@mondaydotcomorg/atp-runtime/src/metadata/generated.ts

Runtime SDK injected into sandbox for Agent Tool Protocol

/**
 * AUTO-GENERATED - DO NOT EDIT
 * Generated by scripts/generate-metadata.ts
 * 
 * Hybrid approach:
 * - ts-json-schema-generator (OSS) for most types
 * - ts-morph fallback for types OSS can't handle (generics)
 */

import type { RuntimeAPIMetadata } from './types';

export const GENERATED_METADATA: RuntimeAPIMetadata[] = [
  {
    "name": "approval",
    "description": "Approval API - Request explicit human approval for sensitive operations",
    "methods": [
      {
        "name": "request",
        "description": "Request approval from a human",
        "params": [
          {
            "name": "message",
            "type": "string",
            "description": "The message to display to the user",
            "optional": false
          },
          {
            "name": "context",
            "type": "Record<string, unknown>",
            "description": "Optional context information about what needs approval",
            "optional": true
          }
        ],
        "returns": "Promise<ApprovalResponse>"
      }
    ]
  },
  {
    "name": "cache",
    "description": "Cache API - Store and retrieve data with optional TTL",
    "methods": [
      {
        "name": "get",
        "description": "Get a value from cache by key",
        "params": [
          {
            "name": "key",
            "type": "string",
            "description": "Cache key",
            "optional": false
          }
        ],
        "returns": "Promise<T | null>"
      },
      {
        "name": "set",
        "description": "Set a value in cache with optional TTL",
        "params": [
          {
            "name": "key",
            "type": "string",
            "description": "Cache key",
            "optional": false
          },
          {
            "name": "value",
            "type": "unknown",
            "description": "Value to cache",
            "optional": false
          },
          {
            "name": "ttl",
            "type": "number",
            "description": "Time to live in seconds",
            "optional": true
          }
        ],
        "returns": "Promise<void>"
      },
      {
        "name": "delete",
        "description": "Delete a value from cache",
        "params": [
          {
            "name": "key",
            "type": "string",
            "description": "Cache key to delete",
            "optional": false
          }
        ],
        "returns": "Promise<void>"
      },
      {
        "name": "has",
        "description": "Check if a key exists in cache",
        "params": [
          {
            "name": "key",
            "type": "string",
            "description": "Cache key to check",
            "optional": false
          }
        ],
        "returns": "Promise<boolean>"
      },
      {
        "name": "clear",
        "description": "Clear all cache entries",
        "params": [],
        "returns": "Promise<void>"
      }
    ]
  },
  {
    "name": "embedding",
    "description": "Embedding API - Client-side embedding with server-side vector storage",
    "methods": [
      {
        "name": "embed",
        "description": "Request client to generate and store embeddings",
        "params": [
          {
            "name": "input",
            "type": "string | string[]",
            "description": "Text(s) to embed",
            "optional": false
          },
          {
            "name": "metadata",
            "type": "Record<string, unknown>",
            "description": "Optional metadata to store with embeddings",
            "optional": true
          }
        ],
        "returns": "Promise<string | string[]>"
      },
      {
        "name": "search",
        "description": "Search stored embeddings by similarity",
        "params": [
          {
            "name": "query",
            "type": "string",
            "description": "Search query text (will be embedded by client)",
            "optional": false
          },
          {
            "name": "options",
            "type": "Omit<SearchOptions, 'query'>",
            "description": "Search options (topK, minSimilarity, filter)",
            "optional": true
          }
        ],
        "returns": "Promise<SearchResult[]>"
      },
      {
        "name": "similarity",
        "description": "Calculate cosine similarity between two embedding vectors",
        "params": [
          {
            "name": "embedding1",
            "type": "number[]",
            "description": "First embedding vector",
            "optional": false
          },
          {
            "name": "embedding2",
            "type": "number[]",
            "description": "Second embedding vector",
            "optional": false
          }
        ],
        "returns": "number"
      },
      {
        "name": "getAll",
        "description": "Get all stored embeddings",
        "params": [],
        "returns": "EmbeddingRecord[]"
      },
      {
        "name": "count",
        "description": "Get count of stored embeddings",
        "params": [],
        "returns": "number"
      }
    ]
  },
  {
    "name": "llm",
    "description": "LLM API - Large Language Model calls using client-provided LLM (requires client.provideLLM())",
    "methods": [
      {
        "name": "call",
        "description": "Make an LLM call with a prompt",
        "params": [
          {
            "name": "options",
            "type": "LLMCallOptions",
            "description": "LLM call options including prompt",
            "optional": false
          }
        ],
        "returns": "Promise<string>"
      },
      {
        "name": "extract",
        "description": "Extract structured data from text using an LLM",
        "params": [
          {
            "name": "options",
            "type": "LLMExtractOptions",
            "description": "Extraction options with JSON schema",
            "optional": false
          }
        ],
        "returns": "Promise<T>"
      },
      {
        "name": "classify",
        "description": "Classify text into one of the provided categories",
        "params": [
          {
            "name": "options",
            "type": "LLMClassifyOptions",
            "description": "Classification options with categories",
            "optional": false
          }
        ],
        "returns": "Promise<string>"
      }
    ]
  },
  {
    "name": "progress",
    "description": "Progress API - Report execution progress to clients",
    "methods": [
      {
        "name": "report",
        "description": "Report progress with message and completion fraction",
        "params": [
          {
            "name": "message",
            "type": "string",
            "description": "Progress message",
            "optional": false
          },
          {
            "name": "fraction",
            "type": "number",
            "description": "Completion fraction (0-1)",
            "optional": false
          }
        ],
        "returns": "void"
      }
    ]
  }
];

/**
 * Runtime API names - specific literal types for type-safe API filtering
 */
export type RuntimeAPIName = 'approval' | 'cache' | 'embedding' | 'llm' | 'progress';

/**
 * Type definitions extracted using ts-json-schema-generator
 */
export const TYPE_REGISTRY = [
  {
    "name": "ApprovalResponse",
    "definition": "export interface ApprovalResponse<T = unknown> {\n\tapproved: boolean;\n\tresponse?: T;\n\ttimestamp: number;\n}"
  },
  {
    "name": "SearchOptions",
    "definition": "interface SearchOptions {\n  query: string;\n  topK?: number;\n  minSimilarity?: number;\n  filter?: Record<string, unknown>;\n}"
  },
  {
    "name": "SearchResult",
    "definition": "interface SearchResult {\n  id: string;\n  text: string;\n  similarity: number;\n  metadata?: Record<string, unknown>;\n}"
  },
  {
    "name": "EmbeddingRecord",
    "definition": "interface EmbeddingRecord {\n  id: string;\n  text: string;\n  embedding: number[];\n  metadata?: Record<string, unknown>;\n}"
  },
  {
    "name": "LLMCallOptions",
    "definition": "interface LLMCallOptions {\n  prompt: string;\n  context?: Record<string, unknown>;\n  model?: string;\n  maxTokens?: number;\n  temperature?: number;\n  systemPrompt?: string;\n}"
  },
  {
    "name": "LLMExtractOptions",
    "definition": "interface LLMExtractOptions {\n  prompt: string;\n  context?: Record<string, unknown>;\n  schema: unknown;\n}"
  },
  {
    "name": "LLMClassifyOptions",
    "definition": "interface LLMClassifyOptions {\n  text: string;\n  categories: string[];\n  context?: Record<string, unknown>;\n}"
  }
];