MCP Cloudflare DNS Server

import { APIResource } from "../../../resource.js"; import { APIPromise } from "../../../core.js"; import * as Core from "../../../core.js"; import * as PromptCachingMessagesAPI from "./messages.js"; import * as MessagesAPI from "../../messages.js"; import * as BetaAPI from "../beta.js"; import { Stream } from "../../../streaming.js"; import { PromptCachingBetaMessageStream } from "../../../lib/PromptCachingBetaMessageStream.js"; export declare class Messages extends APIResource { /** * Send a structured list of input messages with text and/or image content, and the * model will generate the next message in the conversation. * * The Messages API can be used for either single queries or stateless multi-turn * conversations. */ create(params: MessageCreateParamsNonStreaming, options?: Core.RequestOptions): APIPromise<PromptCachingBetaMessage>; create(params: MessageCreateParamsStreaming, options?: Core.RequestOptions): APIPromise<Stream<RawPromptCachingBetaMessageStreamEvent>>; create(params: MessageCreateParamsBase, options?: Core.RequestOptions): APIPromise<Stream<RawPromptCachingBetaMessageStreamEvent> | PromptCachingBetaMessage>; /** * Create a Message stream */ stream(body: MessageStreamParams, options?: Core.RequestOptions): PromptCachingBetaMessageStream; } export type MessageStreamParams = MessageCreateParamsBase; export interface PromptCachingBetaCacheControlEphemeral { type: 'ephemeral'; } export interface PromptCachingBetaImageBlockParam { source: PromptCachingBetaImageBlockParam.Source; type: 'image'; cache_control?: PromptCachingBetaCacheControlEphemeral | null; } export declare namespace PromptCachingBetaImageBlockParam { interface Source { data: string; media_type: 'image/jpeg' | 'image/png' | 'image/gif' | 'image/webp'; type: 'base64'; } } export interface PromptCachingBetaMessage { /** * Unique object identifier. * * The format and length of IDs may change over time. */ id: string; /** * Content generated by the model. * * This is an array of content blocks, each of which has a `type` that determines * its shape. * * Example: * * ```json * [{ "type": "text", "text": "Hi, I'm Claude." }] * ``` * * If the request input `messages` ended with an `assistant` turn, then the * response `content` will continue directly from that last turn. You can use this * to constrain the model's output. * * For example, if the input `messages` were: * * ```json * [ * { * "role": "user", * "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" * }, * { "role": "assistant", "content": "The best answer is (" } * ] * ``` * * Then the response `content` might be: * * ```json * [{ "type": "text", "text": "B)" }] * ``` */ content: Array<MessagesAPI.ContentBlock>; /** * The model that will complete your prompt.\n\nSee * [models](https://docs.anthropic.com/en/docs/models-overview) for additional * details and options. */ model: MessagesAPI.Model; /** * Conversational role of the generated message. * * This will always be `"assistant"`. */ role: 'assistant'; /** * The reason that we stopped. * * This may be one the following values: * * - `"end_turn"`: the model reached a natural stopping point * - `"max_tokens"`: we exceeded the requested `max_tokens` or the model's maximum * - `"stop_sequence"`: one of your provided custom `stop_sequences` was generated * - `"tool_use"`: the model invoked one or more tools * * In non-streaming mode this value is always non-null. In streaming mode, it is * null in the `message_start` event and non-null otherwise. */ stop_reason: 'end_turn' | 'max_tokens' | 'stop_sequence' | 'tool_use' | null; /** * Which custom stop sequence was generated, if any. * * This value will be a non-null string if one of your custom stop sequences was * generated. */ stop_sequence: string | null; /** * Object type. * * For Messages, this is always `"message"`. */ type: 'message'; /** * Billing and rate-limit usage. * * Anthropic's API bills and rate-limits by token counts, as tokens represent the * underlying cost to our systems. * * Under the hood, the API transforms requests into a format suitable for the * model. The model's output then goes through a parsing stage before becoming an * API response. As a result, the token counts in `usage` will not match one-to-one * with the exact visible content of an API request or response. * * For