Not Ace Tool Rs

One MCP server that gives AI coding agents searchable code context, planning, memory, review, docs, and web research.

English | 简体中文

not-ace-tool-rs is a high-performance tool server for AI coding assistants. It exposes codebase search, AI-augmented goal planning, intelligent workflow tools, persistent memory/learning, taste preferences, and web research through the Model Context Protocol and a universal one-shot CLI mode.

Quick Start

Run the server once with your API URL and auth token:

npx not-ace-tool-rs --base-url <API_URL> --token <AUTH_TOKEN>

What happens: npx downloads the right platform package, starts not-ace-tool-rs, connects it to your API, and serves MCP tools over stdio. For day-to-day use, add it to your MCP client so your assistant can launch it automatically. For one-off commands without MCP, see CLI Mode.

Supports Linux (x64/ARM64), macOS (x64/ARM64), and Windows (x64/ARM64).

What's New in v0.7

• Universal CLI mode: all 25 tools can run directly from the command line with --tool and --input, without starting an MCP client.
• Added a skills/ directory with 25 tool-specific SKILL.md files. Each skill explains when to use the tool and how to run it through MCP, CLI, or manually.
• generate_docs now accepts any free-form natural-language scope, such as REST API documentation, security audit report, onboarding guide for new developers, or 用中文写项目入门指南.
• The smart-agent tools diagnose, code_review, and generate_docs are available in both MCP and CLI mode.
• Lenient Serde argument parsing remains supported for complex arguments: arrays and objects can arrive as native JSON values or as stringified JSON.

What It Does

The table below is the fastest way to understand the tool surface. Start with the capability you need, then jump to the tool reference for exact parameters.

Capability	Tools	Description
Code Search	search_context	Semantic codebase search using natural language queries
Goal Planning	goal, goal_phase	AI-augmented deep planning with phase decomposition, 3-strike self-healing, and audit
Workflow	clarify, handoff, improve, triage	Requirement clarification, cross-session context transfer, architecture analysis, backlog triage
Prompt Enhancement	enhance_prompt	Enriches prompts with codebase context and conversation history
Memory & Learning	memory, recall, memory_forget, memory_list, memory_profile, memory_event, batch_learn	Persistent memory with reflection and learning
Taste Preferences	taste_context, taste_profile	Learns and applies user coding style preferences
Project Q&A	ask_project	Answers questions grounded in codebase context and memory
Smart Debugging	diagnose	Diagnoses errors using memory, code search, and web — auto-saves solutions
Code Review	code_review	Reviews code diffs for risks, style consistency, and issues
Doc Generation	generate_docs	Generates free-form documentation and reports from codebase analysis
Web Research	web_search, search_papers, search_images, web_fetch	Web search, academic paper search, image search, page fetching

MCP Client Setup

Claude Code

claude mcp add-json not-ace-tool --scope user '{
  "type": "stdio",
  "command": "npx",
  "args": ["not-ace-tool-rs", "--base-url", "https://your-api.example.com", "--token", "your-token"]
}'

Add permissions to ~/.claude/settings.json:

{
  "permissions": {
    "allow": [
      "mcp__not-ace-tool__search_context",
      "mcp__not-ace-tool__enhance_prompt",
      "mcp__not-ace-tool__memory",
      "mcp__not-ace-tool__recall",
      "mcp__not-ace-tool__memory_forget",
      "mcp__not-ace-tool__memory_list",
      "mcp__not-ace-tool__memory_profile",
      "mcp__not-ace-tool__memory_event",
      "mcp__not-ace-tool__batch_learn",
      "mcp__not-ace-tool__taste_context",
      "mcp__not-ace-tool__taste_profile",
      "mcp__not-ace-tool__ask_project",
      "mcp__not-ace-tool__goal",
      "mcp__not-ace-tool__goal_phase",
      "mcp__not-ace-tool__clarify",
      "mcp__not-ace-tool__handoff",
      "mcp__not-ace-tool__improve",
      "mcp__not-ace-tool__triage",
      "mcp__not-ace-tool__web_search",
      "mcp__not-ace-tool__search_papers",
      "mcp__not-ace-tool__search_images",
      "mcp__not-ace-tool__web_fetch",
      "mcp__not-ace-tool__diagnose",
      "mcp__not-ace-tool__code_review",
      "mcp__not-ace-tool__generate_docs"
    ]
  }
}

