ace-search

name	ace-search
description	Use ACE (mcp__ace-tool__search_context + enhance_prompt) as the primary code search and prompt enhancement tool. For exploring unfamiliar codebases, finding implementations by behavior, understanding architecture, or before editing code. Prefer over Grep/Glob for semantic and structural queries. Chinese triggers: 代码搜索, 找实现, 找代码, 搜代码, 哪里实现的, 怎么实现的, 代码在哪, 架构, 调用链, 入口在哪, ace搜索.

ACE — Augment Context Engine

ACE provides two tools. Use BOTH, not just search.

Tool 1: search_context — Semantic Code Search

Real-time indexed, cross-language, understands code structure and behavior.

When to Use (INSTEAD of Grep/Glob)

• Exploring unfamiliar codebases ("where does auth happen?")
• Finding implementations by behavior, not name ("file upload chunk merging")
• Understanding architecture, data flow, or call chains
• Before editing any code — find all related symbols first
• Before starting any task — recon the codebase to understand scope

When NOT to Use (use Grep/Glob instead)

• Exact string match (error messages, config values, literal strings)
• Finding ALL references to a known identifier (grep guarantees completeness)
• Searching within a specific known file (just Read it)

Query Construction

Format: Natural language intent + Keywords: specific_identifiers

Good queries — describe behavior, not names:

"How the server handles chunk merging during file upload Keywords: upload, chunk, merge, FileService"
"Where cached data is refreshed after user permissions change Keywords: permission, cache, refresh"
"Initialization flow of message queue consumers at startup Keywords: mq, consumer, init, subscribe"
"How configuration hot-reload is triggered and applied Keywords: config, reload, hot update"

Bad queries — too vague or too literal:

"Find class Foo"              → use Grep: class Foo
"Show me foo.py"              → use Read
"Find all references to bar"  → use Grep: bar

Mandatory Workflows

These are not suggestions — treat them as requirements.

Pre-Task Reconnaissance

Before starting ANY non-trivial task in an unfamiliar area:

search_context(
  project_root_path="/path/to/project",
  query="high-level architecture and main entry points of [feature area] Keywords: [domain terms]"
)

This prevents "writing code in the dark" — understand the landscape first.

Pre-Edit Deep Dive

Before editing a file, search for ALL symbols involved in the change in ONE call:

search_context(
  project_root_path="/path/to/project",
  query="detailed information about [ClassA.methodX], [ClassB], and [InterfaceC] —
         I need to understand how they interact because I'm going to [describe change].
         Keywords: ClassA, methodX, ClassB, InterfaceC"
)

One comprehensive query > multiple narrow queries. Include:

• Classes/methods you'll call
• Interfaces you'll implement
• Types you'll use as parameters/return values
• Related tests

Bug Investigation

When debugging, search for the behavior, not the symptom:

search_context(
  project_root_path="/path/to/project",
  query="how [feature] processes [input type] and what validation/error handling exists Keywords: [error-related terms]"
)

---

Tool 2: enhance_prompt — Codebase-Aware Prompt Enhancement

Takes a vague user request, combines it with codebase context and conversation history, and generates a detailed, actionable prompt. Opens a Web UI for user review.

When to Use

• User message contains -enhance or -enhancer flag (case-insensitive)
• User explicitly asks to "enhance my prompt" or "improve this requirement"

Usage

enhance_prompt(
  prompt="the user's original request",
  conversation_history="recent 5-10 turns of conversation as string",
  project_root_path="/path/to/project"
)

Workflow

1. User writes a rough requirement (e.g., "Add a login page -enhance")
2. You call enhance_prompt with the requirement + conversation context
3. Tool analyzes codebase to understand existing patterns, tech stack, conventions
4. Web UI opens — user reviews and confirms the enhanced prompt
5. You execute based on the enhanced, context-rich prompt

This is especially powerful for:

• New feature requests in unfamiliar codebases
• Ambiguous requirements that need grounding in existing code patterns
• Ensuring new code follows existing conventions

---

Decision Matrix

Scenario	Tool
"How is this feature implemented?"	search_context
"Add a new feature -enhance"	enhance_prompt → then search_context
Find exact string ERROR_CODE_42	Grep
Find all *.test.ts files	Glob
Read a known file	Read
Understand how module X works	search_context
About to edit service.py	search_context first, THEN edit
Debug a failing test	search_context for the behavior, then Read the test

---

Tips

• Always use absolute paths for project_root_path
• One rich query > many thin queries — describe everything you need in one call
• Describe behavior, not names — "how auth tokens are validated" beats "find validateToken"
• Include Keywords for precision — append concrete identifiers after the natural language description
• ACE reflects disk state only — no git history, no uncommitted-but-unstaged changes