{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-nx-mcp","slug":"nx-mcp","name":"nx-mcp","type":"mcp","url":"https://nx.dev","page_url":"https://unfragile.ai/nx-mcp","categories":["mcp-servers"],"tags":["nx","monorepo","ai","mcp","model","context","protocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-nx-mcp__cap_0","uri":"capability://tool.use.integration.monorepo.task.execution.via.mcp.protocol","name":"monorepo task execution via mcp protocol","description":"Exposes Nx task execution (build, test, lint, serve) as MCP tools that AI clients can invoke directly. Implements the Model Context Protocol server specification to translate natural language task requests into Nx CLI commands, handling task graph resolution, dependency ordering, and parallel execution configuration. Routes execution through Nx's task scheduler rather than shelling out, enabling real-time progress streaming and structured result parsing.","intents":["I want my AI agent to run tests or builds in my Nx monorepo without needing shell access","I need to let Claude or another LLM trigger specific Nx tasks as part of an agentic workflow","I want to expose my monorepo's build system to an AI tool without exposing raw CLI access"],"best_for":["teams using Nx monorepos with AI-powered development tools","developers building LLM agents that need to interact with monorepo build systems","organizations integrating Nx with Claude, ChatGPT, or other MCP-compatible AI clients"],"limitations":["Requires Nx 18+ for full MCP compatibility; older versions may have limited task introspection","Task execution is synchronous from the MCP server perspective — long-running tasks may timeout if client doesn't support streaming","No built-in caching of task results across MCP sessions; each invocation re-executes unless Nx's local cache is warm","Limited to tasks defined in nx.json and project.json files; dynamic or plugin-generated tasks may not be discoverable"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Valid Nx workspace with nx.json configuration"],"input_types":["task name (string)","project name (string)","configuration flags (key-value pairs)","target name (string)"],"output_types":["task execution result (JSON with exit code, stdout, stderr)","structured task metadata (available tasks, dependencies)","real-time execution logs (if streaming supported)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_1","uri":"capability://memory.knowledge.workspace.project.and.target.discovery.via.mcp","name":"workspace project and target discovery via mcp","description":"Provides MCP tools that introspect the Nx workspace to enumerate all projects, their targets (build, test, lint, etc.), dependencies, and configuration. Parses nx.json, project.json files, and plugin metadata to build a queryable index of available tasks and their parameters. Returns structured metadata (project graph, target configurations, affected projects) that AI clients can use to understand workspace structure without manual exploration.","intents":["I want my AI agent to understand what projects and tasks exist in my monorepo before deciding what to run","I need to query which projects are affected by a code change so the AI can run only relevant tests","I want to expose my workspace structure to an LLM so it can make informed decisions about which targets to invoke"],"best_for":["AI agents that need to reason about monorepo structure before taking actions","developers building intelligent CI/CD workflows with LLMs","teams using Nx plugins and wanting AI to discover plugin-provided targets"],"limitations":["Project graph computation can be expensive in very large monorepos (1000+ projects); caching is recommended","Plugin-provided targets may not be discoverable until plugins are loaded, adding latency to discovery","Configuration overrides in nx.json may not be fully reflected in the discovery output if using advanced features like dynamic imports","No real-time updates if workspace configuration changes during an MCP session; requires reconnection to refresh"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Valid nx.json and project.json files in workspace root"],"input_types":["project name filter (optional string)","target name filter (optional string)","include-dependencies flag (boolean)"],"output_types":["project list (JSON array with metadata)","target configurations (JSON with parameters, outputs, cache settings)","project dependency graph (adjacency structure)","affected projects list (based on file changes)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_10","uri":"capability://search.retrieval.git.integration.and.change.tracking","name":"git integration and change tracking","description":"Provides MCP tools for git operations within the Nx workspace context, including file change detection, commit history analysis, and branch management. Integrates with Nx's affected detection to correlate git changes with project impacts. Enables AI clients to understand code history and make informed decisions about which projects to rebuild or test.","intents":["I want my AI to understand what files have changed since the last commit","I need an LLM to analyze commit history to understand code evolution","I want to expose git operations to Claude so it can make informed decisions about affected projects"],"best_for":["developers using AI for change analysis and impact assessment","teams automating CI/CD workflows with AI decision-making","organizations using AI to analyze code history and suggest refactoring"],"limitations":["Git operations require git to be installed and available in PATH","Large commit histories may be slow to analyze; consider limiting history depth","Shallow clones may not have complete history; full clone recommended for accurate analysis","Git operations are read-only; write operations (commits, pushes) are not supported"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Git installed and available in PATH","Git