Claude Desktop

Add to config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "not-ace-tool": {
      "command": "npx",
      "args": ["not-ace-tool-rs", "--base-url", "https://your-api.example.com", "--token", "your-token"]
    }
  }
}

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.not-ace-tool]
command = "npx"
args = ["not-ace-tool-rs", "--base-url", "https://your-api.example.com", "--token", "your-token", "--transport", "lsp"]
env = { RUST_LOG = "info" }
startup_timeout_ms = 60000

CLI Mode

All 25 tools can run as one-off command-line calls without MCP. Use the same tool names and JSON parameter names shown in the tool reference.

not-ace-tool-rs --tool <name> --input '<json>'

In a plain shell, add the same connection flags used in Quick Start:

not-ace-tool-rs --base-url <API_URL> --token <AUTH_TOKEN> --tool <name> --input '<json>'

Examples below show the tool-specific part:

not-ace-tool-rs --tool diagnose --input '{"error_message": "TypeError: Cannot read properties of undefined"}'
not-ace-tool-rs --tool code_review --input '{"diff": "..."}'
not-ace-tool-rs --tool generate_docs --input '{"project_root_path": ".", "scope": "REST API docs"}'
not-ace-tool-rs --tool recall --input '{"query": "auth flow"}'
not-ace-tool-rs --tool web_search --input '{"query": "rust async best practices"}'

Skills

The repository includes a skills/ directory with 25 SKILL.md files, one per tool. Each skill explains when to use the tool, how to call it through MCP, how to run it through CLI mode, and how to complete the task manually if the tool is unavailable.

These skill files are for source checkouts and agent workflows. They are in the repo, but they are not included in the npm package.

Tool Reference

The reference is grouped by the job you are trying to do. Each group starts with why you would use it, then lists the exact tool names and parameter names for MCP calls or CLI --input JSON.

Codebase Context

Use these tools when the assistant needs grounded project knowledge: either raw code snippets (search_context) or a synthesized answer (ask_project). They are the best starting point when file locations are unknown.

search_context

Natural-language search over the current codebase. Use it when you know the behavior or workflow you need, but not the exact files.

Parameter	Type	Required	Description
project_root_path	string	Yes	Project root path
query	string	Yes	Natural language search query

ask_project

Ask a project question and get a concise answer grounded in codebase retrieval plus project memory. Use search_context instead when you need raw code snippets.

Parameter	Type	Required	Description
project_root_path	string	Yes	Project root path
question	string	Yes	Your question
container_tag	string	No	Memory container tag

Planning

Use planning tools when a change needs sequencing, verification, and a durable execution trail instead of a one-shot answer.

AI-augmented planning is inspired by supergoal. When creating a goal, the server automatically retrieves relevant code context, loads project memory and taste preferences, then generates a deep plan via LLM with risk analysis, phase decomposition, and self-critique.

goal

Manage the goal lifecycle.

Parameter	Type	Required	Description
action	string	Yes	create, list, status, audit, complete, cancel, plan
goal_id	string	Most actions	Goal ID
project_root_path	string	create	Project root path
name	string	create	Goal name
requirement	string	create	What you want to achieve
container_tag	string	No	Memory container tag
audit_evidence	object	audit	Evidence for final audit
clarify_session_id	string	create (optional)	Use a resolved clarify session's brief as enriched requirement
status_filter	string	list	Filter by status

Goal lifecycle: created → planning → planned → in_progress → auditing → completed

goal_phase

Manage phase execution with 3-strike self-healing.

Parameter	Type	Required	Description
action	string	Yes	start, verify, fail, heal, list
goal_id	string	Yes	Goal ID
phase_id	string	start/verify/fail/heal	Phase ID
evidence	object	verify	Verification evidence
error_detail	string	fail	Failure description
spec	object	heal	Healing specification
notes	string	heal	Healing notes
metadata	object	heal (optional)	Healing metadata

Phase lifecycle: pending → in_progress → verifying → done

On failure: strike 1 → memory-assisted retry suggestion → strike 2 → heal spec required → strike 3 → escalated

Workflow Coordination

Use these when work is not ready to execute yet, context needs to move between sessions, or a backlog needs routing. The workflow tools are inspired by AI Hero's skill system, and they use code index, memory, and taste preferences to improve future sessions.

clarify

AI-driven requirement clarification with context-aware recommendations. Multi-round Q&A produces a structured brief for goal creation and captures terminology and architecture decisions into memory.

