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prompyai-mcp

v0.1.4

Published

Context-aware prompt intelligence MCP server for Claude CLI — scores your prompts against your real codebase

Downloads

569

Readme

prompyai-mcp

Context-aware prompt intelligence MCP server for Claude CLI. Scores your prompts against your real codebase and rewrites them with AI.

Install

claude mcp add prompyai -- npx prompyai-mcp serve

No sign-up. No config. Works immediately. Requires Node.js 20+.

What It Does

Every time you write a prompt in Claude CLI, PrompyAI automatically:

  1. Scores it (0–100) across Specificity, Context Completeness, Task Clarity, and File & Folder Anchoring
  2. Suggests improvements tailored to your project
  3. Rewrites the prompt with real file paths, verified symbols, and codebase context
Prompt Score: 43/100 [D]

  Specificity         3/25  ==..............
  Context Completeness 13/25  ========........
  Task Clarity        15/25  =========.......
  File & Folder Anchoring 10/25  ======..........

Key improvements:
  1. Expand your prompt with more context
  2. Add file paths using @mentions
  3. Specify what format you expect the output in

Try something more like:

  "Build the VS Code extension in packages/vscode-extension/ that integrates
   with the PrompyAI MCP server at packages/mcp-server/. It should provide
   real-time prompt scoring in the editor sidebar, show score breakdowns
   (specificity, context, clarity, anchoring), and offer a 'rewrite prompt'
   action. Use the shared types from packages/shared/."

AI Enhancement — Works for Everyone

PrompyAI uses a two-layer architecture:

  • API key users (ANTHROPIC_API_KEY set) → PrompyAI calls Claude Haiku directly for fast AI rewrites
  • Subscription users (no API key) → PrompyAI returns rich codebase context to Claude, which generates the enhanced prompt using your existing session

All users get AI-enhanced output. No separate API key required.

How It Knows Your Codebase

PrompyAI indexes your project locally:

  • File tree — structure, key folders, recently modified files
  • Tech stack — language, framework, ORM, test runner detection
  • Code symbols — functions, classes, interfaces extracted via the TypeScript Compiler API
  • Git state — branch, dirty files, recent changes
  • Session history — reads Claude Code conversation transcripts for multi-turn context

All indexing happens locally. Nothing is sent externally (except optional anonymous telemetry).

MCP Tools

evaluate_prompt

Auto-called on every user message. Scores your prompt against your project.

| Parameter | Required | Description | |-----------|----------|-------------| | prompt | yes | The prompt text to evaluate | | workspace_path | yes | Absolute path to your project | | active_file | no | Currently open file path | | session_id | no | Claude Code session ID for multi-turn context |

get_context

Returns your project summary: tech stack, recent files, key folders.

| Parameter | Required | Description | |-----------|----------|-------------| | workspace_path | yes | Absolute path to your project |

prompyai_toggle

Turn auto-evaluation on or off. Say "pause prompyai" or "enable prompyai" in Claude CLI.

Desktop Config

Alternatively, add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "prompyai": {
      "command": "npx",
      "args": ["prompyai-mcp", "serve"]
    }
  }
}

Environment Variables

| Variable | Required | Description | |----------|----------|-------------| | ANTHROPIC_API_KEY | No | Enables direct AI suggestions via Claude Haiku (optional) | | PROMPYAI_TELEMETRY | No | Set to false to opt out of anonymous telemetry |

CLI Commands

prompyai serve              # Start the MCP server (default)
prompyai doctor             # Run environment diagnostics
  --workspace <path>        # Workspace to check (default: cwd)

Programmatic API

import { WorkspaceIndexer, ScoringEngine } from 'prompyai-mcp';

const indexer = new WorkspaceIndexer();
const scorer = new ScoringEngine();

const fingerprint = await indexer.getFingerprint('/path/to/project');
const result = await scorer.evaluate('fix the auth', fingerprint);

console.log(result.display); // Pre-formatted score output
console.log(result.total);   // 0-100
console.log(result.grade);   // A-F

Links

License

MIT