Research Reporter

Research Reporter

Overview

Automate the process of researching topics through web search and authoritative sources, then synthesize findings into well-structured reports saved to user-specified locations.

When to Use This Skill

Use this skill when the user requests:

• Research on a topic with results saved to a file
• Web searches combined with report generation
• Information compilation from multiple sources
• Trend analysis or competitive research saved as documents
• Any "search and save" or "research and write" workflow

Example trigger phrases:

• "Research TypeScript trends and save to a file"
• "Search for information about X and compile it into a report"
• "Find the latest news on Y and save the summary"
• "Investigate Z and create a document with your findings"

Research & Report Workflow

Follow this sequential workflow to execute research and reporting tasks effectively.

Step 1: Identify Research Parameters

Extract the following from the user request:

• Topic: What to research (e.g., "TypeScript trends 2025")
• Output Path: Where to save the report (e.g., /Users/user/Desktop/report.txt)
• Scope: How comprehensive the research should be (default: 5-10 sources)
• Format: Report structure preference (default: structured markdown)

Step 2: Execute Web Search

Use the web_search tool from mcp_server_search to gather initial information.

Search strategy:

1. Craft a comprehensive search query including:
   • Main topic keywords
   • Time qualifiers if relevant ("2024", "2025", "latest")
   • Specificity terms ("trends", "new features", "best practices")
2. Request 5-10 results for breadth
3. Review search results for relevant sources

Code pattern:

import asyncio
from extensions.wrapped_mcp.mcp_server_search import web_search

async def main():
    search_results = await web_search({
        "query": "topic keywords with time qualifiers",
        "num_results": 5
    })
    print(search_results)

asyncio.run(main())

Step 3: Fetch Authoritative Sources

Use the fetch tool from mcp_server_fetch to retrieve content from official or authoritative sources.

Fetching strategy:

1. Identify 1-3 authoritative URLs (official sites, documentation, reputable publishers)
2. Fetch each with appropriate max_length (5000-10000 characters)
3. Handle errors gracefully with try-except blocks

Code pattern:

from extensions.wrapped_mcp.mcp_server_fetch import fetch

async def fetch_official_info():
    try:
        result = await fetch({
            "url": "https://official-source.com",
            "max_length": 10000
        })
        return result
    except Exception as e:
        print(f"Error fetching: {e}")
        return None

Step 4: Synthesize Information

Combine search results and fetched content into a structured report.

Report structure:

# [Topic] Research Report

Date: [YYYY-MM-DD HH:MM:SS]

## 🔍 Web Search Results

[Search results with numbered sources, titles, descriptions, and URLs]

## 📚 Authoritative Sources

[Content from official sites and primary sources]

## 💡 Key Insights

[Synthesized summary of main findings, trends, or patterns]

Best practices:

• Include timestamps for time-sensitive research
• Organize sources clearly with titles and URLs
• Provide a synthesized summary beyond raw data
• Use clear section headers and formatting

Step 5: Save Report to File

Write the compiled report to the user-specified file path.

File operations:

from datetime import datetime

# Create report content
report = f"""# {topic} Research Report

Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

## 🔍 Web Search Results

{search_results}

## 📚 Authoritative Sources

{fetched_content}

## 💡 Key Insights

{synthesized_summary}
"""

# Save to file
with open(output_path, "w", encoding="utf-8") as f:
    f.write(report)

print(f"✅ Report saved to {output_path}")
print(f"📊 File size: {len(report)} characters")

Step 6: Confirm Completion

Provide clear feedback about:

• Successful completion
• File location (full path)
• Number of sources consulted
• File size or content summary

Complete Implementation Script

The scripts/research_and_report.py script provides a complete, reusable implementation of this workflow that can be executed directly or customized as needed.

Usage:

python scripts/research_and_report.py

Then modify the script parameters:

• topic: Research topic
• output_path: Where to save the report
• num_search_results: How many search results to gather
• official_urls: List of authoritative sources to fetch

Error Handling

Handle common errors gracefully:

Network errors:

try:
    result = await fetch({"url": url})
except Exception as e:
    print(f"⚠️ Could not fetch {url}: {e}")
    # Continue with available data

File path errors:

import os

# Ensure directory exists
os.makedirs(os.path.dirname(output_path), exist_ok=True)

# Write with proper encoding
with open(output_path, "w", encoding="utf-8") as f:
    f.write(report)

Tips for Effective Research

1. Search query optimization: Include time qualifiers, specific terms, and context
2. Source diversity: Mix web search results with official documentation
3. Content limits: Use appropriate max_length values (5000-10000 for most pages)
4. Structured output: Use clear headers, emojis, and formatting for readability
5. Synthesis: Don't just concatenate sources—provide insights and summaries