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# Content Extract MCP
# 内容提取 MCP
Python MCP scaffold for article content extraction, structured summary validation, deterministic filtering, and Markdown sink output.
这是一个基于 Python 的 MCP 项目骨架，用于完成文章内容提取、结构化摘要校验、确定性过滤，以及 Markdown 输出落盘。
## Run
## 运行
```bash
pip install -e .
summary-mcp
```
The server exposes five tools:
服务当前暴露 5 个工具：
- `extract_url_content`
- `extract_item_content`
@@ -17,31 +17,33 @@ The server exposes five tools:
- `run_freshrss_openclaw_pipeline`
- `generate_article_summaries`
Article-summary post-processing (separate LLM optional):
## 单篇文章总结后处理（可选使用独立 LLM）
**Recommended production input for selected-article summaries**
### 生产环境推荐输入
- Use the per-item extracted files written by the main FreshRSS pipeline:
- 单篇总结的正式生产输入，优先使用 FreshRSS 主流水线输出的**单篇 extracted 文件**：
- `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json`
- Treat these per-item extracted files as the formal default artifacts for downstream selected-article summarization.
- A batch extracted file such as `outputs/freshrss/extracted/freshrss.extracted.json` is only a compatible input shape for ad hoc or legacy workflows, not the preferred production default.
- 这些**逐条 extracted 文件**是下游单篇总结的**正式默认产物**。
- 像 `outputs/freshrss/extracted/freshrss.extracted.json` 这样的**批量 extracted 文件**，只作为临时场景、兼容旧流程的输入形态保留，**不是首选生产默认**。
Daily knowledge-base defaults:
### daily 知识库默认配置
- `IMA_DAILY_KNOWLEDGE_BASE_ID` — default IMA knowledge base ID for daily single-article summaries
- `IMA_DAILY_KNOWLEDGE_BASE_NAME` — default IMA knowledge base name (expected: `daily`)
- Runtime upload logic should verify the configured target before upload; if unavailable, try resolving by name and create `daily` if needed
- `IMA_DAILY_KNOWLEDGE_BASE_ID` —— 单篇日报总结默认上传的 IMA 知识库 ID
- `IMA_DAILY_KNOWLEDGE_BASE_NAME` —— 默认知识库名称（预期值：`daily`）
- 上传逻辑在运行时应先校验目标知识库；若配置的目标不存在，应先按名称查找，仍不存在则创建 `daily`
- `article-summary` MCP tool (operates on existing extracted payloads)
- `scripts/run_article_summaries.py` CLI helper
相关能力：
Validate an LLM summary result:
- `article-summary` MCP 工具（基于已有 extracted payload 做单篇总结）
- `scripts/run_article_summaries.py` CLI 辅助脚本
## 校验 LLM 摘要结果
```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:
## 跑最小 extraction → summary 循环
```bash
python scripts/run_summary_loop.py ^
@@ -50,7 +52,7 @@ python scripts/run_summary_loop.py ^
--output outputs/reference/summary/result.loop.json
```
Pull FreshRSS entries and map them into normalized `item` objects:
## 拉取 FreshRSS 条目并映射为标准化 `item`
```bash
set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
@@ -59,15 +61,16 @@ 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.
默认会排除已经带 `read` 标签的条目。
如果你想拿到完整阅读列表，可以加 `--include-read`。
启用 `--mark-read` 后，脚本会在执行成功后把本次抓到的条目标记为已读。
The script writes:
脚本会写出：
- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`
Pull FreshRSS entries and run content extraction for each mapped item:
## 拉取 FreshRSS 条目并逐条做内容提取
```bash
set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
@@ -76,17 +79,18 @@ 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.
默认会排除已经带 `read` 标签的条目。
启用 `--mark-read` 后，只有提取成功的条目才会被标记为已读。
The script writes:
脚本会写出：
- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`
- `outputs/freshrss/extracted/freshrss.extracted.json`
Note: this batch extracted file is mainly a compatible artifact for standalone extraction runs and older workflows. The formal production default for downstream selected-article summarization is still the per-item extracted output under `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json`.
注意：这个**批量 extracted 文件**主要用于独立提取场景和旧流程兼容。下游单篇总结的正式生产默认输入，仍然是 `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json` 这类**逐条 extracted 文件**。
Run the full FreshRSS pipeline and mark items as read only after the final OpenClaw delivery payload is written:
## 跑完整 FreshRSS 流水线，并在最终 delivery payload 写盘成功后再标记已读
```bash
set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
@@ -98,7 +102,7 @@ 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:
如果你希望过滤时引入个人工程兴趣 / AI Agent 兴趣画像，可以传入 context 文件：
```bash
python scripts/run_freshrss_pipeline.py ^
@@ -107,24 +111,25 @@ python scripts/run_freshrss_pipeline.py ^
--mark-read
```
This is the recommended production entrypoint. By default it writes only:
这是当前推荐的**正式生产入口**。默认只写出这些产物：
- `outputs/freshrss/rerun/<timestamp>/raw/freshrss.raw.json`
- `outputs/freshrss/rerun/<timestamp>/candidates/openclaw-delivery-payload.json`
- `outputs/freshrss/rerun/<timestamp>/run-report.json`
- `outputs/freshrss/rerun/<timestamp>/extracted/item-XX.extracted.json` (one per item)
- `outputs/freshrss/rerun/<timestamp>/extracted/item-XX.extracted.json`（每篇一份）
It also updates the daily keyword index runtime data:
同时还会更新每日关键词索引运行数据：
- `data/term_index/daily/YYYY-MM-DD.json`
- `data/term_index/term_stats.json`
The main pipeline does not emit a batch-level `freshrss.extracted.json` file by default.
