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MCP 客户端集成

PromptFlowEngine 支持 Model Context Protocol，因此任何 MCP 客户端（GitHub Copilot 智能体模式、Claude Desktop、Claude Code、Cursor 或你自己的智能体）都可以把引擎当作工具调用。这样，你的助手就能把检查、重构和脱敏提示词当作自己工作中的一个步骤，而不用你手动去做。

关于术语：你要安装的是一个 MCP 服务器（@promptflow/mcp-server v2.1.0，服务器名称 promptflow-engine）。连接它的 AI 工具是 MCP 客户端。

Claude Code、Windsurf 和 Gemini CLI 的设置、TypeScript 客户端示例和故障排查：连接 PromptFlowEngine。

连接方式

连接

MCP client (Copilot, Claude, Cursor, your agent)
   │  spawns a local process and talks JSON-RPC over stdio
   ▼
promptflow-mcp  (node packages/mcp-server/dist/bin.js)
   │  validates arguments, runs the engine in-process
   ▼
@promptflow/engine   no network, no model calls, no files read

传输方式仅限 stdio。服务器作为客户端的子进程运行在你的电脑上，只读取收到的参数，不发起任何网络调用。

身份验证

既不需要，也不支持：没有 API 密钥、账号或 token。访问由你的操作系统控制，因为只有你启动的进程才能通过标准输入输出与服务器通信。没有托管的或 HTTP 的 MCP 端点；如需远程使用，请改为调用 HTTP API。

配置

从 PromptFlowEngine 仓库的检出代码构建（npm 发布尚在进行中），然后让客户端指向 dist/bin.js：

shell

pnpm --filter @promptflow/engine build && pnpm --filter @promptflow/mcp-server build

VS Code / Copilot: .vscode/mcp.json

{
  "servers": {
    "promptflow": {
      "type": "stdio",
      "command": "node",
      "args": ["/absolute/path/to/checkout/packages/mcp-server/dist/bin.js"]
    }
  }
}

Claude Desktop、Cursor 和其他客户端

{
  "mcpServers": {
    "promptflow": { "command": "node", "args": ["/absolute/path/to/checkout/packages/mcp-server/dist/bin.js"] }
  }
}

VS Code 扩展内置了同一个服务器，并会为你写入第一条配置。

环境变量	默认值	用途
`PROMPTFLOW_MCP_MAX_PROMPT_CHARS`	`100000`	每个文本参数的最大字符数（硬上限 2,000,000）。
`PROMPTFLOW_MCP_LOG`	未设置	`debug` 会把工具名称、输入大小、结果和耗时记录到 stderr。从不记录内容。

