import { z } from "zod";
// Define the static discovery tool schema using Zod
export const DiscoverToolsInputSchema = z.object({
server_uuid: z.string().uuid().optional().describe("Optional UUID of a specific server to discover. If omitted, attempts to discover all."),
force_refresh: z.boolean().optional().default(false).describe("Set to true to bypass cache and force a fresh discovery. Defaults to false."),
}).describe("Triggers tool discovery for configured MCP servers in the Pluggedin App.");
// Define the schema for asking questions to the knowledge base
export const AskKnowledgeBaseInputSchema = z.object({
query: z.string()
.min(1, "Query cannot be empty")
.max(1000, "Query too long")
.describe("Your question or query to get AI-generated answers from the knowledge base.")
}).describe("Ask questions and get AI-generated answers from your knowledge base. Returns JSON with answer, sources, and metadata.");
// Input schema for send notification validation
export const SendNotificationInputSchema = z.object({
title: z.string().optional(),
message: z.string().min(1, "Message cannot be empty"),
severity: z.enum(["INFO", "SUCCESS", "WARNING", "ALERT"]).default("INFO"),
link: z.string().url().optional(),
email: z.boolean().default(false),
});
// Input schema for list notifications validation
export const ListNotificationsInputSchema = z.object({
limit: z.number().int().min(1).max(100).default(20),
unreadOnly: z.boolean().default(false),
severity: z.enum(["INFO", "SUCCESS", "WARNING", "ALERT"]).optional(),
});
// Input schema for mark notification done validation
export const MarkNotificationDoneInputSchema = z.object({
notificationId: z.string().min(1, "Notification ID cannot be empty"),
});
// Input schema for delete notification validation
export const DeleteNotificationInputSchema = z.object({
notificationId: z.string().min(1, "Notification ID cannot be empty"),
});
// Input schema for create document validation
export const CreateDocumentInputSchema = z.object({
title: z.string()
.min(1, "Title is required")
.max(255, "Title too long")
.describe("Document title - concise, descriptive name for the document"),
content: z.string()
.min(1, "Content is required")
.describe("Document content in the specified format (markdown, plain text, JSON, or HTML)"),
format: z.enum(["md", "txt", "json", "html"])
.default("md")
.describe("Document format: 'md' for Markdown (default), 'txt' for plain text, 'json' for JSON data, 'html' for HTML markup"),
tags: z.array(z.string())
.max(20, "Maximum 20 tags allowed")
.optional()
.describe("Optional tags for categorization and search (e.g., ['api-docs', 'typescript', 'tutorial'])"),
category: z.enum(["report", "analysis", "documentation", "guide", "research", "code", "other"])
.default("other")
.describe("Document category: 'report' for reports/summaries, 'analysis' for data analysis, 'documentation' for technical docs, 'guide' for how-to guides, 'research' for research papers, 'code' for code snippets, 'other' for miscellaneous"),
metadata: z.object({
model: z.object({
name: z.string()
.describe("AI model name (e.g., 'claude-3-opus', 'gpt-4', 'gemini-pro')"),
provider: z.string()
.describe("Model provider (e.g., 'anthropic', 'openai', 'google')"),
version: z.string()
.optional()
.describe("Optional model version (e.g., '20240229', '1.5')"),
}).describe("AI model information for attribution"),
context: z.string()
.optional()
.describe("Optional context about how/why this document was created"),
visibility: z.enum(["private", "workspace", "public"])
.default("private")
.describe("Document visibility: 'private' (only you), 'workspace' (your team), 'public' (everyone)"),
prompt: z.string()
.optional()
.describe("The user prompt/question that triggered this document creation"),
conversationContext: z.array(z.string())
.optional()
.describe("Previous conversation messages that provide context for this document (array of message strings)"),
sourceDocuments: z.array(z.string())
.optional()
.describe("UUIDs of existing documents used as references or sources for this document"),
generationParams: z.object({
temperature: z.number()
.min(0)
.max(2)
.optional()
.describe("Model temperature setting (0.0-2.0, lower = more focused, higher = more creative)"),
maxTokens: z.number()
.positive()
.optional()
.describe("Maximum tokens for generation"),
topP: z.number()
.min(0)
.max(1)
.optional()
.describe("Top-p sampling value (0.0-1.0, nucleus sampling parameter)"),
})
.optional()
.describe("Optional generation parameters used by the AI model"),
}).describe("Required metadata for AI-generated document attribution and tracking"),
}).describe("Create and save an AI-generated document to the user's library with full metadata tracking");
// Input schema for list documents validation
export const ListDocumentsInputSchema = z.object({
filters: z.object({
source: z.enum(["all", "upload", "ai_generated", "api"]).default("all"),
modelName: z.string().optional(),
modelProvider: z.string().optional(),
dateFrom: z.string().datetime().optional(),
dateTo: z.string().datetime().optional(),
tags: z.array(z.string()).optional(),
category: z.enum(["report", "analysis", "documentation", "guide", "research", "code", "other"]).optional(),
searchQuery: z.string().optional(),
}).optional(),
sort: z.enum(["date_desc", "date_asc", "title", "size"]).default("date_desc"),
limit: z.number().int().min(1).max(100).default(20),
offset: z.number().int().min(0).default(0),
});
// Define the search documents input schema using Zod
export const SearchDocumentsInputSchema = z.object({
query: z.string().min(1).max(500),
filters: z.object({
modelName: z.string().optional(),
modelProvider: z.string().optional(),
dateFrom: z.string().datetime().optional(),
dateTo: z.string().datetime().optional(),
tags: z.array(z.string()).optional(),
source: z.enum(["all", "upload", "ai_generated", "api"]).default("all"),
}).optional(),
limit: z.number().int().min(1).max(50).default(10),
});
// Define the get document input schema using Zod
export const GetDocumentInputSchema = z.object({
documentId: z.string().uuid(),
includeContent: z.boolean().default(false),
includeVersions: z.boolean().default(false),
});
// Define the update document input schema using Zod
export const UpdateDocumentInputSchema = z.object({
documentId: z.string()
.uuid()
.describe("UUID of the document to update"),
operation: z.enum(["replace", "append", "prepend"])
.describe("Update operation: 'replace' to overwrite content, 'append' to add at end, 'prepend' to add at beginning"),
content: z.string()
.min(1, "Content is required")
.describe("New content to add or replace existing content with"),
metadata: z.object({
tags: z.array(z.string())
.optional()
.describe("Updated tags for the document"),
changeSummary: z.string()
.optional()
.describe("Brief summary of what changed in this update"),
updateReason: z.string()
.optional()
.describe("Why this update was made (e.g., 'Added error handling', 'Updated API endpoints')"),
changesFromPrompt: z.string()
.optional()
.describe("The user prompt that triggered this update"),
model: z.object({
name: z.string()
.describe("AI model name performing the update"),
provider: z.string()
.describe("Model provider (e.g., 'anthropic', 'openai')"),
version: z.string()
.optional()
.describe("Optional model version"),
}).describe("AI model information for update attribution"),
})
.optional()
.describe("Optional metadata about the update and model attribution"),
}).describe("Update or append to an existing AI-generated document with version tracking");