Parameter	Type	Required	Description
action	string	Yes	start, answer, status, list
project_root_path	string	start	Project root path
requirement	string	start	Requirement to clarify
container_tag	string	No	Memory container tag
session_id	string	answer, status	Session ID
answers	array	answer	[{question_id, answer}]

How it works:

1. start — indexes project, loads memory/taste, LLM generates questions with recommendations
2. answer — submit answers, LLM decides to ask more or produce a brief
3. Brief output includes refined requirement, decisions, terminology, ADRs, risk flags
4. Terminology and ADRs are automatically written to memory for future sessions

handoff

Create and load context handoffs between agent sessions. Stores compressed context in memory, enriched with project terminology and preferences.

Parameter	Type	Required	Description
action	string	Yes	create, load, list
context	string	create	Current session context
purpose	string	create	What the next session should do
artifacts	array	create (optional)	Relevant file paths
container_tag	string	No	Memory container tag
handoff_id	string	load	Handoff ID

improve

Analyze codebase architecture for improvement opportunities. Surfaces friction points based on code structure, project history, and user preferences.

Parameter	Type	Required	Description
action	string	Yes	analyze, detail
project_root_path	string	analyze	Project root path
container_tag	string	No	Memory container tag
candidate_id	string	detail	Improvement candidate ID
focus	string	analyze (optional)	Narrow analysis to specific area

triage

Classify requirements and assess readiness for agent execution. Routes items to clarify, goal create, or human review.

Parameter	Type	Required	Description
action	string	Yes	assess, detail
project_root_path	string	assess	Project root path
container_tag	string	No	Memory container tag
items	array	assess	[{id, title, description}]
item_id	string	detail	Item ID

Triage states: needs-clarify → route to clarify | ready → route to goal create | needs-human → requires human judgment

Prompt, Memory & Preferences

Use these tools to make assistant behavior more consistent over time: improve a prompt before execution, save durable project knowledge, recall past decisions, and apply learned coding style preferences.

enhance_prompt

Enrich a prompt with codebase context and recent conversation so it becomes clearer and more actionable. Use it only when the user explicitly asks to enhance a prompt or uses -enhance / -enhancer markers.

Parameter	Type	Required	Description
prompt	string	Yes	Original prompt
conversation_history	string	Yes	Recent conversation context
project_root_path	string	No	Project root path

Supports Claude, OpenAI, Gemini, and built-in endpoints via ACE_ENHANCER_ENDPOINT.

Memory & Learning tools

Memory tools turn useful context into durable knowledge. Use them to save project facts, recall similar past work, remove stale facts, list or export memory, and import learning data in bulk.

Tool	Use it when	Required parameters	Optional parameters
memory	Save a memory	content (string)	container_tag (string), metadata (object), task_type (string)
recall	Search memories	query (string)	container_tag (string), limit (integer), threshold (number), search_mode (string), include_profile (boolean)
memory_forget	Forget a memory by ID or exact content	id (string) or content (string)	container_tag (string)
memory_list	List stored memories with pagination	—	container_tag (string), limit (integer, default: 20), page (integer), include_content (boolean, default: true)
memory_profile	Export the memory profile	—	container_tag (string), q (string), threshold (number)
memory_event	Submit a learning event	type (string), content (string)	container_tag (string), source (string, default: mcp/client), metadata (object)
batch_learn	Batch import prompts for learning	source (string), prompts (array)	container_tag (string)

Accepted memory_event.type values: prompt_submitted, assistant_response_accepted, assistant_response_rejected, user_edited_code, user_reverted_change, review_comment_added, preference_corrected, session_imported.

Taste Preferences

Taste tools expose the learned coding style profile so agents can match the user's preferred patterns, tone, and implementation choices.