功能：工具（19）

工具	说明
`optimize_prompt`	Optimize a prompt with deterministic, verified transforms (duplicates, filler, optional sections). Every candidate is checked to preserve all sentences, requirements, code, and variables; the original is kept if nothing is better. Returns the selected prompt, token savings, quality score, and what changed.
`optimize_prompt_pipeline`	Run the optimization pipeline and return per-stage results plus token and estimated cost metrics, in the result shape used by the PromptFlow VS Code extension.
`compress_prompt`	Remove exact duplicate sentences and extra whitespace without rewriting any wording.
`validate_prompt`	Check a prompt for missing objective or output format, ambiguity, conflicting instructions, duplicates, prompt-injection patterns, secrets, and undelimited variables. isValid is false only when a critical issue exists.
`estimate_tokens`	Count tokens and estimate input cost for a model. "exact" is true only for OpenAI models with a loaded tokenizer; other providers are approximate. Costs are estimates from a reference price table.
`analyze_prompt`	Detect intent, task type, technical domain, requested output, requirements, and entities (languages, frameworks, file paths, identifiers).
`extract_entities`	Extract languages, frameworks, libraries, cloud platforms, services, technologies, file paths, identifiers, URLs, and commands.
`summarize_context`	Extractive summary: the first N unique, non-empty sentences of a context block, in order. Does not rank or rewrite.
`score_prompt`	Score prompt quality 0-100: 100 minus a penalty per issue (critical 30, warning 10, info 3; capped at 50 with any critical issue), with per-dimension scores and suggestions.
`evaluate_prompt`	Full quality report: overall score, grade, eight quality dimensions with the checks that failed, and every finding with why it matters and how to fix it.
`compare_prompts`	Compare two prompts: token and estimated cost delta, quality delta per dimension, and which findings were resolved or introduced.
`scan_prompt_security`	Prompt-injection risk (0-100, signature-based, with threat ids), sensitive-data counts by category, undelimited template variables, and destructive operations. Never returns detected secret values.
`scan_sensitive_data`	Scan text for credentials, cloud secrets, and PII with local deterministic detectors. Returns offsets and per-category counts, never the sensitive values.
`sanitize_prompt`	Replace detected sensitive data with placeholders such as [REDACTED_API_KEY]. Returns the sanitized text and counts, never the original values. Use before sending a prompt to an external model.
`restructure_prompt`	Turn a loosely written request into a sectioned engineering prompt: Role, Objective, Context, Requirements, Constraints, Edge cases, Testing, Acceptance criteria, Expected output. Only sections with content appear. The author's clauses are kept and reordered; for software tasks, fixed engineering guidance chosen by intent and topic is added, and every line is labeled "prompt" or "engine" so additions can be reviewed. Set scaffold=false to reorganize without adding guidance.
`convert_structured_payload`	Convert a JSON payload to TOON (Token-Oriented Object Notation) and say whether to use it. Returns token counts for pretty JSON, compact JSON and TOON, a round-trip check (the TOON must decode back to the same value), and the recommendation: "toon" only when it round-trips and saves at least 5 % over compact JSON, otherwise "json". Lossless or it keeps JSON.
`simulate_cost`	Simulate what a prompt costs across models at a request volume, from catalog list prices (labelled simulated, never a bill). Optionally optimizes the prompt first and reports hypothetical savings. Flags models whose context window is too small.
`analyze_context_budget`	Measure the context sources you intend to send with a prompt: tokens and share per source, context-window usage with an output reserve, sensitive data (categories and counts only), instruction-like text in retrieved sources, and duplicates. Reads only the text passed in; never reads files.
`run_prompt_tests`	Run a regression suite against a prompt. Assertions check the prompt (contains, not_contains, matches, max_tokens, min_quality, no_finding, max_severity, has_variables, max_injection_risk, no_sensitive_data, output_format) and, when a case carries a recorded model response, the response (response_valid_json, response_has_keys, response_contains, response_max_chars). With baseline_prompt, also runs the suite on the baseline and lists cases that regressed. No model is called.

所有工具都标注为只读且幂等。前十一个保持了它们在 1.5.x 扩展中的结果字段，因此现有的智能体配置仍然可用。

资源与提示词

promptflow://rules：规则目录、维度和评分扣分。
promptflow://models：模型、token 编码和参考价格。
提示词 review-prompt：请助手评估并优化一个提示词，该提示词作为数据被包裹在标签中。

示例

在与智能体的聊天中，用自然语言提出要求，客户端会选择工具：

你

Use promptflow to restructure this before you start:
"fix login issue and make google login work and don't break anything"

客户端发送的 tools/call

{
  "name": "restructure_prompt",
  "arguments": { "prompt": "fix login issue and make google login work and don't break anything" }
}

结果（节选）

{
  "restructuredPrompt": "## Role\nAct as a senior software engineer.\n\n## Objective\nFix login issue and make Google login work.\n…",
  "intent": "debugging",
  "topics": ["authentication"],
  "counts": { "prompt": 4, "engine": 20, "codebase": 0 }
}

如需在没有客户端的情况下试用，可以用管道把 JSON-RPC 发给该进程：

shell

printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"cli","version":"1"}}}' \
  '{"jsonrpc":"2.0","method":"notifications/initialized"}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"estimate_tokens","arguments":{"prompt":"Hello, world!","model":"gpt-4o"}}}' \
  | node packages/mcp-server/dist/bin.js

错误与安全

参数通过 schema 验证。错误或过大的参数会返回带原因的工具错误（isError），而不是协议错误，这样智能体可以纠正后重试。
没有任何工具会返回检测到的密钥值：敏感数据的结果只包含类别和位置。
传给工具的提示词文本被视为数据。服务器从不执行它，review-prompt 模板会把它包裹在标签中再交给模型。
服务器没有文件、shell 或网络工具，因此遭到提示词注入的智能体无法借它访问任何东西。