repository with commit history"],"input_types":["base branch (string, e.g., 'main')","head branch (string, e.g., 'feature/my-change')","file path filter (optional regex)"],"output_types":["changed files list (JSON array with paths)","commit history (JSON array with commit metadata)","branch information (current branch, tracking branch)","diff summary (additions, deletions, modifications)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_2","uri":"capability://planning.reasoning.affected.project.detection.for.code.changes","name":"affected project detection for code changes","description":"Implements Nx's affected command as an MCP tool, analyzing file changes (via git diff or provided file list) to determine which projects in the monorepo are impacted. Uses Nx's dependency graph and file-to-project mapping to compute the minimal set of projects that need re-testing or rebuilding. Returns structured output (affected projects, their targets, and change scope) that AI agents can use to optimize CI/CD workflows.","intents":["I want my AI to know which tests to run based on what files I changed","I need to identify the blast radius of a code change across my monorepo","I want an LLM to automatically run only the affected projects' targets instead of the entire test suite"],"best_for":["teams building intelligent CI/CD pipelines with AI decision-making","developers using AI agents for code review and impact analysis","organizations optimizing test execution time in large monorepos"],"limitations":["Requires git history to be available; works only with git-based repositories","Accuracy depends on correct dependency declarations in nx.json; missing or incorrect dependencies will produce incomplete results","Computing affected projects for very large changesets (1000+ files) can be slow; consider batching or caching","Does not account for implicit dependencies (e.g., shared environment variables or global configuration changes)"],"requires":["Nx 18.0.0 or higher","Git repository with commit history","Node.js 18+ runtime"],"input_types":["base branch (string, e.g., 'main')","head branch (string, e.g., 'feature/my-change')","file list (optional array of file paths)"],"output_types":["affected projects list (JSON array)","affected targets (e.g., test, build, lint)","change scope (files, packages, libraries affected)","dependency chain (which projects depend on changed projects)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_3","uri":"capability://code.generation.editing.code.generation.and.scaffolding.via.nx.generators","name":"code generation and scaffolding via nx generators","description":"Exposes Nx generators (schematics-based code generation) as MCP tools, allowing AI clients to invoke generators for creating components, services, libraries, and other boilerplate. Parses generator schemas to expose configurable options as MCP tool parameters, handles generator execution with proper file I/O and git integration, and returns structured output (generated files, paths, next steps). Supports both built-in Nx generators and custom workspace generators.","intents":["I want my AI to generate a new Angular component with the correct folder structure and imports","I need an LLM to scaffold a new library in my monorepo following my workspace conventions","I want to expose my custom generators to Claude so it can use them as part of code generation workflows"],"best_for":["teams with custom Nx generators wanting AI-assisted code scaffolding","developers using AI agents for rapid prototyping and boilerplate generation","organizations standardizing code structure across large monorepos"],"limitations":["Generator execution is synchronous; interactive generators (with prompts) are not supported","Custom generators must be properly typed and exported; undocumented or poorly-structured generators may not expose parameters correctly","File conflicts are not automatically resolved; generators that would overwrite existing files will fail unless --force flag is used","Generator output is limited to file creation/modification; generators with side effects (e.g., npm install) may not complete as expected"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Generators must be defined in workspace or installed as npm packages"],"input_types":["generator name (string)","generator options (key-value pairs matching generator schema)","project name (string, for scoped generation)"],"output_types":["generated files list (JSON array with paths)","file contents (code snippets for preview)","next steps (suggested follow-up commands or edits)","execution summary (success/failure, warnings)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_4","uri":"capability://memory.knowledge.dependency.graph.visualization.and.analysis","name":"dependency graph visualization and analysis","description":"Provides MCP tools to query and analyze Nx's project dependency graph, including transitive dependencies, circular dependency detection, and dependency path analysis. Returns graph data in structured formats (adjacency lists, edge lists) suitable for visualization or algorithmic analysis. Enables AI agents to understand project relationships, identify tightly-coupled modules, and suggest refactoring opportunities.","intents":["I want my AI to understand the dependency structure of my monorepo to make informed refactoring suggestions","I need to detect circular dependencies and have an LLM suggest how to break them","I want to query the shortest dependency path between two projects for impact analysis"],"best_for":["architects and senior developers using AI for codebase analysis","teams refactoring large monorepos with AI-assisted guidance","developers building visualization tools on top of Nx workspace data"],"limitations":["Graph computation can be expensive in very large monorepos; consider caching results","Circular dependency detection is O(V+E) but may timeout on graphs with 1000+ projects","Does not account