Content Extract MCP

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

Run

pip install -e .
summary-mcp

The server exposes four tools:

• extract_url_content
• extract_item_content
• filter_summary_result
• run_freshrss_openclaw_pipeline

Validate an LLM summary result:

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

Run the minimal extraction-to-summary loop:

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:

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 --mark-read

By default the script excludes entries already tagged as read. Add --include-read if you want the full reading list. When --mark-read is enabled, fetched entries are marked as read after the script finishes successfully.

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:

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 --mark-read

By default the script excludes entries already tagged as read. When --mark-read is enabled, only entries with successful extraction are marked as read.

The script writes:

• outputs/freshrss/raw/freshrss.raw.json
• outputs/freshrss/items/freshrss.items.json
• outputs/freshrss/extracted/freshrss.extracted.json

Run the full FreshRSS pipeline and mark items as read only after the final OpenClaw delivery payload is written:

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
set LLM_API_URL=https://api.deepseek.com
set LLM_API_KEY=your-llm-api-key
set LLM_MODEL=deepseek-chat
python scripts/run_freshrss_pipeline.py --limit 5 --mark-read

If you want to apply your personal engineering and AI-agent interest profile during filtering, pass a context file:

python scripts/run_freshrss_pipeline.py ^
  --limit 5 ^
  --context configs/filter_context.personal.json ^
  --mark-read

This is the recommended production entrypoint. By default it writes only:

• outputs/freshrss/rerun/<timestamp>/raw/freshrss.raw.json
• outputs/freshrss/rerun/<timestamp>/candidates/openclaw-delivery-payload.json
• outputs/freshrss/rerun/<timestamp>/run-report.json

If you need per-item intermediates, add --debug-artifacts.

When OpenClaw is connected to the MCP server, it should call run_freshrss_openclaw_pipeline for the same behavior directly through MCP. The tool also supports debug_artifacts=true when deeper inspection is needed.

Run deterministic filter rules against a structured summary result:

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:

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

Rule engine details and rule authoring guidance live in:

• docs/design/filter-rule-engine-design.md
• docs/design/filter-rule-engine-usage.md

Write a filtered result into the Markdown sink:

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:

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:

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.