Tips & Tricks

A collection of practical tips for getting better results from CodeAlive across all AI agents.

Prompting Strategies

Be specific with domain terms

Use the actual names from your codebase — class names, service names, module names. The more specific you are, the more relevant the results.

Instead of…	Try…
“auth code"	"authentication middleware in the Express API gateway"
"database stuff"	"Prisma schema for the User model"
"error handling"	"how does OrderService handle payment failures?"
"the API"	"the /api/v2/users REST endpoint”

Ask about patterns, not just code

CodeAlive understands architectural patterns, not just individual files. Ask higher-level questions:

"How does error handling work across the API layer?"
"What pattern do we use for database transactions?"
"How are background jobs structured in this project?"

Make chat questions self-contained

Tool API chat is stateless. Include the relevant findings and constraints again when you drill deeper:

First: "How does the payment flow work?"
Next: "Given the payment flow above, what happens if the payment provider returns a timeout?"
Next: "Using the timeout path above, show me the retry logic for that case"

Combine search and chat

Start with semantic_search or grep_search to gather evidence, then use chat only if you still need a synthesized explanation:

Search: "payment webhook handler"
Grep: "refund"
Chat: "Explain how the payment webhook handler processes refund events"

This gives chat more focused context and produces better answers.

Search Optimization

Choose the right search tool

Use the canonical pair by default:

Tool	When to use	Speed
`semantic_search`	Meaning-based retrieval, architecture clues, related patterns	Fast
`grep_search`	Exact strings, identifiers, log lines, regex patterns	Fast
`codebase_search`	Legacy compatibility only	Varies

Scope searches to repositories

If you have multiple repositories indexed, scope your search to avoid noise:

"Search for authentication logic in the backend repository"
"Find React components in the frontend repo"

Use get_data_sources to see available repositories and workspaces. Pass your task as its query argument to get only the relevant sources, each with a relevanceReason — useful when many repositories are indexed.

Search first, then chat

semantic_search and grep_search are the default tools. chat is slower, can take up to 30 seconds, and uses more tokens but synthesizes a complete answer.Recommended flow:

Search to find where relevant code lives
Fetch or read the most relevant artifacts
Chat only when you need synthesis, explanation, or analysis

Workspace Organization

Keep workspaces focused

Indexing too many unrelated repositories in one workspace adds noise to search results. If you’re getting irrelevant hits, split your workspace or scope your queries to specific repos.

Cost & Token Optimization

Use search before chat

semantic_search and grep_search are significantly cheaper and faster than chat. Use search for lookups and locating code. Reserve chat for synthesis and analysis.If your agent supports subagents and you need maximum reliability or depth, prefer a subagent-driven workflow built on semantic_search and grep_search instead of jumping straight to chat.

Tool	Best for	Relative cost
`get_data_sources`	Listing repos, checking status	Minimal (low with `query`, which runs an AI relevance filter)
`semantic_search` / `grep_search`	Finding code, locating evidence	Low
`chat`	Explanations, analysis, synthesized answers	Higher

Write focused queries

Shorter, more focused queries return better results and use fewer tokens. Instead of explaining your entire situation, ask a direct question:

Instead of…	Try…
”I’m working on a feature where users can reset their password and I need to understand how the current password reset flow works so I can modify it"	"How does the password reset flow work?”

Agent-Specific Tips

Claude Code

Use claude mcp add for the simplest setup — one command, done
Add CodeAlive to your custom instructions so Claude uses it automatically
Works with both remote MCP and local Docker

Cursor

Composer Agent mode automatically uses MCP tools when relevant
Use .cursor/mcp.json for project-specific config (shareable with team)
Add CodeAlive patterns to .cursorrules for consistent AI behavior

Windsurf

Config uses serverUrl — not url — different from other agents
Supports Streamable HTTP transport
Check Windsurf’s MCP settings page for connection status

Cline

Supports auto-approve for MCP tools to reduce confirmation prompts

Add CodeAlive rules to .cline/rules.md for automatic context usage:

Always search CodeAlive before writing new code.
Follow existing patterns found in the codebase.

VS Code + GitHub Copilot

Native MCP support is GA in VS Code
Configure in .vscode/mcp.json for project-level setup
Agent mode in Copilot Chat automatically discovers MCP tools

What’s Next

Example Workflows

See real-world usage patterns

Troubleshooting

Solutions for common issues

Getting Started

Features

Model Context Protocol

Agent Skills & Plugins

Instructing Agents

Prompting Strategies

Search Optimization

Workspace Organization

Cost & Token Optimization

Agent-Specific Tips

What’s Next

Example Workflows

Troubleshooting

​Prompting Strategies

​Search Optimization

​Workspace Organization

​Cost & Token Optimization

​Agent-Specific Tips

​What’s Next

Example Workflows

Troubleshooting

Prompting Strategies

Search Optimization

Workspace Organization

Cost & Token Optimization

Agent-Specific Tips

What’s Next