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This allows tools like GitHub Copilot or Claude to generate code that follows your project's conventions and integrates properly with existing modules.","intents":["I want GitHub Copilot to understand my codebase structure and generate code that fits my architecture","I need AI coding assistants to know about my project's patterns and conventions","I want to use LLM-assisted coding without losing consistency with my existing codebase"],"best_for":["Teams using AI coding assistants (Copilot, Claude, etc.) wanting better context","Organizations wanting to maintain code consistency while using AI-assisted development","Projects with strong architectural patterns that should be preserved in AI-generated code"],"limitations":["Context injection adds latency to AI coding assistant requests","Token budget for LLM requests increases due to additional context","AI-generated code may still violate project conventions despite context injection","Requires integration with specific IDE and AI assistant (limited to supported combinations)"],"requires":["Supported IDE: VS Code, JetBrains IDEs, or Neovim","Supported AI assistant: GitHub Copilot, Claude, or OpenAI API","Generated documentation and codebase analysis from Mutable"],"input_types":["code context","documentation","codebase structure"],"output_types":["context-enriched prompts","codebase-aware suggestions"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_2","uri":"capability://automation.workflow.incremental.documentation.updates.on.code.changes","name":"incremental documentation updates on code changes","description":"Mutable monitors Git commits and diffs to identify which code elements have changed, then selectively regenerates documentation only for affected modules and functions rather than re-documenting the entire codebase. This uses a change-tracking system that maps commits to code elements and maintains a documentation state graph, enabling efficient updates that scale to large codebases without regenerating unchanged documentation.","intents":["I want documentation to update automatically when I push code changes without manual triggering","I need documentation to stay in sync with my development workflow without slowing down CI/CD","I want to see what documentation changed alongside code changes in pull requests"],"best_for":["Teams with continuous deployment workflows requiring fast feedback loops","Large codebases where full re-documentation would be prohibitively expensive","Organizations wanting documentation updates to be part of the standard commit workflow"],"limitations":["Requires Git integration; works only with Git-based repositories","Change detection is based on file-level diffs; refactoring that preserves functionality but changes structure may trigger unnecessary regeneration","Merge conflicts in documentation may occur if multiple branches modify the same code elements","Initial indexing of existing codebase can take 5-30 minutes depending on size"],"requires":["Git repository with commit history accessible to Mutable","Webhook or polling integration with Git provider (GitHub, GitLab, Bitbucket)","Write access to documentation storage (wiki, markdown files, or Mutable's hosted wiki)"],"input_types":["git commits","file diffs","branch metadata"],"output_types":["updated documentation","change logs","documentation diffs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_3","uri":"capability://text.generation.language.multi.language.codebase.documentation.in.unified.wiki","name":"multi-language codebase documentation in unified wiki","description":"Mutable generates a unified, searchable wiki that documents codebases containing multiple programming languages, maintaining consistent structure and navigation across polyglot projects. The system normalizes documentation across language-specific conventions (e.g., Python docstrings vs. Java Javadoc) into a common format, enabling developers to navigate and understand code regardless of which language each module is written in.","intents":["I have a microservices architecture with services in different languages and need unified documentation","I want developers to understand the full system without needing to learn language-specific documentation conventions","I need to document frontend (JavaScript), backend (Python), and infrastructure (Go) in one place"],"best_for":["Polyglot organizations with services in multiple languages","Microservices architectures requiring cross-service documentation","Teams with mixed-language codebases (e.g., Node.js + Python + Go)"],"limitations":["Documentation quality may vary across languages if AST parsing support differs","Language-specific idioms and patterns may be lost in normalization to a common format","Cross-language dependency tracking (e.g., Python calling Node.js via gRPC) requires additional configuration","Search indexing across multiple languages may have varying accuracy"],"requires":["Supported languages: Python, JavaScript, TypeScript, Java, Go, Rust, C++","Separate repository access for each language/service","Consistent naming conventions across services for effective cross-linking"],"input_types":["source code in multiple languages","git repositories","service definitions"],"output_types":["unified wiki","cross-language documentation","service dependency maps"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_4","uri":"capability://search.retrieval.semantic.code.search.and.documentation.retrieval","name":"semantic code search and documentation retrieval","description":"Mutable indexes generated documentation alongside code structure to enable semantic search that understands intent rather than just keyword matching. When a developer searches for 'authentication flow' or 'database connection pooling', the system returns relevant code elements and documentation based on semantic understanding of what the code does, not just string matching against function names or comments.","intents":["I want to find all code related to a feature or concept without knowing exact function names","I need to understand how authentication is implemented across my codebase","I want to search documentation and get back relevant code examples"],"best_for":["Large codebases (10k+ lines) where keyword search is insufficient","Teams with high developer turnover needing efficient code discovery","Organizations wanting to reduce time spent understanding unfamiliar code"],"limitations":["Semantic search quality depends on documentation quality; poor documentation produces poor search results","Search latency may be higher than keyword-based search due to embedding computation","Requires sufficient documentation coverage; undocumented code elements won't be discoverable","Cross-repository search is not supported; searches are scoped to single codebase"],"requires":["Codebase with generated documentation indexed","Embedding model access (OpenAI, Anthropic, or Mutable's internal model)","Vector database for storing and querying embeddings"],"input_types":["natural language search queries","code snippets"],"output_types":["ranked documentation results","code element matches","relevance scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_5","uri":"capability://safety.moderation.automated.documentation.quality.assessment.and.flagging","name":"automated documentation quality