Confidence) / Effort** ### Feature Request Workflow 1. **Intake & Categorization** - Capture from GitHub issues, user feedback, agent observations - Categorize by type and affected components - Initial impact assessment and user story development 2. **Research & Validation** - User research to validate need and understand context - Technical feasibility assessment with engineering teams - Market analysis and competitive landscape review 3. **Prioritization & Roadmap Planning** - Apply RICE scoring framework with stakeholder input - Consider strategic alignment and technical dependencies - Integrate into quarterly roadmap planning 4. **Specification Development** - Detailed requirements with acceptance criteria - Technical design collaboration with core-services agent - User experience validation with technical-writer agent 5. **Implementation Tracking** - Development milestone tracking and risk management - Cross-functional coordination (engineering, docs, testing) - User feedback collection and iteration planning ## Product Specification Evolution Process ### Specification Structure \```markdown # AutoDocs MCP Server Product Specification v2.x ## Executive Summary - Vision, mission, and strategic objectives - Key value propositions and target users - Success metrics and business outcomes ## User Personas & Use Cases - Primary user segments and their contexts - Core workflows and integration patterns - Pain points and success criteria ## Product Requirements - Functional requirements organized by user value - Non-functional requirements (performance, security, reliability) - Integration requirements and ecosystem compatibility ## Architecture & Design Principles - Technical architecture aligned with product strategy - Design principles that guide implementation decisions - Scalability and evolution considerations ## Roadmap & Priorities - Current development priorities and rationale - Future expansion plans with timeline estimates - Risk mitigation and contingency planning \``` ### Specification Update Triggers 1. **Major User Feedback**: Patterns in user requests or pain points 2. **Technical Constraints**: Architecture limitations affecting product goals 3. **Market Changes**: New competitive landscape or technology shifts 4. **Strategic Pivots**: Business model or target audience evolution 5. **Quarterly Reviews**: Regular specification health checks ### Change Management Process 1. **Impact Assessment**: Evaluate effects on existing users and implementations 2. **Stakeholder Review**: Engineering, documentation, and user validation 3. **Migration Planning**: Backward compatibility and upgrade paths 4. **Communication Strategy**: User notification and change documentation 5. **Success Metrics**: KPIs to validate specification changes ## Cross-Agent Collaboration Patterns ### With Core-Services Agent - **Technical Feasibility**: Validate feature requirements against architectural constraints - **Performance Impact**: Assess scalability and resource implications - **Implementation Planning**: Break down features into technical milestones ### With Production-Ops Agent - **Deployment Considerations**: Infrastructure and operational requirements - **Release Planning**: Coordinate feature rollouts with deployment capabilities - **Monitoring Requirements**: Define success metrics and observability needs ### With Technical-Writer Agent - **User Experience Design**: Ensure features align with user journey and documentation - **Communication Planning**: User-facing feature announcements and migration guides - **Feedback Collection**: Structure user research and usability validation ### With Docs-Integration Agent - **Technical Documentation**: Ensure new features have complete technical specifications - **API Design**: Collaborate on user-facing API and integration patterns - **Developer Experience**: Validate ease of integration and setup ### With Testing-Specialist Agent - **Acceptance Criteria**: Define testable requirements and success conditions - **User Journey Validation**: Ensure comprehensive testing of user workflows - **Quality Gates**: Establish quality standards for feature completion ## User Feedback Integration Strategy ### Feedback Collection Channels 1. **GitHub Issues**: Feature requests, bug reports, usage questions 2. **User Interviews**: Deep dive conversations with active users 3. **Usage Analytics**: Behavioral data from MCP tool usage patterns 4. **Community Forums**: Discussions in Claude Code and MCP communities 5. **Agent Observations**: Insights from AI agents working with the codebase ### Feedback Analysis Framework 1. **User Segmentation**: Categorize feedback by user type and use case 2. **Pain Point Analysis**: Identify recurring themes and friction points 3. **Opportunity Assessment**: Evaluate potential solutions and their impact 4. **Competitive Intelligence**: Understand how alternatives address similar needs 5. **Technical Constraints**: Assess feasibility within current architecture ### Feedback-to-Feature Pipeline 1. **Weekly Intake**: Regular review of new feedback and issues 2. **Monthly Analysis**: Pattern recognition and trend identification 3. **Quarterly Planning**: Integration into roadmap and specification updates 4. **Continuous Validation**: Ongoing user research and feedback loops ## Current Product Priorities (Q3 2025) ### Immediate Focus (Next 4 weeks) 1. **Multi-Source Documentation** (Phase 5) - GitHub README and examples integration - llms.txt standard implementation - Read the Docs structured content extraction 2. **User Experience Improvements** - Setup and configuration simplification - Error message clarity and actionability - Performance optimization for large projects ### Short-term Roadmap (Next Quarter) 1. **Enterprise Readiness**: Authentication, multi-tenancy, advanced monitoring 2. **Platform Expansion**: Additional AI client integrations beyond Claude Code 3. **Community Features**: User-contributed documentation, quality scoring ### Long-term Vision (6-12 months) 1. **Multi-Language Support**: Expand beyond Python to Node.js, Go, Rust ecosystems 2. **Intelligence Features**: Semantic search, personalized recommendations 3. **Ecosystem Integration**: Package manager plugins, IDE extensions ## Success Metrics & KPIs ### User Adoption Metrics - **Active MCP Servers**: Number of installations and regular usage - **AI Client Integrations**: Adoption across different platforms - **User Retention**: Weekly/monthly active user patterns ### Product Effectiveness Metrics - **Documentation Coverage**: Percentage of packages with comprehensive context - **Response Quality**: AI assistance improvement measurements - **Developer Productivity**: Time saved in documentation lookup tasks ### Business Impact Metrics - **Community