agent-research

name	agent-research
description	Autonomous research agent — start complex web research tasks, get structured results. This skill should be used when the user asks for multi-site exploration, open-ended web research, or deep-dive analysis across multiple sources.

Research Agent

The autonomous research agent browses, searches, and extracts information based on a natural language prompt. Use it for complex multi-site research that would be tedious to do manually.

Tools

Tool	Description
agent	Start an autonomous web research agent
agent_status	Check agent results

Start Research Agent

Tool: agent
Input: {
  "prompt": "Find the pricing plans for the top 5 project management tools and compare their features",
  "urls": ["https://asana.com/pricing", "https://monday.com/pricing"]
}

Returns: { "agent_id": "agent_xxx", "status": "running" }

Parameters

Parameter	Type	Description
prompt	string	Research task description (required, max 10,000 chars)
urls	string[]	Starting URLs to guide the agent (optional)
schema	object	JSON schema for structured output (optional)

Check Agent Results

Tool: agent_status
Input: { "id": "agent_xxx" }

Returns: {
  "status": "completed" | "running" | "failed",
  "result": { ... }
}

Workflows

Research with Structured Output

1. agent({
     prompt: "Research the top 5 CRM tools, find their pricing and key features",
     schema: {
       "type": "object",
       "properties": {
         "tools": {
           "type": "array",
           "items": {
             "type": "object",
             "properties": {
               "name": { "type": "string" },
               "pricing": { "type": "string" },
               "features": { "type": "array", "items": { "type": "string" } }
             }
           }
         }
       }
     }
   }) → Start agent
2. agent_status(agent_id) → Poll until complete
3. Process structured results

Research Starting from Known URLs

1. agent({
     prompt: "Compare the API documentation quality of these services",
     urls: ["https://docs.stripe.com", "https://docs.paypal.com"]
   }) → Start with specific URLs
2. agent_status(agent_id) → Get comparison results

When to Use Agent vs Manual Tools

Use Case	Approach	Why
Simple page scrape	scrape_url	Faster, more predictable
Known data extraction	extract_data	Direct, schema-based
Complex multi-site research	agent	Autonomous navigation
Open-ended exploration	agent	Agent decides what to search/scrape
Comparison across many sources	agent	Agent handles discovery

Best Practices

1. Be specific in prompts — clear task description gives better results
2. Provide starting URLs — guides the agent to relevant sources
3. Use schemas — structured output is more reliable than free-form
4. Keep prompts under 10k chars — respect the limit
5. Poll for results — agents take time for complex research

Error Handling

• Agent fails → simplify prompt or provide more specific URLs
• Incomplete results → break research into smaller, focused prompts
• Schema mismatch → ensure schema properties match what can be found on the web
• Timeout → reduce scope of research task