120 lines
3.4 KiB
Markdown
120 lines
3.4 KiB
Markdown
# Content Extract MCP
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Python MCP scaffold for article content extraction, structured summary validation, deterministic filtering, and Markdown sink output.
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## Run
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```bash
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pip install -e .
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summary-mcp
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```
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The server exposes three tools:
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- `extract_url_content`
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- `extract_item_content`
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- `filter_summary_result`
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Validate an LLM summary result:
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```bash
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validate-llm-result outputs/reference/summary/result.json --extracted outputs/reference/extracted/read-flow-2026.extracted.json
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```
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Run the minimal extraction-to-summary loop:
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```bash
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python scripts/run_summary_loop.py ^
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--extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
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--prompt outputs/prompts/llm-summary-prompt.txt ^
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--output outputs/reference/summary/result.loop.json
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```
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Pull FreshRSS entries and map them into normalized `item` objects:
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```bash
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set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
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set FRESHRSS_USERNAME=bot
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set FRESHRSS_API_PASSWORD=your-api-password
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python scripts/pull_freshrss_items.py --limit 5
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```
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The script writes:
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- `outputs/freshrss/raw/freshrss.raw.json`
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- `outputs/freshrss/items/freshrss.items.json`
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Pull FreshRSS entries and run content extraction for each mapped item:
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```bash
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set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
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set FRESHRSS_USERNAME=osiman
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set FRESHRSS_API_PASSWORD=your-api-password
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python scripts/run_freshrss_extract.py --limit 1
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```
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The script writes:
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- `outputs/freshrss/raw/freshrss.raw.json`
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- `outputs/freshrss/items/freshrss.items.json`
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- `outputs/freshrss/extracted/freshrss.extracted.json`
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Run deterministic filter rules against a structured summary result:
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```bash
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python scripts/run_filter_rules.py ^
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--summary outputs/reference/summary/result.loop.json ^
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--extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
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--output outputs/reference/filter/filter-decision.json
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```
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You can optionally pass a context file to inject interest topics or source tags:
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```bash
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python scripts/run_filter_rules.py ^
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--summary outputs/reference/summary/result.loop.json ^
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--extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
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--context outputs/reference/filter/filter-context.json ^
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--output outputs/reference/filter/filter-decision.with-context.json
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```
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Write a filtered result into the Markdown sink:
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```bash
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python scripts/run_markdown_sink.py ^
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--summary outputs/reference/summary/result.loop.json ^
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--extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
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--filter outputs/reference/filter/filter-decision.json
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```
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The script writes markdown notes under `knowledge-base/`.
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Build an internal `ArticleCandidateRecord` and a slim `OpenClawCandidateInput`:
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```bash
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python scripts/run_article_candidate.py ^
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--summary outputs/reference/summary/result.loop.json ^
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--extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
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--filter outputs/reference/filter/filter-decision.json ^
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--section-hint tools_and_workflows
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```
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The script writes by default:
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- `outputs/reference/candidates/article-candidate-record.json`
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- `outputs/reference/candidates/openclaw-candidate-input.json`
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Build a batch OpenClaw delivery payload:
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```bash
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python scripts/build_openclaw_delivery.py ^
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--input-dir outputs/freshrss/candidates/batch ^
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--sort-by-rank ^
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--date 2026-03-25
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```
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The script writes by default:
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- `outputs/reference/candidates/openclaw-delivery-payload.json`
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Output layout details live in `outputs/README.md`. |