example, `output_tokens` will be non-zero, even for an empty string response * from Claude. */ usage: PromptCachingBetaUsage; } export interface PromptCachingBetaMessageParam { content: string | Array<PromptCachingBetaTextBlockParam | PromptCachingBetaImageBlockParam | PromptCachingBetaToolUseBlockParam | PromptCachingBetaToolResultBlockParam>; role: 'user' | 'assistant'; } export interface PromptCachingBetaTextBlockParam { text: string; type: 'text'; cache_control?: PromptCachingBetaCacheControlEphemeral | null; } export interface PromptCachingBetaTool { /** * [JSON schema](https://json-schema.org/) for this tool's input. * * This defines the shape of the `input` that your tool accepts and that the model * will produce. */ input_schema: PromptCachingBetaTool.InputSchema; /** * Name of the tool. * * This is how the tool will be called by the model and in tool_use blocks. */ name: string; cache_control?: PromptCachingBetaCacheControlEphemeral | null; /** * Description of what this tool does. * * Tool descriptions should be as detailed as possible. The more information that * the model has about what the tool is and how to use it, the better it will * perform. You can use natural language descriptions to reinforce important * aspects of the tool input JSON schema. */ description?: string; } export declare namespace PromptCachingBetaTool { /** * [JSON schema](https://json-schema.org/) for this tool's input. * * This defines the shape of the `input` that your tool accepts and that the model * will produce. */ interface InputSchema { type: 'object'; properties?: unknown | null; [k: string]: unknown; } } export interface PromptCachingBetaToolResultBlockParam { tool_use_id: string; type: 'tool_result'; cache_control?: PromptCachingBetaCacheControlEphemeral | null; content?: string | Array<PromptCachingBetaTextBlockParam | PromptCachingBetaImageBlockParam>; is_error?: boolean; } export interface PromptCachingBetaToolUseBlockParam { id: string; input: unknown; name: string; type: 'tool_use'; cache_control?: PromptCachingBetaCacheControlEphemeral | null; } export interface PromptCachingBetaUsage { /** * The number of input tokens used to create the cache entry. */ cache_creation_input_tokens: number | null; /** * The number of input tokens read from the cache. */ cache_read_input_tokens: number | null; /** * The number of input tokens which were used. */ input_tokens: number; /** * The number of output tokens which were used. */ output_tokens: number; } export interface RawPromptCachingBetaMessageStartEvent { message: PromptCachingBetaMessage; type: 'message_start'; } export type RawPromptCachingBetaMessageStreamEvent = RawPromptCachingBetaMessageStartEvent | MessagesAPI.RawMessageDeltaEvent | MessagesAPI.RawMessageStopEvent | MessagesAPI.RawContentBlockStartEvent | MessagesAPI.RawContentBlockDeltaEvent | MessagesAPI.RawContentBlockStopEvent; export type MessageCreateParams = MessageCreateParamsNonStreaming | MessageCreateParamsStreaming; export interface MessageCreateParamsBase { /** * Body param: The maximum number of tokens to generate before stopping. * * Note that our models may stop _before_ reaching this maximum. This parameter * only specifies the absolute maximum number of tokens to generate. * * Different models have different maximum values for this parameter. See * [models](https://docs.anthropic.com/en/docs/models-overview) for details. */ max_tokens: number; /** * Body param: Input messages. * * Our models are trained to operate on alternating `user` and `assistant` * conversational turns. When creating a new `Message`, you specify the prior * conversational turns with the `messages` parameter, and the model then generates * the next `Message` in the conversation. Consecutive `user` or `assistant` turns * in your request will be combined into a single turn. * * Each input message must be an object with a `role` and `content`. You can * specify a single `user`-role message, or you can include multiple `user` and * `assistant` messages. * * If the final message uses the `assistant` role, the response content will * continue immediately from the content in that message. This can be used to * constrain part of the model's response. * * Example with a single `user` message: * * ```json * [{ "role": "user", "content": "Hello, Claude" }] * ``` * * Example with multiple