import { O as OpenAIChatTool } from './types-ESeMOj94.js';

/**
 *
 * OpenAI Chat Completions `tools[]` projection of the queue-bound agent-runtime
 * MCP delegation tools.
 *
 * Use when configuring `createOpenAICompatibleBackend({ tools: ... })` so the
 * model can call `delegate_feedback`, `delegation_status`, and
 * `delegation_history` through the OpenAI-compat transport (tcloud, OpenRouter,
 * OpenAI direct, cli-bridge). The runtime surfaces tool calls as `tool_call`
 * stream events — execution is the caller's responsibility (typically the
 * parent sandbox runtime's MCP mount).
 *
 * Sandbox-SDK callers do NOT need this helper: the sandbox runtime mounts
 * MCP servers natively and the in-sandbox harness discovers tools via the
 * runtime, not via an OpenAI tools array.
 *
 * Tool name + description + JSON-schema are pulled from the canonical
 * `DELEGATE_*` constants exported by `./tools/*` so the projection cannot
 * drift from the server's own validators.
 *
 * @experimental
 */

/**
 *
 * Returns the queue-bound delegation tools projected into OpenAI Chat
 * Completions `tools[]` shape. The order is stable: `delegate_feedback`,
 * `delegation_status`, `delegation_history`.
 *
 * @experimental
 */
declare function mcpToolsForRuntimeMcp(): OpenAIChatTool[];
/**
 *
 * Subset filter — return only the projected tools whose `function.name`
 * appears in `names`. Useful for curated mounts (e.g. only the queue-bound
 * delegation tools, omitting `delegate_feedback`). Unknown names are
 * silently ignored; pass an empty array to get an empty result.
 *
 * @experimental
 */
declare function mcpToolsForRuntimeMcpSubset(names: ReadonlyArray<string>): OpenAIChatTool[];

export { mcpToolsForRuntimeMcpSubset as a, mcpToolsForRuntimeMcp as m };
