# IBM watsonx MCP > IBM watsonx provides a connection to an enterprise-grade suite of AI models for running complex data operations. Use this MCP to generate text, create vector embeddings for semantic search, manage model lifecycle details, and conduct advanced prompt tuning jobs directly from your agent. ## Overview - **Category:** industry-titans - **Price:** Free ## Description You need more than just a simple chat interface; you're dealing with production-level AI work. This connection lets your agent interact with the full power of IBM watsonx, handling everything from basic text generation to deep model management. You can manage prompts, list available foundation models, and get detailed specs for any particular model version. It’s built for engineers who need control over their data pipeline; you can generate vector embeddings for similarity searches or run multi-turn chat completions that require state tracking. When working with these complex systems, Vinkius provides the centralized platform, letting you connect your preferred AI client to this entire catalog of operations. It means your agent doesn't just talk to an API; it manages the model itself—it can initiate tuning jobs or check the status of existing ones. It’s about making sure the output isn't just generated, but that it meets specific structural requirements. ## Tools ### create_prompt Allows your agent to save and organize a new prompt template within watsonx for later use. ### generate_chat Generates chat completions, making it ideal for building multi-turn conversations with the AI model. ### generate_embeddings Creates numerical vector embeddings from input text, which is necessary for semantic search and clustering tasks. ### generate_text Generates standard text content for single-turn jobs like summarization or drafting articles. ### get_model_details Retrieves specific technical specifications and metadata for a foundation model you plan to use. ### get_tuning_status Checks the current progress or status of an ongoing prompt tuning job. ### list_models Queries and provides a list of all available foundation models in your watsonx environment, including their IDs and capabilities. ### list_projects Lists the different project containers you have set up within your watsonx account. ### list_prompts Retrieves a list of all saved prompts associated with a specific watsonx project for reference. ### start_model_tuning Initiates the process of fine-tuning a foundation model by pointing it to an external dataset URL. ## Capabilities ### Run Multi-Turn Conversations Execute complex, ongoing chat applications by generating completions using a watsonx chat model. ### Prepare Data for Search Generate vector embeddings from text inputs. This process is necessary for semantic analysis and finding related data points in large knowledge bases. ### Execute Text Generation Tasks Create single-turn content, such as summarizing documents or writing initial drafts, using a watsonx foundation model. ### Manage Model Definitions List available foundation models, checking their IDs, capabilities, and current lifecycle status to select the right resource for a job. ### Initiate Prompt Tuning Start model tuning jobs using training data from cloud storage, refining a foundation model's behavior on specific tasks. ## Use Cases ### Building a Custom Q&A Bot An agent needs to build an internal knowledge bot. First, it runs `generate_embeddings` on all corporate PDFs; this creates the vector index. Then, when a user asks a question, the agent uses those embeddings to find relevant source chunks and passes them into `generate_chat` for a grounded answer. ### Automating Content Pipelines A marketing team needs weekly blog summaries. The agent calls `list_prompts` to retrieve the standard summary template, then uses `generate_text` with the raw article content to produce a polished draft. ### Model Performance Validation Before deployment, an ML engineer needs to confirm if a model can handle structured data. They call `get_model_details` to validate the capabilities and then use `list_models` to check which version is stable enough for testing. ### Fine-Tuning on Proprietary Data A financial services firm has specialized terminology. They must call `start_model_tuning`, pointing it to a secure cloud bucket of historical reports, and then monitor the progress using `get_tuning_status` until the model is ready. ## Benefits - You eliminate guesswork about available models. By using `list_models`, your agent gets a definitive list of foundation model IDs, ensuring you always select the correct resource for the job. - Complex interactions no longer fail on state. The ability to use `generate_chat` handles multi-turn conversational contexts automatically, maintaining dialogue history across multiple calls. - Search becomes semantic, not keyword-based. Calling `generate_embeddings` transforms simple text into vectors, enabling true similarity search that finds contextually related documents. - Tuning is manageable, not a black box. You can initiate advanced training using `start_model_tuning` and then track progress via `get_tuning_status`, keeping your model performance predictable. - Model selection is streamlined. Instead of guessing which API endpoint to use, you first check the specs with `get_model_details` to guarantee the model meets your required output schema. ## How It Works The bottom line is: you get direct programmatic access to the full spectrum of watsonx's operational tools, making model interaction predictable and repeatable. 1. Tell your agent which foundation models you need to interact with by calling `list_models` to see available IDs and capabilities. 2. For content creation, specify the text input and desired output using `generate_text`, or for conversational flow, use `generate_chat`. 3. When data needs searching against a corpus, first generate vector embeddings via `generate_embeddings`, then feed those vectors into your application logic. ## Frequently Asked Questions **How do I know what models are available using `list_models`?** `list_models` returns all foundation model IDs and their capabilities; this tells your agent exactly which versions it can run against. **What is the difference between `generate_text` and `generate_chat`?** 'Generate text' handles single, standalone tasks like summarization. 'Generate chat' manages conversation history, making it suitable for multi-turn dialogue where context matters. **Is tuning a model difficult? Can I check the status using `get_tuning_status`?** No; you initiate the job with `start_model_tuning`, and then your agent monitors its progress by calling `get_tuning_status`. This keeps the whole process visible. **Can I save my prompts using `create_prompt`?** Yes. Calling `create_prompt` saves a new template into the watsonx project, so you don't have to rewrite the exact prompt structure every single time. **How do I use `generate_embeddings` for similarity search or clustering?** It creates vector embeddings from your input text. You take these vectors and run them against a database to find texts that are semantically similar, even if the words aren't identical. **What information can I get about a specific model using `get_model_details`?** This tool provides detailed specifications for any foundation model. You check it to confirm things like its supported capabilities, required inputs, and optimal use cases before writing code. **What is the purpose of running `list_projects`?** It displays all the distinct watsonx projects within your account. You run this command first to confirm the correct operational scope for any model management or data task you intend to perform. **What prerequisites are needed when calling `start_model_tuning`?** You must provide a cloud storage URL pointing directly to your training data. The tuning job cannot begin until the foundation model can access and read the content at that specific link.