import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { UserContext } from "../types";
export function registerSearchTool(server: McpServer, userContext: UserContext) {
server.tool(
"search",
"Search for content across various data sources",
{
query: {
type: "string",
description: "The search query to find relevant content"
},
source: {
type: "string",
description: "The data source to search in (e.g., 'documents', 'database', 'files')",
enum: ["documents", "database", "files", "web", "knowledge_base"]
},
limit: {
type: "number",
description: "Maximum number of results to return (default: 10)",
minimum: 1,
maximum: 50
}
},
async (args) => {
const { query, source = "documents", limit = 10 } = args;
// Simulate search functionality - replace with actual search logic
const searchResults = await performSearch(query as string, source as string, limit as number, userContext);
return {
content: [
{
type: "text",
text: JSON.stringify({
query,
source,
results: searchResults,
total_results: searchResults.length,
timestamp: new Date().toISOString()
}, null, 2),
},
],
};
}
);
}
async function performSearch(query: string, source: string, limit: number, userContext: UserContext) {
// This is a mock implementation - replace with actual search logic
// You would integrate with your actual data sources here
const mockResults = [
{
id: "doc_1",
title: `Search result for "${query}" in ${source}`,
content: `This is a mock search result for the query "${query}" from ${source}`,
relevance_score: 0.95,
url: `https://example.com/${source}/doc_1`,
last_modified: "2024-12-19T10:00:00Z"
},
{
id: "doc_2",
title: `Another result for "${query}"`,
content: `This is another mock search result related to "${query}"`,
relevance_score: 0.87,
url: `https://example.com/${source}/doc_2`,
last_modified: "2024-12-18T15:30:00Z"
}
];
// Filter based on user context if needed
return mockResults.slice(0, limit).map(result => ({
...result,
searched_by: userContext.userId
}));
}
