prompte-mcp
An MCP server that automatically enhances user prompts by applying advanced engineering techniques like chain-of-thought and few-shot reasoning based on identified intent. It optimizes technique selection through local learning and integrates directly into Claude sessions to improve output quality without additional API costs.
README
prompte-mcp
An MCP server that enhances your prompts before Claude processes them — automatically applying chain-of-thought, few-shot, tree-of-thought, and other prompt engineering techniques based on what you're asking.
you type: "fix this null pointer crash"
claude sees: "Work through this using the ReAct pattern — alternate between
Thought (reasoning) and Action (what you would do), then give
a final Answer.
fix this null pointer crash"
Setup
No API key needed. Prompte runs entirely on your existing Claude Code or Codex session.
git clone https://github.com/AlanRoybal/prompte-mcp
cd prompte-mcp
node bin/setup.js
The setup script handles everything:
- Registers the MCP server in
~/.claude/settings.json - Installs the
UserPromptSubmithook (automatic enhancement on every prompt) - Creates
~/.prompte/config.jsonwith defaults
Then restart Claude Code.
Flags
node bin/setup.js --yes # accept all defaults, no prompts
node bin/setup.js --dry-run # preview changes without writing anything
Manual setup
If you prefer to edit ~/.claude/settings.json directly:
{
"mcpServers": {
"prompte": {
"command": "node",
"args": ["/path/to/prompte-mcp/bin/prompte-mcp.js"]
}
},
"hooks": {
"UserPromptSubmit": [
{
"matcher": "",
"hooks": [
{
"type": "command",
"command": "python3 /path/to/prompte-mcp/hooks/user-prompt-submit.py"
}
]
}
]
}
}
How it works
The MCP server handles classification and technique selection. Claude Code (your existing session) does the actual enhancement — no separate API calls, no extra costs.
your prompt
│
▼
┌─────────────┐
│ Classifier │ keyword heuristics (no API call)
│ │ → intent: debugging / reasoning / generation / ...
└──────┬──────┘
│ technique affinity scores
▼
┌─────────────┐
│ Scorer │ affinity × your learned acceptance rate
│ │ → selects best technique
└──────┬──────┘
│ techniqueInstruction
▼
Claude Code ←── applies the technique using its own intelligence
│
▼
response
The scorer learns from you. Acceptance rates per technique are tracked in ~/.prompte/ — techniques you skip get demoted over time.
Two modes
Automatic (hook)
The UserPromptSubmit hook fires on every prompt silently — no tool call, no interruption. Claude receives the enhanced version without you doing anything.
Prefix a prompt with * to bypass:
* just answer this exactly as asked
Interactive (MCP tools)
When Claude calls enhance_prompt, it shows you the enhancement and waits for your decision before answering:
I've selected the Chain of Thought technique for this (debugging).
Original: why does my function crash when the list is empty?
Enhanced: Think through this step-by-step before giving your final
answer. Show your reasoning explicitly.
why does my function crash when the list is empty?
[A] Accept [E] Edit [S] Skip [Q] Quit
Reply with a / e / s / q (or just say "accept", "skip", etc.):
| Reply | What happens |
|---|---|
a / accept |
Claude answers using the enhanced prompt |
e / edit |
Paste your revised version, Claude uses that |
s / skip |
Claude answers your original prompt, no technique |
q / quit |
Claude stops, does nothing |
Set autoAccept: true in ~/.prompte/config.json to skip the confirmation and apply silently.
Claude Code can also call these tools directly during a session:
| Tool | What it does |
|---|---|
enhance_prompt |
Classify intent, select best technique, return techniqueInstruction for Claude to apply |
list_techniques |
All 8 techniques with your acceptance stats |
get_stats |
Session totals + current config |
record_feedback |
Mark an enhancement helpful/not (trains technique weights) |
get_config |
Read ~/.prompte/config.json |
set_config |
Write a config value |
The CLAUDE.md in this repo tells Claude when to call enhance_prompt automatically — on debugging, reasoning, generation, architecture, and review prompts.
The 8 techniques
| Technique | Best for | What it adds |
|---|---|---|
| Chain of Thought | Debugging, reasoning | Step-by-step reasoning before answering |
| Few-Shot | Generation, review | Concrete example to anchor output |
| Tree of Thought | Decisions, architecture | 3 approaches with pros/cons, then a recommendation |
| Meta-Prompting | Architecture, generation | Restate understanding of the goal before answering |
| Role Prompting | Review, generation | Senior software engineer framing |
| Self-Consistency | Reasoning, debugging | Verify from a different angle, correct if wrong |
| Step-Back | Explanation, reasoning | Consider broader context and first principles first |
| ReAct | Debugging, multi-step | Interleaved Thought / Action / Observation steps |
Configuration
~/.prompte/config.json:
{
"enabled": true,
"autoAccept": false,
"bypassPrefix": "*",
"preferredTechniques": [],
"disabledTechniques": [],
"llmClassifier": true,
"maxPromptLength": 4000
}
| Key | Default | Description |
|---|---|---|
enabled |
true |
Master switch |
bypassPrefix |
"*" |
Prompt prefix to skip enhancement |
preferredTechniques |
[] |
Boost these techniques |
disabledTechniques |
[] |
Never use these techniques |
maxPromptLength |
4000 |
Skip enhancement above this length |
Per-project overrides: drop a .prompte file anywhere in your project tree (or a parent directory). Values override the global config.
Project structure
prompte-mcp/
├── bin/
│ ├── prompte-mcp.js MCP server
│ └── setup.js setup script
├── src/
│ ├── classifier/ intent classification (LLM + keyword fallback)
│ ├── techniques/ 8 technique definitions
│ ├── engine/ classify → score → select → rewrite
│ └── config/ ~/.prompte/ storage and acceptance rate learning
├── hooks/
│ └── user-prompt-submit.py UserPromptSubmit hook
└── CLAUDE.md tells Claude when to call enhance_prompt
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。