mcp-deep-research/dist/index.js

[](https://smithery.ai/server/@baranwang/mcp-deep-research) [](https://www.npmjs.com/package/mcp-deep-research) ![NPM Li

#!/usr/bin/env node
import * as __WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_server_mcp_js_2c42c5d0__ from "@modelcontextprotocol/sdk/server/mcp.js";
import * as __WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_server_stdio_js_4f861174__ from "@modelcontextprotocol/sdk/server/stdio.js";
import * as __WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_types_js_fc45a208__ from "@modelcontextprotocol/sdk/types.js";
import * as __WEBPACK_EXTERNAL_MODULE__tavily_core_d71b120a__ from "@tavily/core";
import * as __WEBPACK_EXTERNAL_MODULE_zod__ from "zod";
import * as __WEBPACK_EXTERNAL_MODULE_zod_to_json_schema_a1d75503__ from "zod-to-json-schema";
function safeParseInt(value, defaultValue) {
    const parsed = Number.parseInt(value ?? '');
    return Number.isNaN(parsed) || parsed < 1 ? defaultValue : parsed;
}
const CONFIG = {
    MAX_SEARCH_KEYWORDS: safeParseInt(process.env.MAX_SEARCH_KEYWORDS, 5),
    MAX_PLANNING_ROUNDS: safeParseInt(process.env.MAX_PLANNING_ROUNDS, 5),
    TAVILY_API_URL: 'https://api.tavily.com/search',
    TAVILY_API_KEY: process.env.TAVILY_API_KEY,
    PACKAGE_VERSION: "1.1.0"
};
async function searchTavily(keyword, options) {
    const tvly = (0, __WEBPACK_EXTERNAL_MODULE__tavily_core_d71b120a__.tavily)({
        apiKey: CONFIG.TAVILY_API_KEY
    });
    const response = await tvly.search(keyword, options);
    let result = `## Search Results for \`${response.query}\`\n`;
    response.results.forEach((searchResult, index)=>{
        result += `### Reference ${index + 1}:\n`;
        result += `Title: ${searchResult.title}\n`;
        result += `Content: ${searchResult.content}\n`;
    });
    return result;
}
function generatePrompt(question, searchResults, rounds) {
    return `# Role Definition
You are a professional information search and analysis expert, specializing in:
- Analyzing core information needs from user queries
- Designing precise search strategies
- Synthesizing and distilling information
- Providing accurate and comprehensive answers

# User Question
${question}

# Current Environment
Current Search Round: ${rounds}
Maximum Search Rounds: ${CONFIG.MAX_PLANNING_ROUNDS}
Current Time: ${new Date().toLocaleString()}

# Known Information
${searchResults ?? 'No reference information available'}

# Analysis Steps
1. Resource Sufficiency Assessment
   - Carefully analyze if existing information is sufficient to answer the user's question
   - Identify information gaps or areas requiring additional verification

2. Search Strategy (if needed)
   - Identify specific information points that need supplementation
   - Design precise search keywords (each keyword must meet these requirements):
     * Include complete subject and predicate
     * Avoid pronouns and references
     * Have independent search value
     * Avoid logical overlap between keywords
   - Keyword quantity limit: 1~${CONFIG.MAX_SEARCH_KEYWORDS}

3. Continue Search (if needed)
   - Use current "Known Information" as reference parameter
   - Set rounds parameter to ${rounds + 1}
   - Output only search keywords, without any other content

4. Direct Answer (if information is sufficient)
   - Provide complete answer based on available information
   - Ensure the answer is accurate, comprehensive, and logically clear

# Output Requirements
## If Further Search is Needed
- Output only the list of search keywords
- One keyword per line
- Do not include any other explanations or notes

## If Direct Answer is Possible
- Provide a clear, structured answer
- Reference relevant sources when necessary
- Clearly indicate any uncertain information

# Constraints
- Current round has reached ${rounds}/${CONFIG.MAX_PLANNING_ROUNDS}, must provide direct answer if maximum rounds exceeded
- Search keywords must be independent and precise, no ambiguity allowed
- Prohibited to output content unrelated to search strategy`;
}
const DeepResearchToolSchema = __WEBPACK_EXTERNAL_MODULE_zod__.z.object({
    question: __WEBPACK_EXTERNAL_MODULE_zod__.z.string().describe('User question'),
    reference: __WEBPACK_EXTERNAL_MODULE_zod__.z.string().optional().describe('Reference materials'),
    keywords: __WEBPACK_EXTERNAL_MODULE_zod__.z.array(__WEBPACK_EXTERNAL_MODULE_zod__.z.string()).min(0).max(CONFIG.MAX_SEARCH_KEYWORDS).optional().describe(`Search keywords, please provide 1~${CONFIG.MAX_SEARCH_KEYWORDS} keywords. Each keyword must: include complete subject and predicate, avoid pronouns and references, have independent search value, avoid logical overlap between keywords, and be directly relevant to the question`),
    topic: __WEBPACK_EXTERNAL_MODULE_zod__.z["enum"]([
        'general',
        'news',
        'finance'
    ]).default('general').describe('Search topic'),
    rounds: __WEBPACK_EXTERNAL_MODULE_zod__.z.number().default(1).describe('Current search round, defaults to 1')
});
async function deepResearch(args) {
    if (!CONFIG.TAVILY_API_KEY) return {
        isError: true,
        content: [
            {
                type: 'text',
                text: 'Please configure the `TAVILY_API_KEY` environment variable, get it from https://tavily.com/'
            }
        ]
    };
    const { question, reference = '', keywords = [], topic, rounds } = DeepResearchToolSchema.parse(args);
    let searchResults = reference;
    if (keywords.length) {
        const results = await Promise.allSettled(keywords.map((keyword)=>searchTavily(keyword, {
                topic
            })));
        searchResults += results.filter((result)=>'fulfilled' === result.status).map((result)=>result.value).join('\n');
    }
    return {
        content: [
            {
                type: 'text',
                text: generatePrompt(question, searchResults, rounds)
            }
        ]
    };
}
async function main() {
    const mcpServer = new __WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_server_mcp_js_2c42c5d0__.McpServer({
        name: 'DeepResearch',
        version: CONFIG.PACKAGE_VERSION
    }, {
        capabilities: {
            tools: {}
        }
    });
    mcpServer.server.setRequestHandler(__WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_types_js_fc45a208__.ListToolsRequestSchema, ()=>({
            tools: [
                {
                    name: 'deep-research',
                    description: 'Deep web information search tool that can conduct multi-round in-depth research based on keywords and topics',
                    inputSchema: (0, __WEBPACK_EXTERNAL_MODULE_zod_to_json_schema_a1d75503__.zodToJsonSchema)(DeepResearchToolSchema)
                }
            ]
        }));
    mcpServer.server.setRequestHandler(__WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_types_js_fc45a208__.CallToolRequestSchema, (request)=>deepResearch(request.params.arguments));
    const transport = new __WEBPACK_EXTERNAL_MODULE__modelcontextprotocol_sdk_server_stdio_js_4f861174__.StdioServerTransport();
    await mcpServer.connect(transport);
}
main().catch((error)=>{
    console.error(error);
    process.exit(1);
});