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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:

# Check available MCP servers via mcporter
mcporter list

Priority:

1. MiniMax.web_search

   mcporter call MiniMax.web_search query:"keyword"
   

2. web-search-prime.web_search_prime

   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:

# 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:

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:

# [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):

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