Tool	Use it when	Required parameters	Optional parameters
taste_context	Get taste preferences relevant to current task	—	query (string), category (string), container_tag (string), limit (integer)
taste_profile	Export the full taste profile	—	container_tag (string), format (markdown, json)

Implementation Support

Use these tools while shipping code: debug failures, review changes, and produce documentation that reflects the actual codebase and project memory.

diagnose

Diagnose errors using memory, code search, and web research. The tool searches project memory for similar past errors, searches the codebase for related code, searches the web for community solutions, then synthesizes a diagnosis via LLM. Automatically saves the error-solution pair to memory for future use.

Parameter	Type	Required	Description
error_message	string	Yes	Error message, stack trace, or error description
project_root_path	string	No	Project root path for code context
container_tag	string	No	Memory container tag

code_review

Review code diffs for risks, style consistency, and issues. The tool analyzes code changes against codebase context, project memory, and team coding style preferences (taste profile). Returns a structured review with risk level, issues, and style consistency check. Automatically saves review to memory.

Parameter	Type	Required	Description
diff	string	Yes	Code diff (unified diff or code snippet)
context	string	No	Change description (PR description, etc.)
project_root_path	string	No	Project root path for code context
container_tag	string	No	Memory container tag

generate_docs

Generate documentation or reports from codebase search and project memory. The scope can be any natural-language request, so it can produce project overviews, REST API documentation, security audit reports, onboarding guides, Chinese documentation, or other focused docs. Generated docs are automatically saved to memory.

Parameter	Type	Required	Description
project_root_path	string	Yes	Project root path
scope	string	No	Free-form documentation request, e.g. REST API documentation, security audit report, onboarding guide for new developers, or 用中文写项目入门指南
format	string	No	Output format (default: markdown)
container_tag	string	No	Memory container tag

Web Research

Use these tools when the answer depends on current docs, community examples, papers, images, or a specific page outside the repository. They complement code search instead of replacing it.

Tool	Use it when	Required parameters	Optional parameters
web_search	Web search in quick, broad, or deep modes	query (string)	mode (search, broad, deep, default: search), count (integer, default: 5, max: 20), max_rounds (integer, default: 3, max: 5)
search_papers	Academic paper search across arXiv and SSRN	query (string)	source (arxiv, ssrn, all, default: all), count (integer, default: 5), extract_content (boolean, default: true)
search_images	Image search across the web	query (string)	count (integer, default: 5)
web_fetch	Fetch and extract web page content	url (string)	format (markdown, text, default: markdown)

CLI Options

Argument	Description
--base-url	API base URL
--token	Authentication token
--transport	Transport framing: auto (default), lsp, line
--tool	Run a single tool by name and exit
--input	JSON arguments for --tool mode (defaults to {})
--index-only	Index current directory and exit
--enhance-prompt	Enhance a prompt and exit
--container-tag	Memory container tag (default: default)
--taste-profile	Export taste profile and exit
--batch-learn	Import prompts from file and exit
--memory-event	Submit a memory event and exit

Environment Variables

Variable	Description
ACE_ENABLE_MEMORY_TOOLS	Enable/disable memory tools (default: enabled)
ACE_ENABLE_TASTE_TOOLS	Enable/disable taste tools (default: enabled)
ACE_ENABLE_GOAL_TOOLS	Enable/disable goal/goal_phase/ask_project/diagnose/code_review/generate_docs tools (default: enabled)
ACE_ENABLE_WORKFLOW_TOOLS	Enable/disable clarify/handoff/improve/triage tools (default: enabled)
ACE_CONTAINER_TAG	Default memory container tag
PROMPT_ENHANCER	Set to disabled to hide enhance_prompt
ACE_ENHANCER_ENDPOINT	Prompt enhancer backend: new, old, claude, openai, gemini
PROMPT_ENHANCER_BASE_URL	Third-party API base URL
PROMPT_ENHANCER_TOKEN	Third-party API key
PROMPT_ENHANCER_MODEL	Third-party model override
RUST_LOG	Log level: info, debug, warn

Build from Source

git clone https://github.com/linze0721/notace-tool-rs.git
cd notace-tool-rs
cargo build --release
# Binary: target/release/not-ace-tool-rs

Requires Rust 1.70+.

License

Dual licensed under GPLv3 (non-commercial) and a commercial license. See LICENSE and LICENSE-COMMERCIAL.

Commercial use (workplace, SaaS, consulting) requires a commercial license. Contact: [email protected]