Web Search Skill | Agent Skills

Web Search

Overview

Search the web using Exa AI's MCP endpoint for real-time information retrieval. Returns raw JSON results with source URLs optimized for LLM consumption.

When to Use

User asks to search the web or find online information
Need current/recent information beyond knowledge cutoff
Looking up documentation, news, or real-time data
Researching topics that require up-to-date sources

Workflow

Identify the search query from user request
Determine appropriate search parameters:
- --type auto (default) for balanced results
- --type fast for quick lookups
- --type deep for comprehensive research
Run the search script
Present results with source URLs to user

Usage

# Basic search
uv run --script scripts/web_search.py --query "your search query"

# With options
uv run --script scripts/web_search.py \
  --query "search query" \
  --num-results 8 \
  --type auto \
  --livecrawl fallback \
  --context-max-chars 10000 \
  --timeout 25

Parameters

| Parameter | Default | Description | | --------------------- | ---------- | ---------------------------------------- | | --query | (required) | Search query string | | --num-results | 8 | Number of results to return | | --type | auto | Search type: auto, fast, deep | | --livecrawl | fallback | Live crawl mode: fallback, preferred | | --context-max-chars | 10000 | Max characters for context | | --timeout | 25 | Request timeout in seconds |

Output Contract

| Scenario | stdout | stderr | exit code | | ---------- | ----------------- | --------------- | --------- | | Success | Raw JSON from Exa | (empty) | 0 | | No results | {"results": []} | Warning message | 0 | | Error | (empty) | Error message | 1 |

Success output contains:

Page titles and URLs
Content snippets optimized for LLM context
Source attribution

Prerequisites

Uses Exa AI's free MCP endpoint (no API key required)
Requires uv for running PEP 723 scripts

Examples

Quick lookup

uv run --script scripts/web_search.py \
  --query "Python 3.12 new features" \
  --type fast \
  --num-results 3

Deep research

uv run --script scripts/web_search.py \
  --query "LLM agent architectures 2024" \
  --type deep \
  --num-results 10 \
  --livecrawl preferred

Agent Skills: Web Search

Skill Files