npm package: @gherk/requirements-extractor
Description: MCP server that extracts, classifies and generates structured requirements from PDF documents with heuristic scanning and active validation
Latest version: 1.5.1

# @gherk/requirements-extractor

An MCP (Model Context Protocol) server that extracts, classifies, and generates structured requirement documents from PDF files using a local Ollama LLM.

## Features

- **3-Pass AI Pipeline** — Identify → Classify → Generate requirements from any PDF
- **Async Processing** — Returns a job ID immediately; poll for incremental results
- **6 Categories** — Backend, Frontend, Mobile, Infrastructure, DevOps, Non-Functional
- **Structured Output** — Generates individual markdown files and/or JSON per requirement
- **Crash-Resilient** — Files are written incrementally during generation (survives interruptions)
- **Local LLM** — Powered by Ollama (default: `qwen3:8b`), no external API keys required

## Prerequisites

- **Node.js** ≥ 18
- **Ollama** running locally with a model pulled:

  ```bash
  ollama pull qwen3:8b
  ```

## Installation

```bash
npx -y @gherk/requirements-extractor
```

Or install globally:

```bash
npm install -g @gherk/requirements-extractor
```

## MCP Configuration

Add to your MCP client configuration (e.g., `mcp-servers.json`):

```json
{
  "name": "requirements-extractor",
  "command": "npx",
  "args": ["-y", "@gherk/requirements-extractor"],
  "enabled": true
}
```

## Tools

### `extract_requirements`

Starts an asynchronous extraction job. Returns a `jobId` immediately.

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `pdfPath` | `string` | ✅ | — | Absolute path to the PDF file |
| `outputDir` | `string` | ✅ | — | Absolute path to the output directory |
| `projectPrefix` | `string` | ❌ | `"ACS"` | Prefix for requirement IDs (e.g., `ACS_BE_01`) |
| `outputFormat` | `"markdown" \| "json" \| "both"` | ❌ | `"markdown"` | Output format |
| `filterCategories` | `string[]` | ❌ | all | Only generate for these categories |
| `model` | `string` | ❌ | `"qwen3:8b"` | Ollama model to use |
| `ollamaUrl` | `string` | ❌ | `"http://localhost:11434"` | Ollama API URL |

**Response:**

```json
{
  "jobId": "a1b2c3d4",
  "status": "running",
  "message": "Extraction started. Use get_extraction_progress to poll for results."
}
```

---

### `get_extraction_progress`

Polls a running job for incremental results.

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `jobId` | `string` | ✅ | — | Job ID from `extract_requirements` |
| `since` | `number` | ❌ | `0` | Return requirements generated after this index |

**Response:**

```json
{
  "jobId": "a1b2c3d4",
  "status": "running",
  "phase": "Pass 3: Generating markdown...",
  "totalIdentified": 42,
  "totalToGenerate": 38,
  "done": 12,
  "finished": false,
  "new": [
    {
      "id": "ACS_BE_01",
      "title": "User Authentication",
      "category": "backend",
      "category_label": "Backend",
      "type": "Funcional",
      "page_range": "5-7",
      "description": "JWT-based authentication system...",
      "acceptance_criteria": ["Given a valid token...", "..."]
    }
  ]
}
```

---

### `list_jobs`

Lists all extraction jobs (running and completed). Takes no parameters.

**Response:**

```json
[
  { "id": "a1b2c3d4", "status": "completed", "phase": "Done", "done": 38, "total": 38 },
  { "id": "e5f6g7h8", "status": "running", "phase": "Pass 2: Classifying...", "done": 0, "total": 0 }
]
```

---

### `list_requirements`

Lists all generated requirement files in a directory, organized by category.

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `requirementsDir` | `string` | ✅ | Absolute path to the requirements directory |

---

### `get_requirement`

Returns the full content of a specific requirement file.

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `requirementsDir` | `string` | ✅ | Absolute path to the requirements directory |
| `requirementId` | `string` | ✅ | Requirement ID (e.g., `BE_01`) or filename (e.g., `ACS_BE_01_auth.md`) |

## Pipeline Architecture

```
PDF → Parse (5-page chunks)
  ↓
Pass 1: Identify — LLM extracts raw requirements from each chunk
  ↓
Pass 2: Classify — LLM assigns category (backend/frontend/mobile/infra/devops/nf)
  ↓
Sequence — Assign IDs: {PREFIX}_{CATEGORY}_{SEQ} (e.g., ACS_BE_01)
  ↓
Pass 3: Generate — LLM generates structured markdown per requirement
  ↓
Write — Each file written immediately (crash-resilient)
  ↓
Summary — README.md + requirements.json in output directory
```

## Categories

| ID | Prefix | Label | Description |
|----|--------|-------|-------------|
| `backend` | `BE` | Backend | APIs, services, business logic, integrations |
| `frontend` | `FE` | Frontend | Web interfaces, dashboards, forms, portals |
| `mobile` | `MB` | Mobile | iOS/Android apps, Bluetooth, GPS, push notifications |
| `infra` | `IF` | Infrastructure | Databases, schemas, storage, message queues, caching |
| `devops` | `DO` | DevOps | CI/CD, environments, containers, monitoring |
| `non-functional` | `NF` | Non-Functional | Security, performance, scalability, SLAs, GDPR |

## Output Structure

```
outputDir/
├── backend/
│   ├── ACS_BE_01_user_authentication.md
│   └── ACS_BE_02_payment_api.md
├── frontend/
│   └── ACS_FE_01_dashboard.md
├── mobile/
│   └── ACS_MB_01_push_notifications.md
├── infra/
├── devops/
├── non-functional/
│   └── ACS_NF_01_gdpr_compliance.md
├── README.md          # Summary with requirement counts
└── requirements.json  # All requirements as structured JSON
```

## Gherk Integration

When used within the Gherk platform, the MCP is called through a multi-layer async pipeline:

```
Frontend (ImporterComponent)
  │  POST /tickets/analyze-requirements (file upload)
  ▼
server-go (RequirementsHandler)
  │  Returns { jobId } immediately
  │  Spawns goroutine →
  ▼
agent-go (/ai/extract-requirements)
  │  Calls MCP tool: extract_requirements
  │  MCP returns { jobId } → agent-go polls get_extraction_progress
  │  When done, reads requirements.json from outputDir
  ▼
agent-go → maps MCP output to AnalyzedRequirementV2:
  │  • category → side (frontend/backend/both)
  │  • Adds priority, roles, acceptanceCriteria
  ▼
server-go → broadcasts via WebSocket:
  │  • requirements_progress (phase updates)
  │  • requirements_complete (final results)
  │  • requirements_failed (errors)
  ▼
Frontend (ImporterStore) — receives results, shows preview
```

### Category-to-Side Mapping

| MCP Category | Gherk Side |
|-------------|------------|
| `frontend` | `frontend` |
| `mobile` | `frontend` |
| `backend` | `backend` |
| `infra` | `both` |
| `devops` | `both` |
| `non-functional` | `both` |

### Key Files

| File | Description |
|------|-------------|
| `agent-go/extract_requirements_handler.go` | Calls MCP, reads `requirements.json`, maps to Gherk format |
| `agent-go/internal/mcp/mcp-servers.json` | MCP registration (`timeoutSeconds: 0`) |
| `server-go/.../requirements.go` | Async handler, WebSocket broadcasting |
| `desktop-app/.../importer.store.ts` | Frontend state (survives navigation) |

## Development

```bash
# Clone and install
npm install

# Build
npm run build

# Watch mode
npm run dev

# Run locally
npm start
```

## License

MIT