# Content Extract MCP

Python MCP scaffold for article content extraction, structured summary validation, deterministic filtering, and Markdown sink output.

## Run

```bash
pip install -e .
summary-mcp
```

The server exposes three tools:

- `extract_url_content`
- `extract_item_content`
- `filter_summary_result`

Validate an LLM summary result:

```bash
validate-llm-result outputs/reference/summary/result.json --extracted outputs/reference/extracted/read-flow-2026.extracted.json
```

Run the minimal extraction-to-summary loop:

```bash
python scripts/run_summary_loop.py ^
  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
  --prompt outputs/prompts/llm-summary-prompt.txt ^
  --output outputs/reference/summary/result.loop.json
```

Pull FreshRSS entries and map them into normalized `item` objects:

```bash
set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
set FRESHRSS_USERNAME=bot
set FRESHRSS_API_PASSWORD=your-api-password
python scripts/pull_freshrss_items.py --limit 5
```

The script writes:

- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`

Pull FreshRSS entries and run content extraction for each mapped item:

```bash
set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
set FRESHRSS_USERNAME=osiman
set FRESHRSS_API_PASSWORD=your-api-password
python scripts/run_freshrss_extract.py --limit 1
```

The script writes:

- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`
- `outputs/freshrss/extracted/freshrss.extracted.json`

Run deterministic filter rules against a structured summary result:

```bash
python scripts/run_filter_rules.py ^
  --summary outputs/reference/summary/result.loop.json ^
  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
  --output outputs/reference/filter/filter-decision.json
```

You can optionally pass a context file to inject interest topics or source tags:

```bash
python scripts/run_filter_rules.py ^
  --summary outputs/reference/summary/result.loop.json ^
  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
  --context outputs/reference/filter/filter-context.json ^
  --output outputs/reference/filter/filter-decision.with-context.json
```

Write a filtered result into the Markdown sink:

```bash
python scripts/run_markdown_sink.py ^
  --summary outputs/reference/summary/result.loop.json ^
  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
  --filter outputs/reference/filter/filter-decision.json
```

The script writes markdown notes under `knowledge-base/`.

Build an internal `ArticleCandidateRecord` and a slim `OpenClawCandidateInput`:

```bash
python scripts/run_article_candidate.py ^
  --summary outputs/reference/summary/result.loop.json ^
  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
  --filter outputs/reference/filter/filter-decision.json ^
  --section-hint tools_and_workflows
```

The script writes by default:

- `outputs/reference/candidates/article-candidate-record.json`
- `outputs/reference/candidates/openclaw-candidate-input.json`

Build a batch OpenClaw delivery payload:

```bash
python scripts/build_openclaw_delivery.py ^
  --input-dir outputs/freshrss/candidates/batch ^
  --sort-by-rank ^
  --date 2026-03-25
```

The script writes by default:

- `outputs/reference/candidates/openclaw-delivery-payload.json`

Output layout details live in `outputs/README.md`.