Skill: wanghan0501/deep-research-with-mcp (version 1.1.1)

--- name: Deep Research with MCP slug: deep-research-with-mcp version: 1.1.1 description: Multi-source deep research agent using MCP search tools (minimax web_search or zai-mcp-server web_search_prime). homepage: https://github.com/wanghan0501/deep-research-with-mcp metadata: emoji: "🔬" category: "research" requires: bins: ["mcporter", "curl", "python3"] license: MIT-0 mcp_requirements: search_tools: - name: "MiniMax.web_search" description: "Primary MCP search tool (recommended)" required: true - name: "web-search-prime.web_search_prime" description: "Alternative MCP search tool" required: true --- # Deep Research with MCP 🔬 A powerful, self-contained deep research skill that produces thorough, cited reports from multiple web sources. Uses MCP-configured search tools (minimax web_search or zai-mcp-server web_search_prime). ## Prerequisites Before using this skill, ensure you have configured at least one MCP search tool: 1. **MiniMax web_search** (recommended) - Configure via `minimax` MCP server with `web_search` tool 2. **web-search-prime web_search_prime** - Configure via `web-search-prime` MCP server with `web_search_prime` tool If neither MCP is configured, the skill will not work. ## How It Works When the user asks for research on any topic, follow this workflow: ### Step 0: Check MCP Search Availability First verify MCP search tool availability: ```bash # Check available MCP servers via mcporter mcporter list ``` **Priority:** 1. `MiniMax.web_search` ```bash mcporter call MiniMax.web_search query:"keyword" ``` 2. `web-search-prime.web_search_prime` ```bash mcporter call web-search-prime.web_search_prime search_query:"keyword" ``` ### Step 1: Understand the Goal (30 seconds) Ask 1-2 quick clarifying questions: - "What's your goal — learning, making a decision, or writing something?" - "Any specific angle or depth you want?" If the user says "just research it" — skip ahead with reasonable defaults. ### Step 2: Plan the Research (think before searching) Break the topic into 3-5 research sub-questions. For example: - Topic: "Impact of AI on healthcare" - What are the main AI applications in healthcare today? - What clinical outcomes have been measured? - What are the regulatory challenges? - What companies are leading this space? - What's the market size and growth trajectory? ### Step 3: Execute Multi-Source Search Use MCP search tools to query: ```bash # Using zai-mcp-server web_search_prime mcporter call web-search-prime.web_search_prime search_query:"<sub-question>" # Using minimax web_search mcporter call MiniMax.web_search query:"<sub-question>" ``` **Search strategy:** - Use 2-3 different keyword variations per sub-question - Aim for 15-30 unique sources total - Prioritize: academic, official, reputable news > blogs > forums ### Step 4: Deep-Read Key Sources For the most promising URLs, fetch full content: ```bash curl -sL "<url>" | python3 -c " import sys, re html = sys.stdin.read() # Strip tags, get text text = re.sub('<[^>]+>', ' ', html) text = re.sub(r'\s+', ' ', text).strip() print(text[:5000]) " ``` Read 3-5 key sources in full for depth. Don't just rely on search snippets. ### Step 5: Synthesize & Write Report Structure the report as: ```markdown # [Topic]: Deep Research Report *Generated: [date] | Sources: [N] | Confidence: [High/Medium/Low]* ## Executive Summary [3-5 sentence overview of key findings] ## 1. [First Major Theme] [Findings with inline citations] - Key point ([Source Name](url)) - Supporting data ([Source Name](url)) ## 2. [Second Major Theme] ... ## 3. [Third Major Theme] ... ## Key Takeaways - [Actionable insight 1] - [Actionable insight 2] - [Actionable insight 3] ## Sources 1. [Title](url) — [one-line summary] 2. ... ## Methodology Searched [N] queries across web and news. Analyzed [M] sources. Sub-questions investigated: [list] ``` ### Step 6: Save & Deliver Save report to the current agent's working directory (auto-resolves to actual runtime path): ```bash mkdir -p research/[slug] # Write report to research/[slug]/report.md ``` Then deliver: - **Short topics**: Post the full report in chat - **Long reports**: Post the executive summary + key takeaways, offer full report as file ## Quality Rules 1. **Every claim needs a source.** No unsourced assertions. 2. **Cross-reference.** If only one source says it, flag it as unverified. 3. **Recency matters.** Prefer sources from the last 12 months. 4. **Acknowledge gaps.** If you couldn't find good info on a sub-question, say so. 5. **No hallucination.** If you don't know, say "insufficient data found." ## Examples ``` "Research the current state of nuclear fusion energy" "Deep dive into Rust vs Go for backend services in 2026" "Research the best strategies for bootstrapping a SaaS business" "What's happening with the US housing market right now?" ``` ## For Sub-Agent Usage When spawning as a sub-agent, include the full research request and context: ``` sessions_spawn( task: "Run deep research on [TOPIC]. Follow the deep-research-with-mcp SKILL.md workflow. Goal: [user's goal] Specific angles: [any specifics] Save report to research/[slug]/report.md (relative to agent's working directory) When done, wake the main session with key findings.", label: "research-[slug]", model: "opus" ) ``` ## Requirements - **MCP Search Tool** (one of the following): - Minimax MCP server with `web_search` tool (recommended) - zai-mcp-server with `web_search_prime` tool - curl (for fetching full pages) - Configure MCP in your OpenClaw config before using this skill


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)