How to use MCP for AI-powered document search

1// Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json
2{
3  "mcpServers": {
4    "documents": {
5      "command": "npx",
6      "args": [
7        "-y", "@modelcontextprotocol/server-filesystem",
8        "/Users/you/Documents",
9        "/Users/you/Projects/docs"
10      ]
11    }
12  }
13}
14
15// For Cursor: .cursor/mcp.json
16// For VS Code: use "servers" key instead of "mcpServers"

1// Example questions to ask:
2
3// File discovery:
4// "What documents are in my Documents folder?"
5// "Find all PDF files in my Projects directory"
6// "List all Markdown files related to onboarding"
7
8// Content search:
9// "Find the document that talks about our pricing strategy"
10// "Which files mention the Q1 2026 budget?"
11// "Search for any documents about the API migration plan"
12
13// Summarization:
14// "Read the meeting-notes-march.md file and summarize the key decisions"
15// "Compare the contents of proposal-v1.md and proposal-v2.md"
16// "What are the action items mentioned across all files in the meetings/ folder?"

1// src/doc-search-server.ts
2import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
3import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
4import { z } from "zod";
5import fs from "fs/promises";
6import path from "path";
7
8interface IndexEntry {
9  path: string;
10  content: string;
11  modified: Date;
12}
13
14const index: IndexEntry[] = [];
15
16async function indexDirectory(dir: string): Promise<void> {
17  const entries = await fs.readdir(dir, { withFileTypes: true, recursive: true });
18  for (const entry of entries) {
19    if (!entry.isFile()) continue;
20    const ext = path.extname(entry.name).toLowerCase();
21    if (!['.md', '.txt', '.json', '.ts', '.js', '.py', '.yaml', '.yml'].includes(ext)) continue;
22    const fullPath = path.join(dir, entry.name);
23    try {
24      const content = await fs.readFile(fullPath, 'utf-8');
25      const stat = await fs.stat(fullPath);
26      index.push({ path: fullPath, content, modified: stat.mtime });
27    } catch {}
28  }
29  console.error(`Indexed ${index.length} files from ${dir}`);
30}
31
32const server = new McpServer({ name: "doc-search", version: "1.0.0" });
33
34server.tool("search_content", "Search documents by content", {
35  query: z.string().describe("Search term or phrase"),
36  maxResults: z.number().default(10),
37}, async ({ query, maxResults }) => {
38  const lower = query.toLowerCase();
39  const results = index
40    .filter(e => e.content.toLowerCase().includes(lower))
41    .slice(0, maxResults)
42    .map(e => {
43      const idx = e.content.toLowerCase().indexOf(lower);
44      const start = Math.max(0, idx - 100);
45      const end = Math.min(e.content.length, idx + query.length + 100);
46      return { file: e.path, excerpt: '...' + e.content.slice(start, end) + '...' };
47    });
48  return { content: [{ type: "text", text: JSON.stringify(results, null, 2) }] };
49});
50
51async function main() {
52  const dirs = process.argv.slice(2);
53  for (const dir of dirs) await indexDirectory(dir);
54  await server.connect(new StdioServerTransport());
55  console.error("Document search server running");
56}
57main().catch(e => { console.error(e); process.exit(1); });

1// Example workflow the AI executes:
2// 1. User asks: "What were the key decisions from last month's meetings?"
3// 2. AI calls search_content with query "meeting" or "decisions"
4// 3. AI reads the top matching files with read_file
5// 4. AI synthesizes a summary across all meeting notes
6// 5. AI returns a structured answer with source citations
7
8// To enable this, configure both servers:
9{
10  "mcpServers": {
11    "filesystem": {
12      "command": "npx",
13      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/Documents"]
14    },
15    "doc-search": {
16      "command": "node",
17      "args": ["dist/doc-search-server.js", "/Users/you/Documents"]
18    }
19  }
20}