agents-store Search & Data Extraction
This skill covers web search and structured data extraction with Firecrawl — searching with content, extracting data with JSON schemas, and LLM-powered extraction.
Available Tools
| Tool | Description |
|---|---|
search |
Search the web and get results with content |
extract_data |
Extract structured data from URLs |
Web Search
Basic Search
Tool: search
Input: {
"query": "best REST API design patterns 2024",
"limit": 5
}
Returns: Array of results with URL, title, description, and full page content.
Search with Geographic and Language Filters
Tool: search
Input: {
"query": "best CRM software",
"limit": 10,
"lang": "en",
"country": "us",
"location": "San Francisco, CA"
}
Search with Time Filter
Tool: search
Input: {
"query": "AI automation tools",
"limit": 5,
"tbs": "qdr:m"
}
Time filter values (tbs): qdr:h (past hour), qdr:d (past day), qdr:w (past week), qdr:m (past month), qdr:y (past year).
Search with Source Filtering
Tool: search
Input: {
"query": "workflow automation comparison",
"limit": 5,
"sources": ["techcrunch.com", "producthunt.com"]
}
Search Parameters
| Parameter | Type | Description |
|---|---|---|
query |
string | Search query (required) |
limit |
number | Max results (default: 5) |
lang |
string | Language code (e.g., "en", "de", "fr") |
country |
string | Country code (e.g., "us", "uk", "de") |
location |
string | Specific location (e.g., "New York") |
tbs |
string | Time-based filter (qdr:h, qdr:d, qdr:w, qdr:m, qdr:y) |
filter |
string | Additional search filter |
sources |
string[] | Limit to specific domains |
scrapeOptions |
object | Options for scraping results (formats, etc.) |
Each result includes the full scraped content of the page, not just a snippet.
Structured Data Extraction
Extract Parameters
| Parameter | Type | Description |
|---|---|---|
urls |
string[] | URLs to extract from (array) |
prompt |
string | What to extract (required) |
systemPrompt |
string | System-level extraction instructions |
schema |
object | JSON schema for structured output |
allowExternalLinks |
boolean | Follow external links during extraction |
enableWebSearch |
boolean | Augment extraction with web search |
includeSubdomains |
boolean | Include subdomain pages |
Extract with JSON Schema
Tool: extract_data
Input: {
"urls": ["https://store.example.com/product/123"],
"prompt": "Extract product details",
"schema": {
"type": "object",
"properties": {
"name": { "type": "string", "description": "Product name" },
"price": { "type": "number", "description": "Price in USD" },
"description": { "type": "string", "description": "Product description" },
"rating": { "type": "number", "description": "Average rating out of 5" },
"in_stock": { "type": "boolean", "description": "Whether product is in stock" }
},
"required": ["name", "price"]
}
}
Returns: { "name": "Widget Pro", "price": 29.99, "description": "...", "rating": 4.5, "in_stock": true }
Extract from Multiple URLs
Tool: extract_data
Input: {
"urls": [
"https://store.example.com/product/123",
"https://store.example.com/product/456"
],
"prompt": "Extract product name and price",
"schema": {
"type": "object",
"properties": {
"name": { "type": "string" },
"price": { "type": "number" }
}
}
}
Extract with Web Search Augmentation
Tool: extract_data
Input: {
"urls": ["https://example.com/company"],
"prompt": "Extract company revenue and employee count",
"enableWebSearch": true,
"systemPrompt": "If data is not on the page, use web search to find it"
}
Extract List Data
Tool: extract_data
Input: {
"urls": ["https://example.com/team"],
"schema": {
"type": "object",
"properties": {
"team_members": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"role": { "type": "string" },
"bio": { "type": "string" }
}
}
}
}
}
}
Extract with Prompt
Tool: extract_data
Input: {
"urls": ["https://example.com/pricing"],
"prompt": "Extract all pricing plans with their names, prices, and included features"
}
Common Workflows
Research a Topic
1. search(query, limit=10) -> Find relevant sources
2. Review results — identify most relevant URLs
3. scrape_url(best_url) -> Get full content for deep analysis
Extract Data from Multiple Pages
1. map_site(url) -> Discover product/listing pages
2. Filter URLs matching pattern (e.g., /products/*)
3. For each URL: extract_data(url, schema) -> Get structured data
4. Compile results
Competitive Analysis
1. search("competitor product features", limit=5) -> Find competitor pages
2. For each competitor: extract_data(url, features_schema) -> Get structured feature data
3. Compare features across competitors
Content Research
1. search(topic, limit=10) -> Get source pages
2. For top results: scrape_url(url) -> Get full content
3. Synthesize information from multiple sources
Schema Design Tips
- Use descriptive field names — helps LLM extraction accuracy
- Add descriptions to properties — guides the extraction model
- Use required fields — ensure critical data is extracted
- Keep schemas focused — extract one type of data per call
- Use arrays for lists — team members, pricing plans, features
- Test with one URL first — verify schema works before batch extraction
Best Practices
- Search before scraping — search is faster for finding specific information
- Use schemas for consistency — structured extraction is more reliable than free-form
- Provide descriptions in schemas — helps the extraction model understand context
- Start with simple schemas — add complexity gradually
- Combine search + extract — search to find, extract to structure
- Limit search results — 5-10 results is usually sufficient