Exa Answer

Token-efficient strategies for generating answers with structured output using exa-ai.

Use --help to see available commands and verify usage before running:

exa-ai <command> --help

Critical Requirements

MUST follow these rules when using exa-ai answer:

Shared Requirements

This skill inherits requirements from Common Requirements:

• Schema design patterns → All schema operations
• Output format selection → All output operations

MUST NOT Rules

1. Avoid –text flag: Use --text only when you need full source text; otherwise rely on default behavior for better token efficiency

Cost Optimization

Pricing

• Per answer: $0.005

Cost strategy:

• Use answer for questions with moderate complexity that need AI synthesis
• For simple lookups, use search instead (same cost but gives you URLs for verification)
• Consider whether you need a synthesized answer or just search results

Token Optimization

Apply these strategies:

• Use toon format: --output-format toon for 40% fewer tokens than JSON (use when reading output directly)
• Use JSON + jq: Extract only needed fields with jq (use when piping/processing output)
• Use schemas: Structure answers with --output-schema for consistent, parseable output
• Custom system prompts: Use --system-prompt to guide answer style and format

IMPORTANT: Choose one approach, don’t mix them:

• Approach 1: toon only – Compact YAML-like output for direct reading
• Approach 2: JSON + jq – Extract specific fields programmatically
• Approach 3: Schemas + jq – Get structured data, always use JSON output (default) and pipe to jq

Examples:

# ❌ High token usage
exa-ai answer "What is Claude?"

# ✅ Approach 1: toon format for direct reading (40% reduction)
exa-ai answer "What is Claude?" --output-format toon

# ✅ Approach 2: JSON + jq for field extraction (80% reduction)
exa-ai answer "What is Claude?" \
  --output-schema '{"type":"object","properties":{"product":{"type":"string"}}}' | jq -r '.answer.product'

# ❌ Don't mix toon with jq (toon is YAML-like, not JSON)
exa-ai answer "What is Claude?" --output-format toon | jq -r '.answer'

Quick Start

Basic Answer

exa-ai answer "What is Anthropic's main product?" --output-format toon

Structured Output

exa-ai answer "What is Claude?" \
  --output-schema '{"type":"object","properties":{"product_name":{"type":"string"},"company":{"type":"string"},"description":{"type":"string"}}}'

Array Output for Lists

exa-ai answer "What are the top 5 programming languages in 2024?" \
  --output-schema '{"type":"object","properties":{"languages":{"type":"array","items":{"type":"string"}}}}' | jq -r '.answer.languages | map("- " + .) | join("\n")'

Custom System Prompt

exa-ai answer "Explain quantum computing" \
  --system-prompt "Respond in simple terms suitable for a high school student"

Detailed Reference

For complete options, examples, and schema design tips, consult REFERENCE.md.

Shared Requirements

Schema Design

MUST: Use object wrapper for schemas

Applies to: answer, search, find-similar, get-contents

When using schema parameters (--output-schema or --summary-schema), always wrap properties in an object:

{"type":"object","properties":{"field_name":{"type":"string"}}}

DO NOT use bare properties without the object wrapper:

{"properties":{"field_name":{"type":"string"}}}  // ❌ Missing "type":"object"

Why: The Exa API requires a valid JSON Schema with an object type at the root level. Omitting this causes validation errors.

Examples:

# ✅ CORRECT - object wrapper included
exa-ai search "AI news" \
  --summary-schema '{"type":"object","properties":{"headline":{"type":"string"}}}'

# ❌ WRONG - missing object wrapper
exa-ai search "AI news" \
  --summary-schema '{"properties":{"headline":{"type":"string"}}}'

Output Format Selection

MUST NOT: Mix toon format with jq

Applies to: answer, context, search, find-similar, get-contents

toon format produces YAML-like output, not JSON. DO NOT pipe toon output to jq for parsing:

# ❌ WRONG - toon is not JSON
exa-ai search "query" --output-format toon | jq -r '.results'

# ✅ CORRECT - use JSON (default) with jq
exa-ai search "query" | jq -r '.results[].title'

# ✅ CORRECT - use toon for direct reading only
exa-ai search "query" --output-format toon

Why: jq expects valid JSON input. toon format is designed for human readability and produces YAML-like output that jq cannot parse.

SHOULD: Choose one output approach

Applies to: answer, context, search, find-similar, get-contents

Pick one strategy and stick with it throughout your workflow:

1. Approach 1: toon only – Compact YAML-like output for direct reading

   • Use when: Reading output directly, no further processing needed
   • Token savings: ~40% reduction vs JSON
   • Example: exa-ai search "query" --output-format toon

2. Approach 2: JSON + jq – Extract specific fields programmatically

   • Use when: Need to extract specific fields or pipe to other commands
   • Token savings: ~80-90% reduction (extracts only needed fields)
   • Example: exa-ai search "query" | jq -r '.results[].title'

3. Approach 3: Schemas + jq – Structured data extraction with validation

   • Use when: Need consistent structured output across multiple queries
   • Token savings: ~85% reduction + consistent schema
   • Example: exa-ai search "query" --summary-schema '{...}' | jq -r '.results[].summary | fromjson'

Why: Mixing approaches increases complexity and token usage. Choosing one approach optimizes for your use case.

Shell Command Best Practices

MUST: Run commands directly, parse separately

Applies to: monitor, search (websets), research, and all skills using complex commands

When using the Bash tool with complex shell syntax, run commands directly and parse output in separate steps:

# ❌ WRONG - nested command substitution
webset_id=$(exa-ai webset-create --search '{"query":"..."}' | jq -r '.webset_id')

# ✅ CORRECT - run directly, then parse
exa-ai webset-create --search '{"query":"..."}'
# Then in a follow-up command:
webset_id=$(cat output.json | jq -r '.webset_id')

Why: Complex nested $(...) command substitutions can fail unpredictably in shell environments. Running commands directly and parsing separately improves reliability and makes debugging easier.

MUST NOT: Use nested command substitutions

Applies to: All skills when using complex multi-step operations

Avoid nesting multiple levels of command substitution:

# ❌ WRONG - deeply nested
result=$(exa-ai search "$(cat query.txt | tr '\n' ' ')" --num-results $(cat config.json | jq -r '.count'))

# ✅ CORRECT - sequential steps
query=$(cat query.txt | tr '\n' ' ')
count=$(cat config.json | jq -r '.count')
exa-ai search "$query" --num-results $count

Why: Nested command substitutions are fragile and hard to debug when they fail. Sequential steps make each operation explicit and easier to troubleshoot.

SHOULD: Break complex commands into sequential steps

Applies to: All skills when working with multi-step workflows

For readability and reliability, break complex operations into clear sequential steps:

# ❌ Less maintainable - everything in one line
exa-ai webset-create --search '{"query":"startups","count":1}' | jq -r '.webset_id' | xargs -I {} exa-ai webset-search-create {} --query "AI" --behavior override

# ✅ More maintainable - clear steps
exa-ai webset-create --search '{"query":"startups","count":1}'
webset_id=$(jq -r '.webset_id' < output.json)
exa-ai webset-search-create $webset_id --query "AI" --behavior override

Why: Sequential steps are easier to understand, debug, and modify. Each step can be verified independently.