for runtime dependencies or dynamic imports; only static declarations are analyzed","Graph data is point-in-time; changes to dependencies require re-computation"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime"],"input_types":["project name (string, optional for full graph)","include-external flag (boolean, to include npm dependencies)","depth limit (integer, for limiting transitive dependencies)"],"output_types":["dependency graph (JSON adjacency list or edge list)","circular dependencies (array of cycles)","dependency paths (array of edges between two projects)","graph statistics (number of projects, edges, density)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_5","uri":"capability://safety.moderation.lint.and.code.quality.rule.execution.via.mcp","name":"lint and code quality rule execution via mcp","description":"Exposes Nx lint targets (ESLint, TSLint, custom linters) as MCP tools, allowing AI clients to run linting rules and retrieve structured violation reports. Parses linter output (JSON format) to provide machine-readable results including file paths, line numbers, rule names, and suggested fixes. Integrates with Nx's caching to avoid re-linting unchanged files, and supports auto-fix capabilities where available.","intents":["I want my AI to run linting checks on code before suggesting changes","I need an LLM to identify code quality issues and suggest fixes based on lint rules","I want to expose my linting rules to Claude so it can validate generated code"],"best_for":["developers using AI for code review and quality assurance","teams enforcing code standards with AI-assisted validation","organizations integrating linting into AI-powered code generation workflows"],"limitations":["Linting performance depends on project size; large projects may take 10+ seconds per lint run","Auto-fix is not available for all rules; some violations require manual intervention","Linter configuration must be present in workspace (eslintrc, tsconfig, etc.); missing config will cause failures","Caching is only effective if files haven't changed; cache misses result in full re-linting"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Linter configuration files (eslintrc, tsconfig, etc.)"],"input_types":["project name (string)","target name (string, e.g., 'lint')","fix flag (boolean, to auto-fix violations)"],"output_types":["lint violations (JSON array with file, line, rule, message)","auto-fix results (files modified, fixes applied)","summary (total violations, errors, warnings)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_6","uri":"capability://automation.workflow.test.execution.and.result.reporting.via.mcp","name":"test execution and result reporting via mcp","description":"Exposes Nx test targets (Jest, Vitest, Cypress, etc.) as MCP tools, enabling AI clients to run tests and retrieve structured results. Parses test output (JSON format) to provide machine-readable results including test names, pass/fail status, execution time, and error messages. Integrates with Nx's caching to skip re-running passing tests, and supports filtering by test name or file path.","intents":["I want my AI to run tests on generated code to verify correctness","I need an LLM to understand test failures and suggest fixes","I want to expose my test suite to Claude so it can validate changes before committing"],"best_for":["developers using AI for test-driven development and validation","teams automating test execution in AI-powered workflows","organizations using AI to debug failing tests"],"limitations":["Test execution can be slow for large test suites (100+ tests may take 30+ seconds)","Caching is only effective for deterministic tests; flaky tests may produce inconsistent results","Test output parsing depends on specific test framework output format; custom formatters may not be supported","Parallel test execution may produce non-deterministic ordering in results"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Test framework configured (Jest, Vitest, Cypress, etc.)"],"input_types":["project name (string)","test name filter (optional regex)","watch mode flag (boolean)"],"output_types":["test results (JSON with test names, status, duration)","failure details (error messages, stack traces)","coverage report (optional, if configured)","execution summary (passed, failed, skipped counts)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_7","uri":"capability://automation.workflow.build.artifact.generation.and.output.handling","name":"build artifact generation and output handling","description":"Exposes Nx build targets as MCP tools, enabling AI clients to trigger builds and retrieve information about generated artifacts. Parses build output to provide structured metadata (output paths, bundle sizes, asset lists, build time). Integrates with Nx's caching to skip rebuilds of unchanged code, and supports multiple build configurations (dev, prod, etc.).","intents":["I want my AI to build my project and verify that the build succeeds","I need an LLM to understand build artifacts and their sizes for optimization suggestions","I want to expose my build system to Claude so it can validate changes produce valid artifacts"],"best_for":["developers using AI for build validation and optimization","teams automating build processes in AI-powered workflows","organizations using AI to analyze build artifacts and suggest optimizations"],"limitations":["Build execution can be slow for large projects (10+ seconds for medium projects)","Caching is only effective if source files haven't changed; cache misses result in full rebuilds","Build output parsing depends on specific build tool output format; custom builders may not be supported","Artifact paths are relative to workspace root; absolute paths require additional context"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Build tool configured (webpack, esbuild, tsc, etc.)"],"input_types":["project name (string)","configuration (string, e.g., 