exa-get-code-context

name	exa-get-code-context
description	Code context using Exa. Finds real snippets and docs from GitHub, StackOverflow, and technical docs. Use when searching for code examples, API syntax, library documentation, or debugging help.
context	fork
url	https://github.com/exa-labs/exa-mcp-server#agent-skills-claude-skills

Code Context (Exa)

Tool Restriction (Critical)

ONLY use get_code_context_exa. Do NOT use other Exa tools.

Token Isolation (Critical)

Never run Exa in main context. Always spawn Task agents:

• Agent calls get_code_context_exa
• Agent extracts the minimum viable snippet(s) + constraints
• Agent deduplicates near-identical results (mirrors, forks, repeated StackOverflow answers) before presenting
• Agent returns copyable snippets + brief explanation
• Main context stays clean regardless of search volume

When to Use

Use this tool for ANY programming-related request:

• API usage and syntax
• SDK/library examples
• config and setup patterns
• framework "how to" questions
• debugging when you need authoritative snippets

Inputs (Supported)

get_code_context_exa supports:

• query (string, required)
• tokensNum (number, optional; default ~5000; typical range 1000–50000)

Query Writing Patterns (High Signal)

To reduce irrelevant results and cross-language noise:

• Always include the programming language in the query.
  • Example: use "Go generics" instead of just "generics".
• When applicable, also include framework + version (e.g., "Next.js 14", "React 19", "Python 3.12").
• Include exact identifiers (function/class names, config keys, error messages) when you have them.

Dynamic Tuning

Token strategy:

• Focused snippet needed → tokensNum 1000–3000
• Most tasks → tokensNum 5000
• Complex integration → tokensNum 10000–20000
• Only go larger when necessary (avoid dumping large context)

Output Format (Recommended)

Return:

1. Best minimal working snippet(s) (keep it copy/paste friendly)
2. Notes on version / constraints / gotchas
3. Sources (URLs if present in returned context)

Before presenting:

• Deduplicate similar results and keep only the best representative snippet per approach.