assessment and flagging","description":"Mutable analyzes generated documentation to identify quality issues such as incomplete descriptions, missing examples, or inconsistent formatting, then flags these for human review or automatic improvement. The system uses heuristics and LLM-based analysis to detect when documentation is too vague, contradicts code behavior, or lacks sufficient detail for developers to understand implementation.","intents":["I want to know which parts of my documentation are incomplete or need improvement","I need to ensure generated documentation meets quality standards before it's published","I want to identify code that's too complex to be adequately documented automatically"],"best_for":["Teams with high documentation quality standards","Organizations wanting to maintain documentation consistency across large codebases","Projects where documentation accuracy is critical (e.g., security-sensitive code)"],"limitations":["Quality assessment is heuristic-based and may produce false positives/negatives","Cannot assess whether documentation accurately reflects business intent or domain logic","Flagging adds latency to documentation generation pipeline","Requires human judgment to determine if flagged documentation is actually problematic"],"requires":["Generated documentation to analyze","Quality criteria configuration (custom rules or defaults)","Human review process for flagged documentation"],"input_types":["generated documentation","code context","quality criteria"],"output_types":["quality scores","issue flags","improvement suggestions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_6","uri":"capability://code.generation.editing.interactive.code.examples.and.usage.patterns.extraction","name":"interactive code examples and usage patterns extraction","description":"Mutable automatically extracts and generates usage examples from test files, integration tests, and example code in the repository, embedding these examples directly into documentation. The system identifies test cases that demonstrate how functions or modules are intended to be used, then synthesizes these into readable examples that show both correct usage and common patterns.","intents":["I want documentation to include real usage examples without manually writing them","I need to show developers how to use APIs through concrete code examples","I want examples to stay in sync with code changes automatically"],"best_for":["Library and SDK developers wanting to document APIs through examples","Teams with comprehensive test coverage that can serve as documentation source","Open-source projects needing rich examples for contributor onboarding"],"limitations":["Example extraction quality depends on test code quality and clarity","Complex test setups or mocks may produce confusing examples","Cannot extract examples from code that lacks tests","Generated examples may be too verbose or include unnecessary setup code"],"requires":["Test files in standard locations (e.g., __tests__, test/, tests/)","Test code written in same language as source code","Reasonable test coverage of public APIs"],"input_types":["test files","example code","integration tests"],"output_types":["code examples","usage patterns","example documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_7","uri":"capability://data.processing.analysis.architecture.diagram.and.dependency.graph.generation","name":"architecture diagram and dependency graph generation","description":"Mutable analyzes code imports, module relationships, and service dependencies to automatically generate architecture diagrams and dependency graphs that visualize how components interact. The system builds a graph of module-to-module dependencies, service-to-service calls, and data flow patterns, then renders these as visual diagrams that help developers understand system structure without manual diagram maintenance.","intents":["I want to visualize how my microservices communicate without manually drawing diagrams","I need to understand module dependencies and circular dependency issues","I want architecture diagrams that stay in sync with code changes"],"best_for":["Microservices architectures with complex inter-service dependencies","Large monoliths with many modules where dependency understanding is critical","Teams wanting to document system architecture without manual diagram tools"],"limitations":["Dependency detection is based on static analysis; dynamic dependencies (reflection, plugin systems) may be missed","Diagram layout and visualization quality depends on graph complexity; very large systems may produce cluttered diagrams","Cross-repository dependencies (e.g., service A calling service B) require additional configuration","Cannot infer data flow or message passing patterns that aren't explicit in code"],"requires":["Codebase with clear import/dependency structure","For microservices: service discovery metadata or explicit service definitions","Supported languages with dependency tracking: Python, JavaScript, TypeScript, Java, Go, Rust"],"input_types":["source code","import statements","service definitions","configuration files"],"output_types":["architecture diagrams","dependency graphs","visual representations"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_8","uri":"capability://tool.use.integration.team.aware.documentation.with.role.based.access.control","name":"team-aware documentation with role-based access control","description":"Mutable supports team collaboration on documentation with role-based access control, allowing different team members to have different permissions for viewing, editing, and approving documentation. The system tracks who generated documentation, who reviewed it, and maintains an audit trail of documentation changes, enabling teams to enforce documentation quality gates and approval workflows.","intents":["I want to require documentation review before it's published to the team","I need to control who can edit documentation for sensitive code areas","I want to track who approved documentation and when"],"best_for":["Large teams with formal documentation review processes","Organizations with compliance requirements for documentation approval","Teams wanting to prevent accidental documentation changes"],"limitations":["Role-based access control adds complexity to documentation workflows","Approval workflows may slow down documentation updates","Requires integration with identity provider (SSO, LDAP, or manual user management)","Audit trail storage and querying may add latency"],"requires":["Team account with multiple users","Identity provider integration (GitHub, GitLab, Okta, or manual user management)","Defined roles and permissions for team members"],"input_types":["user roles","permissions","documentation changes"],"output_types":["access-controlled wiki","audit logs","approval workflows"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mutable__cap_9","uri":"capability://tool.use.integration.integration.with.existing.documentation.platforms.and.wikis","name":"integration with existing documentation platforms and wikis","description":"Mutable can export generated documentation to external platforms such as Confluence, GitBook, Notion, or static site generators, maintaining synchronization between the auto-generated documentation and existing team wikis. 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