If you need additional per-item intermediates such as normalized items, summaries, filter decisions, candidate records, or candidate inputs, add `--debug-artifacts`.
主流水线默认**不会**产出批量级的 `freshrss.extracted.json`。
如果你需要更多逐条中间产物，例如标准化 items、摘要结果、过滤决策、candidate record、candidate input，可以加 `--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.
当 OpenClaw 接入这个 MCP 服务后，应直接调用 `run_freshrss_openclaw_pipeline` 来获得同样行为。
在排查复杂问题时，也可以把 `debug_artifacts=true` 打开。
Run deterministic filter rules against a structured summary result:
## 对结构化摘要结果执行确定性过滤规则
```bash
python scripts/run_filter_rules.py ^
@@ -133,7 +138,7 @@ python scripts/run_filter_rules.py ^
--output outputs/reference/filter/filter-decision.json
```
You can optionally pass a context file to inject interest topics or source tags:
如果你希望注入兴趣主题或来源标签，也可以额外传入 context 文件：
```bash
python scripts/run_filter_rules.py ^
@@ -143,12 +148,12 @@ python scripts/run_filter_rules.py ^
--output outputs/reference/filter/filter-decision.with-context.json
```
Rule engine details and rule authoring guidance live in:
规则引擎设计和规则编写说明见：
- `docs/design/filter-rule-engine-design.md`
- `docs/design/filter-rule-engine-usage.md`
Write a filtered result into the Markdown sink:
## 将过滤结果写入 Markdown sink
```bash
python scripts/run_markdown_sink.py ^
@@ -157,9 +162,9 @@ python scripts/run_markdown_sink.py ^
--filter outputs/reference/filter/filter-decision.json
```
The script writes markdown notes under `knowledge-base/`.
脚本会把 Markdown 笔记写到 `knowledge-base/` 下。
Build an internal `ArticleCandidateRecord` and a slim `OpenClawCandidateInput`:
## 构建内部 `ArticleCandidateRecord` 与精简版 `OpenClawCandidateInput`
```bash
python scripts/run_article_candidate.py ^
@@ -169,12 +174,12 @@ python scripts/run_article_candidate.py ^
--section-hint tools_and_workflows
```
The script writes by default:
默认会写出：
- `outputs/reference/candidates/article-candidate-record.json`
- `outputs/reference/candidates/openclaw-candidate-input.json`
Build a batch OpenClaw delivery payload:
## 构建批量 OpenClaw delivery payload
```bash
python scripts/build_openclaw_delivery.py ^
@@ -183,13 +188,15 @@ python scripts/build_openclaw_delivery.py ^
--date 2026-03-25
```
The script writes by default:
默认会写出：
- `outputs/reference/candidates/openclaw-delivery-payload.json`
Output layout details live in `outputs/README.md`.
输出目录布局说明见 `outputs/README.md`。
Keyword index defaults live in:
## 关键词索引默认配置
相关配置文件位于：
- `configs/term_aliases.json`
- `configs/term_stopwords.json`
@@ -197,20 +204,22 @@ Keyword index defaults live in:
- `configs/term_watchlist.json`
- `configs/term_change_log.json`
You can also rebuild the keyword index from an existing delivery payload:
你也可以基于已有 delivery payload 重新构建关键词索引：
```bash
python scripts/build_keyword_index.py ^
--input outputs/reference/candidates/openclaw-delivery-payload.json
```
Runtime keyword data is stored under `data/term_index/`.
运行期关键词数据存放在：
The keyword cleanup review skill lives in:
- `data/term_index/`
关键词清理评审 skill 位于：
- `skills/keyword-cleanup-review/`
To build a review bundle for the LLM skill:
构建给 LLM skill 使用的评审数据包（review bundle）：
```bash
python skills/keyword-cleanup-review/scripts/build_review_bundle.py ^
@@ -219,15 +228,19 @@ python skills/keyword-cleanup-review/scripts/build_review_bundle.py ^
--output outputs/term_index/review/keyword-cleanup-bundle.json
```
The review bundle now also carries cleanup governance context:
现在这个评审数据包（review bundle）还会额外携带治理上下文：
- cleanup thresholds from `configs/term_cleanup_policy.json`
- the current watch list from `configs/term_watchlist.json`
- recent applied changes from `configs/term_change_log.json`
- 来自 `configs/term_cleanup_policy.json` 的清理阈值
- 当前 watch list（`configs/term_watchlist.json`）
- 最近已应用的变更（`configs/term_change_log.json`）
The skill only produces review inputs and suggestions. It does not modify `term_aliases`, `term_stopwords`, or `filter_context.personal.json` automatically.