conversational turns: * * ```json * [ * { "role": "user", "content": "Hello there." }, * { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, * { "role": "user", "content": "Can you explain LLMs in plain English?" } * ] * ``` * * Example with a partially-filled response from Claude: * * ```json * [ * { * "role": "user", * "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" * }, * { "role": "assistant", "content": "The best answer is (" } * ] * ``` * * Each input message `content` may be either a single `string` or an array of * content blocks, where each block has a specific `type`. Using a `string` for * `content` is shorthand for an array of one content block of type `"text"`. The * following input messages are equivalent: * * ```json * { "role": "user", "content": "Hello, Claude" } * ``` * * ```json * { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } * ``` * * Starting with Claude 3 models, you can also send image content blocks: * * ```json * { * "role": "user", * "content": [ * { * "type": "image", * "source": { * "type": "base64", * "media_type": "image/jpeg", * "data": "/9j/4AAQSkZJRg..." * } * }, * { "type": "text", "text": "What is in this image?" } * ] * } * ``` * * We currently support the `base64` source type for images, and the `image/jpeg`, * `image/png`, `image/gif`, and `image/webp` media types. * * See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for * more input examples. * * Note that if you want to include a * [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use * the top-level `system` parameter — there is no `"system"` role for input * messages in the Messages API. */ messages: Array<PromptCachingBetaMessageParam>; /** * Body param: The model that will complete your prompt.\n\nSee * [models](https://docs.anthropic.com/en/docs/models-overview) for additional * details and options. */ model: MessagesAPI.Model; /** * Body param: An object describing metadata about the request. */ metadata?: MessagesAPI.Metadata; /** * Body param: Custom text sequences that will cause the model to stop generating. * * Our models will normally stop when they have naturally completed their turn, * which will result in a response `stop_reason` of `"end_turn"`. * * If you want the model to stop generating when it encounters custom strings of * text, you can use the `stop_sequences` parameter. If the model encounters one of * the custom sequences, the response `stop_reason` value will be `"stop_sequence"` * and the response `stop_sequence` value will contain the matched stop sequence. */ stop_sequences?: Array<string>; /** * Body param: Whether to incrementally stream the response using server-sent * events. * * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for * details. */ stream?: boolean; /** * Body param: System prompt. * * A system prompt is a way of providing context and instructions to Claude, such * as specifying a particular goal or role. See our * [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). */ system?: string | Array<PromptCachingBetaTextBlockParam>; /** * Body param: Amount of randomness injected into the response. * * Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` * for analytical / multiple choice, and closer to `1.0` for creative and * generative tasks. * * Note that even with `temperature` of `0.0`, the results will not be fully * deterministic. */ temperature?: number; /** * Body param: How the model should use the provided tools. The model can use a * specific tool, any available tool, or decide by itself. */ tool_choice?: MessagesAPI.ToolChoice; /** * Body param: Definitions of tools that the model may use. * * If you include `tools` in your API request, the model may return `tool_use` * content blocks that represent the model's use of those tools. You can then run * those tools using the tool input generated by the model and then optionally * return results back to the model using `tool_result` content blocks. * * Each tool definition includes: * * - `name`: Name of the tool. * - `description`: Optional, but strongly-recommended description of the tool. * - `input_schema`: [JSON schema](https://json-schema.org/) for the tool `input` * shape that the model will produce in `tool_use` output content blocks. * * For example, if you defined `tools` as: * * ```json * [ * { * "name": "get_stock_price", * "description": "Get