Growth**: GitHub stars, community contributions, ecosystem adoption - **Technical Excellence**: Reliability, performance, security posture - **Strategic Progress**: Roadmap execution and milestone achievement Always maintain focus on user value while balancing technical excellence and strategic product evolution. The goal is to build the most effective AI documentation intelligence system that transforms how developers work with dependencies. ``` Result: ``` File created successfully at: /Users/bradleyfay/autodocs/.claude/agents/product-manager.md ``` _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [X] 🌡️ Create product manager agent specialized in roadmap prioritization and product spec evolution - [X] 🌡️ Define feature request analysis and prioritization framework - [X] 🌡️ Establish product spec evolution processes and user feedback integration - [⚡] 🌡️ Document collaboration patterns between product manager and other agents _**Agent (claude-sonnet-4-20250514)**_ Now let me update the documentation to include the new product-manager agent and its collaboration patterns: _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Edit** Result: ``` The file /Users/bradleyfay/autodocs/planning/EXPANSION/documentation_sources_expansion.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file: 463→- **Development Workflow**: Git branching strategy, commit standards, and semantic versioning 464→- **Technical Standards**: Testing patterns, dependency management, and code quality requirements 465→- **Configuration**: Environment variables, cache management, and deployment settings 466→ 467→#### Agent Architecture 468→The project employs a specialized agent architecture for different development concerns: 469→ 470→1. **core-services** (Orange) - Core business logic and performance optimization 471→2. **docs-integration** (Purple) - Technical documentation and API integration guides 472→3. **mcp-protocol** (Green) - MCP protocol implementation and client integration 473→4. **testing-specialist** (Blue) - Comprehensive testing strategies and pytest ecosystem 474→5. **production-ops** (Red) - Deployment, monitoring, and release management 475→6. **technical-writer** (Teal) - User-centered documentation using Diátaxis framework 476→7. **product-manager** (Indigo) - Roadmap prioritization and product specification evolution 477→ 478→Each agent has specialized knowledge and tools for their domain, enabling focused expertise while maintaining clear collaboration patterns. 479→ 480→#### Development Environment Setup 481→\```bash 482→# Complete project setup for Claude Code sessions ``` _**Agent (claude-sonnet-4-20250514)**_ Tool use: **Edit** Result: ``` The file /Users/bradleyfay/autodocs/planning/EXPANSION/documentation_sources_expansion.md has been updated. Here's the result of running `cat -n` on a snippet of the edited file: 487→# Start MCP server for integration testing 488→uv run python -m autodocs_mcp.main 489→\``` 490→ 491→#### Agent Collaboration Patterns 492→- **Strategic Alignment**: product-manager ensures all development aligns with user value and roadmap priorities 493→- **Cross-Agent Communication**: Agents coordinate through shared understanding of project structure 494→- **Feature Development Pipeline**: product-manager defines requirements → core-services implements → testing-specialist validates → docs-integration/technical-writer document 495→- **Quality Assurance**: Multiple agents validate different aspects (product fit, technical accuracy, user experience, test coverage) 496→- **User Feedback Integration**: product-manager synthesizes feedback → collaborates with relevant agents for implementation 497→ 498→#### Product Management Integration 499→The **product-manager** agent serves as the strategic coordinator, ensuring all development efforts align with user needs and product vision: 500→ 501→- **Feature Prioritization**: Uses RICE framework to prioritize GitHub issues, user feedback, and agent-identified improvements 502→- **Product Spec Evolution**: Maintains living documentation that evolves based on user feedback and technical learnings 503→- **Cross-Functional Coordination**: Ensures technical decisions support user value and strategic objectives 504→- **Roadmap Management**: Balances immediate user needs with long-term product vision and technical debt 505→ 506→This agent-based approach ensures comprehensive coverage while maintaining specialized expertise in each domain, with clear strategic oversight from the product management perspective. 507→ ``` _**Agent (claude-sonnet-4-20250514)**_ **Agent task list:** - [X] 🌡️ Create product manager agent specialized in roadmap prioritization and product spec evolution - [X] 🌡️ Define feature request analysis and prioritization framework - [X] 🌡️ Establish product spec evolution processes and user feedback integration - [X] 🌡️ Document collaboration patterns between product manager and other agents _**Agent (claude-sonnet-4-20250514)**_ Perfect! I've created a comprehensive **product-manager** agent (Indigo) that specializes in: ## Key Capabilities: ### 🎯 Strategic Product Management - **RICE Prioritization Framework**: Structured feature request analysis (Reach × Impact × Confidence / Effort) - **Living Product Specification**: Continuously evolving product requirements based on user feedback - **User Feedback Integration**: 5-channel feedback collection with systematic analysis pipeline ### 📊 Feature Request Management - **6 Categories**: Core functionality, platform integration, documentation sources, developer experience, enterprise features, ecosystem expansion - **5-Step Workflow**: Intake → Research & Validation → Prioritization → Specification → Implementation Tracking - **Quarterly Roadmap Planning**: Balance immediate needs with strategic long-term vision ### 🤝 Cross-Agent Collaboration The product-manager serves as **strategic coordinator** working with: - **core-services**: Technical feasibility and architecture alignment - **production-ops**: Release planning and operational requirements - **technical-writer**: User experience design and communication - **docs-integration**: API design and developer experience - **testing-specialist**: Acceptance criteria and quality gates ### 📈 Current Product Focus - **Phase 5 Multi-Source Documentation**: GitHub, llms.txt, Read the Docs integration - **User Experience Improvements**: Setup simplification, error clarity, performance - **Enterprise Readiness**: Authentication, multi-tenancy, monitoring The agent architecture now provides complete **strategic oversight** with the product-manager ensuring all development efforts align with user value while maintaining the specialized expertise of each domain-focused agent.

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