'production')","output path (optional override)"],"output_types":["build status (success/failure)","artifact paths (JSON array with file paths)","bundle sizes (JSON with size metrics)","build time (execution duration)","warnings/errors (build diagnostics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_8","uri":"capability://automation.workflow.workspace.configuration.and.settings.management","name":"workspace configuration and settings management","description":"Provides MCP tools to read and modify Nx workspace configuration (nx.json, project.json files). Enables AI clients to query current settings, update task configurations, modify project metadata, and manage workspace-level settings. Handles JSON parsing and validation, and supports atomic updates with rollback capability.","intents":["I want my AI to understand and modify my Nx workspace configuration","I need an LLM to update task configurations based on analysis or user requests","I want to expose configuration management to Claude so it can optimize workspace settings"],"best_for":["developers using AI for workspace optimization and configuration management","teams automating configuration updates in AI-powered workflows","organizations using AI to analyze and suggest configuration improvements"],"limitations":["Configuration changes are immediate; no preview or dry-run capability","Rollback requires manual intervention or git history; no built-in undo","Complex configuration inheritance may not be fully understood by AI; manual review recommended","Configuration validation is limited to JSON schema; semantic validation requires manual review"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","Write access to workspace configuration files"],"input_types":["configuration key (string path, e.g., 'tasksRunnerOptions.default')","configuration value (JSON value)","project name (optional, for project-specific config)"],"output_types":["current configuration (JSON)","configuration schema (JSON schema for validation)","update result (success/failure, changes made)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-nx-mcp__cap_9","uri":"capability://tool.use.integration.plugin.and.extension.discovery.and.management","name":"plugin and extension discovery and management","description":"Exposes Nx's plugin system as MCP tools, allowing AI clients to discover installed plugins, their capabilities, and available generators/executors. Queries plugin metadata to provide information about plugin-provided targets, generators, and configuration options. Supports plugin installation and configuration through MCP.","intents":["I want my AI to understand what Nx plugins are installed and what they provide","I need an LLM to discover available generators from installed plugins","I want to expose plugin management to Claude so it can suggest or install plugins"],"best_for":["developers using AI for plugin discovery and management","teams automating plugin installation in AI-powered workflows","organizations using AI to suggest plugins based on workspace needs"],"limitations":["Plugin discovery depends on plugins being properly registered in nx.json","Plugin metadata may not be complete if plugins don't follow Nx conventions","Plugin installation requires npm/yarn access and may have network dependencies","Plugin compatibility is not automatically verified; manual testing recommended"],"requires":["Nx 18.0.0 or higher","Node.js 18+ runtime","npm or yarn for plugin installation"],"input_types":["plugin name (string)","plugin version (optional, for installation)"],"output_types":["installed plugins list (JSON array with metadata)","plugin capabilities (generators, executors, targets)","plugin configuration schema (JSON schema)","installation result (success/failure)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Nx 18.0.0 or higher","Node.js 18+ runtime","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Valid Nx workspace with nx.json configuration","Valid nx.json and project.json files in workspace root","Git installed and available in PATH","Git repository with commit history","Generators must be defined in workspace or installed as npm packages","Linter configuration files (eslintrc, tsconfig, etc.)","Test framework configured (Jest, Vitest, Cypress, etc.)"],"failure_modes":["Requires Nx 18+ for full MCP compatibility; older versions may have limited task introspection","Task execution is synchronous from the MCP server perspective — long-running tasks may timeout if client doesn't support streaming","No built-in caching of task results across MCP sessions; each invocation re-executes unless Nx's local cache is warm","Limited to tasks defined in nx.json and project.json files; dynamic or plugin-generated tasks may not be discoverable","Project graph computation can be expensive in very large monorepos (1000+ projects); caching is recommended","Plugin-provided targets may not be discoverable until plugins are loaded, adding latency to discovery","Configuration overrides in nx.json may not be fully reflected in the discovery output if using advanced features like dynamic imports","No real-time updates if workspace configuration changes during an MCP session; requires reconnection to refresh","Git operations require git to be installed and available in PATH","Large commit histories may be slow to analyze; consider limiting history depth","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6225973703819999,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":64857,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nx-mcp","compare_url":"https://unfragile.ai/compare?artifact=nx-mcp"}},"signature":"RA//43gvkrLgtgyxOorEj1ofDlNKjQs5OIrhKgEFoP9PXh0I0z4iigZnaHx6hXjSKAzQpE9KwPKENT6oDkHLBQ==","signedAt":"2026-06-22T00:16:47.769Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nx-mcp","artifact":"https://unfragile.ai/nx-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=nx-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}