the current stock price for a given ticker symbol.", * "input_schema": { * "type": "object", * "properties": { * "ticker": { * "type": "string", * "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." * } * }, * "required": ["ticker"] * } * } * ] * ``` * * And then asked the model "What's the S&P 500 at today?", the model might produce * `tool_use` content blocks in the response like this: * * ```json * [ * { * "type": "tool_use", * "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", * "name": "get_stock_price", * "input": { "ticker": "^GSPC" } * } * ] * ``` * * You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an * input, and return the following back to the model in a subsequent `user` * message: * * ```json * [ * { * "type": "tool_result", * "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", * "content": "259.75 USD" * } * ] * ``` * * Tools can be used for workflows that include running client-side tools and * functions, or more generally whenever you want the model to produce a particular * JSON structure of output. * * See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. */ tools?: Array<PromptCachingBetaTool>; /** * Body param: Only sample from the top K options for each subsequent token. * * Used to remove "long tail" low probability responses. * [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). * * Recommended for advanced use cases only. You usually only need to use * `temperature`. */ top_k?: number; /** * Body param: Use nucleus sampling. * * In nucleus sampling, we compute the cumulative distribution over all the options * for each subsequent token in decreasing probability order and cut it off once it * reaches a particular probability specified by `top_p`. You should either alter * `temperature` or `top_p`, but not both. * * Recommended for advanced use cases only. You usually only need to use * `temperature`. */ top_p?: number; /** * Header param: Optional header to specify the beta version(s) you want to use. */ betas?: Array<BetaAPI.AnthropicBeta>; } export declare namespace MessageCreateParams { /** * @deprecated use `Anthropic.Messages.Metadata` instead */ type Metadata = MessagesAPI.Metadata; /** * @deprecated use `Anthropic.Messages.ToolChoiceAuto` instead */ type ToolChoiceAuto = MessagesAPI.ToolChoiceAuto; /** * @deprecated use `Anthropic.Messages.ToolChoiceAny` instead */ type ToolChoiceAny = MessagesAPI.ToolChoiceAny; /** * @deprecated use `Anthropic.Messages.ToolChoiceTool` instead */ type ToolChoiceTool = MessagesAPI.ToolChoiceTool; type MessageCreateParamsNonStreaming = PromptCachingMessagesAPI.MessageCreateParamsNonStreaming; type MessageCreateParamsStreaming = PromptCachingMessagesAPI.MessageCreateParamsStreaming; } export interface MessageCreateParamsNonStreaming extends MessageCreateParamsBase { /** * Body param: Whether to incrementally stream the response using server-sent * events. * * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for * details. */ stream?: false; } export interface MessageCreateParamsStreaming extends MessageCreateParamsBase { /** * Body param: Whether to incrementally stream the response using server-sent * events. * * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for * details. */ stream: true; } export declare namespace Messages { export { type PromptCachingBetaCacheControlEphemeral as PromptCachingBetaCacheControlEphemeral, type PromptCachingBetaImageBlockParam as PromptCachingBetaImageBlockParam, type PromptCachingBetaMessage as PromptCachingBetaMessage, type PromptCachingBetaMessageParam as PromptCachingBetaMessageParam, type PromptCachingBetaTextBlockParam as PromptCachingBetaTextBlockParam, type PromptCachingBetaTool as PromptCachingBetaTool, type PromptCachingBetaToolResultBlockParam as PromptCachingBetaToolResultBlockParam, type PromptCachingBetaToolUseBlockParam as PromptCachingBetaToolUseBlockParam, type PromptCachingBetaUsage as PromptCachingBetaUsage, type RawPromptCachingBetaMessageStartEvent as RawPromptCachingBetaMessageStartEvent, type RawPromptCachingBetaMessageStreamEvent as RawPromptCachingBetaMessageStreamEvent, type MessageCreateParams as MessageCreateParams, type MessageCreateParamsNonStreaming as MessageCreateParamsNonStreaming, type MessageCreateParamsStreaming as MessageCreateParamsStreaming, }; } //# sourceMappingURL=messages.d.ts.map