Build your project for production, auto-detecting the framework from package.json. Use before deploying to ensure the project compiles correctly.
MIT
Destructive
649,985 tools. Updated 2026-10-11 15:26
"Guide to Deploying a Large Language Model Framework" matching MCP tools:
- Read this guide to learn Posecode language grammar, joints, and actions before writing a movement.Apache 2.0
- Detect programming language and test framework from source code. Input either raw code or file path.MIT
- Pre-download a Whisper model for audio transcription. Choose a model size—tiny, base, small, medium, or large-v3—to balance speed and accuracy.Apache 2.0
- Pre-download a Whisper model to select the desired accuracy and speed for upcoming transcription tasks, with sizes from tiny to large-v3.Apache 2.0
- Retrieve a topical context guide with partial topic matching. Large guides return a chunked summary; specify a section slug for detailed content or 'all' for the full guide.MIT
Matching MCP Servers
- AlicenseBqualityDmaintenanceTurn natural language into 3D models in Fusion 360. 64 CAD tools including sketches, extrudes, fillets,and JIS standard parts.655MIT
- FlicenseCqualityCmaintenanceMCP server that fetches API responses and generates ready-to-use model/POJO classes in your configured language, with built-in login/refresh token handling.3-
Matching MCP Connectors
Verified, version-pinned answers about fast-moving frameworks for coding agents.
Unofficial MCP server for the Ionic Framework documentation, components and blog.
- Retrieve configured Large Language Models on Polarion to verify AI availability and select a valid model for generation. Paginated; empty result signals no LLM configured.MIT
- Delete a Cognigy.AI large language model configuration. Features using this model stop working. Use dryRun parameter to validate before deletion.MITDestructive
- Download model artifacts from Kaggle to a work directory by specifying a model version (owner/model/framework/variation/version).MIT
- Returns a testing template tailored to the source file's framework, including baseline scenarios and test file path patterns. Falls back to language baseline if framework is unrecognized.MIT
- Get current project information—name, language, framework, root directory, and Git status—to establish a clear working context.MIT
- Convert lists of texts into embedding vectors for similarity comparisons, duplicate detection, or topic-based sorting without invoking a large language model.MIT
- Retrieve all available large language model providers to see which models are supported for your AI workflows.MIT
- Test the connection to a Cognigy.AI large language model provider to validate credentials and reachability before using it in flows.MIT
- Lists available MCP server templates with name, language, framework, and entry point details to help you select a starting point for scaffolding new servers.MIT
- Provides structured scaffolding for a SCENARIO PLAN, specifying language, framework, depth, adversarial count, baseline scenarios, and test file path.MIT
- Lists large language model configurations for Cognigy.AI generative AI features like Knowledge AI and AI Agents, showing provider, model type, and connection info, with pagination and filtering options.MIT
- Retrieves detailed configuration of a specific large language model, including provider settings, model type, connection details, and fallback configuration.MIT
- Retrieve a chapter from The Swift Programming Language book by slug, covering Language Guide and Reference. Search with wwdc_search first to find the slug.MIT
- Get framework-specific setup instructions for adding Fumadocs to existing projects using Next.js, React Router, TanStack Start, or Waku.MIT
- Scan all projects in your workspace to find outdated dependencies. Auto-detects language and framework, with filters for targeted checks.MIT