# Anthropic MCP for AI Agents AI Agent Connect > Anthropic MCP lets you connect your Anthropic account to any AI agent to send messages to Claude models, count tokens, and manage batches. It turns your agent into a full LLM orchestration layer, allowing you to handle model discovery, cost estimation, and asynchronous request processing without writing any custom request code. Use it to manage your Claude workflows directly from your favorite AI client. ## Overview - **Category:** ai-frontier - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_olS3uDDLdOw9uiZoTqVqVIoK0UcivzobkVz8mrNA/ai-agent-connect - **Tags:** llm, model-discovery, token-counting, natural-language-processing, prompt-engineering, api-management ## Description This Connector lets you put Anthropic's Claude models directly into your agent's reach. Instead of jumping between different developer tools and dashboards, you can just talk to your agent to get things done. It handles the heavy lifting of model discovery, so you can see exactly what's available at any time. You can send messages with specific settings like temperature or system prompts, and it handles the response for you. If you're worried about costs, it lets you check your token counts before you hit send. For the bigger jobs, it supports batch processing, letting you queue up independent requests and check their status later. This makes it a lot easier to manage multiple prompts without worrying about the underlying request code. By using this Connector through the Vinkius catalog, you get a clean way to manage your LLM orchestration without the usual friction of manual request management. ## Tools ### get_batch_message Check the progress and results of a submitted batch to see what succeeded or failed. Use this to track your long-running jobs. ### list_models See all available Claude models, their IDs, and their specific capabilities in one list. This helps you find the right model for your specific task. ### send_message Send a direct message to a Claude model with custom system prompts, temperature, and max tokens. This is the primary way to get a response from Claude. ### cancel_batch_message Stop an active batch of requests to save on costs if you realize a mistake was made. This is useful for halting large jobs immediately. ### count_tokens Get the exact input token count for a message to stay within context limits and manage your budget. It helps you estimate costs before you hit send. ### create_batch_message Queue up multiple independent prompts for cost-effective, asynchronous processing by Claude. This is the best way to handle many requests at once. ## Prompt Examples **Prompt:** ``` What models can I use right now? ``` **Response:** ``` Here are the available Claude models you can use: | Model ID | Capabilities | | :--- | :--- | | `claude-sonnet-4-20250514` | Latest Sonnet, 200K context | | `claude-opus-4-20250514` | Most capable, 200K context | | `claude-haiku-20241022` | Fastest, cost-effective, 200K context | Each model has different pricing and capabilities. ``` **Prompt:** ``` How many tokens are in this summary request? ``` **Response:** ``` The message you want to send would use approximately **547 input tokens**. With a typical response of 150 tokens, the total for this conversation turn would be around **697 tokens**. ``` **Prompt:** ``` Send a message to Claude to summarize this text. ``` **Response:** ``` I've sent that to Claude. Here is the summary: **Summary:** The article discusses the history of Brasília as the capital of Brazil, highlighting its purpose-built nature to replace Rio de Janeiro in 1960. * **Location:** Central-west region * **Key Date:** 1960 * **Previous Capital:** Rio de Janeiro ``` ## Capabilities ### Send messages to Claude models Talk to Claude models directly through your agent using custom prompts and parameters. ### Count tokens for cost estimation Check your token count before sending to stay within limits and manage your budget. ### Create batches for many prompts Queue up multiple independent prompts for cost-effective, asynchronous processing. ### Check status of batch requests Track the progress and results of your submitted batch jobs in real time. ### See all available Claude models Get a full list of available Claude models, their IDs, and their specific capabilities. ### Stop active batch processing Cancel ongoing batch requests immediately to save on costs if you spot an error. ## Use Cases ### Batching Prompt Evaluations An ML engineer uses `create_batch_message` to run 500 different prompts across multiple models to compare accuracy scores for a new release. ### Cost-Conscious App Development A developer uses `count_tokens` to ensure a user's long document fits within the Claude context window before attempting to summarize it for a customer. ### Model Exploration A product team uses `list_models` to find the right balance between speed and reasoning for a new customer service bot they are building. ### Real-time Chat Integration A developer uses `send_message` to build a custom chatbot that follows a specific system prompt for a company's internal HR help site. ## Benefits - Skip the request boilerplate by using `send_message` to talk to Claude directly from your agent. This removes the need to write custom request code for every single interaction you want to have with the model. - Track your spending accurately with `count_tokens` to see exactly how much a prompt will cost before you run it. This helps you stay under budget and avoid unexpected costs when processing large amounts of text. - Handle massive workloads efficiently by using `create_batch_message` for asynchronous request processing. This is the best way to run hundreds of independent prompts without waiting for each one to finish individually. - Stay updated on the latest options with `list_models` to see every available Claude model ID and capability. This makes it easy to switch between models as new ones are released by the provider. - Save money on mistakes by using `cancel_batch_message` to halt ongoing requests that you no longer need to process. If you spot an error in a large batch, you can stop the remaining tasks to save on costs. ## How It Works The bottom line is you get a direct line to Claude's models without the overhead of manual request management. 1. Subscribe to the Connector and enter your Anthropic API Key. 2. Connect your preferred AI client like Cursor or Windsurf. 3. Start sending messages and managing batches directly through your agent. ## Frequently Asked Questions **What is the Anthropic MCP?** It's a way to connect Claude models directly to your AI agent. It lets you send messages, count tokens, and manage batches without having to write your own API code. **Can I use the Anthropic MCP in Cursor?** Yes, you can connect it to Cursor or any other MCP-compatible client. This lets you use Claude's capabilities directly within your coding environment. **How do I manage costs with the Anthropic MCP?** You can use the token counting feature to see exactly how much a prompt will cost before you send it. You can also use the batch tools to run many prompts more efficiently. **Can I run many prompts at once with this?** Yes, that's one of its main features. You can create batches of independent prompts and have your agent process them asynchronously for you. **How do I see which Claude models are available?** You can just ask your agent to list the models. It will show you all the available IDs and what each model is capable of doing. **Can I stop a batch if I make a mistake?** Yes, you can use the batch cancellation tool to stop an active job immediately. This is helpful if you notice an error in a large batch and want to save on costs. **How do I get an Anthropic API Key?** Log in to the [**Anthropic Console**](https://console.anthropic.com), go to **Account Settings > API Keys** and click **Create Key**. Copy the key immediately — it starts with `sk-ant-` and won't be shown again. You can also create workspace-scoped keys to control spending by use case. **What models are available?** Use the `list_models` tool to see all available Claude models. Current models include Claude Sonnet 4, Claude Opus 4 and Claude Haiku variants, each with different capabilities, context windows and pricing. The model ID format is like `claude-sonnet-4-20250514`. **Can I send multi-turn conversations?** Yes! Pass a messages array with alternating 'user' and 'assistant' roles. Each message has a 'role' and 'content' field. Claude will continue the conversation based on the full message history. Example: [{"role":"user","content":"Hello"},{"role":"assistant","content":"Hi!"},{"role":"user","content":"What's 2+2?"}]. **How does batch processing work?** Use `create_batch_message` with an array of independent message requests. Each request is processed asynchronously and costs 50% less than individual requests. Use `get_batch_message` to check progress and results. Batches are ideal when you have many unrelated prompts to process.