这个 skill 只负责生成 review 输入与建议，不会自动修改：
To preview accepted suggestions before writing any config files:
- `term_aliases`
- `term_stopwords`
- `filter_context.personal.json`
如果你想先预览已接受建议，再决定是否写配置文件：
```bash
python scripts/apply_term_suggestions.py ^
@@ -236,20 +249,21 @@ python scripts/apply_term_suggestions.py ^
--dry-run
```
Remove `--dry-run` to write the accepted changes. The script can also apply accepted `alias`, `stopword`, and `interest keyword` suggestions through `--accept-alias`, `--accept-stopword`, and `--accept-interest`. Accepted watch terms are written into `configs/term_watchlist.json`, and every applied action is appended into `configs/term_change_log.json`.
去掉 `--dry-run` 后才会真正写文件。
这个脚本也支持通过 `--accept-alias`、`--accept-stopword`、`--accept-interest` 应用 alias / stopword / interest keyword 变更。
已接受的 watch 词会写入 `configs/term_watchlist.json`，每次应用动作也会被追加到 `configs/term_change_log.json`。
## 单篇总结 LLM 配置
## Article-summary LLM configuration
Set a dedicated model for post-processing summaries without affecting the main pipeline:
如果你希望单篇总结后处理使用独立模型，而不影响主流水线，可以设置：
- `ARTICLE_SUMMARY_LLM_API_URL`
- `ARTICLE_SUMMARY_LLM_MODEL`
- `ARTICLE_SUMMARY_LLM_API_KEY`
If these are not set, the summarizer falls back to the main `LLM_*` / `OPENAI_*` settings used elsewhere.
如果这些变量未设置，单篇总结会回退使用主流程中的 `LLM_*` / `OPENAI_*` 配置。
Example (PowerShell style):
示例（PowerShell 风格）：
```bash
set ARTICLE_SUMMARY_LLM_API_URL=https://api.deepseek.com
@@ -257,8 +271,8 @@ set ARTICLE_SUMMARY_LLM_API_KEY=your-article-summary-key
set ARTICLE_SUMMARY_LLM_MODEL=deepseek-chat
```
Then run, for example, either via the CLI script.
For normal production use, prefer a per-item extracted file from `outputs/freshrss/rerun/<run_id>/extracted/`. The batch extracted example below is kept only as a compatible legacy/ad hoc input shape:
然后可以这样调用 CLI。
正式生产环境建议优先使用 `outputs/freshrss/rerun/<run_id>/extracted/` 下的**逐条 extracted 文件**；下面这个**批量 extracted** 示例仅保留为兼容旧流程 / 临时场景输入：
```bash
python scripts/run_article_summaries.py ^
@@ -267,13 +281,22 @@ python scripts/run_article_summaries.py ^
--output-dir outputs/freshrss/single_summaries
```
…or through the MCP server tool `generate_article_summaries` exposed by `summary_mcp.server`:
也可以通过 `summary_mcp.server` 暴露的 MCP 工具 `generate_article_summaries` 调用：
- `extracted_path` (string): path to a single-item extracted JSON (e.g. `outputs/freshrss/rerun/<run_id>/extracted/item-01.extracted.json`) or a batch extracted JSON containing a `results` array.
- `selected_ids` (array of strings): one or more `item_id` values to summarize. Pass an empty array to summarize all items in the file.
- `output_dir` (optional string): directory to write Markdown summaries. If omitted, summaries are written under `single_summaries/` next to the extracted file.
- `llm_api_key` / `llm_model` / `llm_api_url` (optional strings): overrides for article-summary LLM settings. If omitted, the tool falls back to `ARTICLE_SUMMARY_*` or main `LLM_*` env vars as described above.
- `extracted_path`（string）：单篇 extracted JSON 路径（例如 `outputs/freshrss/rerun/<run_id>/extracted/item-01.extracted.json`），或者包含 `results` 数组的 batch extracted JSON
- `selected_ids`（array of strings）：要总结的一个或多个 `item_id`。如果传空数组，则对文件中的全部条目做总结
- `output_dir`（optional string）：Markdown 输出目录；若不传，则默认写到 extracted 文件旁边的 `single_summaries/` 目录
- `llm_api_key` / `llm_model` / `llm_api_url`（optional strings）：单篇总结 LLM 的覆盖配置；不传时会按前文规则回退到 `ARTICLE_SUMMARY_*` 或主 `LLM_*`
The tool returns a JSON array of file paths for the generated Markdown summaries.
该工具返回一个 JSON 数组，内容为生成好的 Markdown 文件路径。
The article summary uses a dedicated prompt (`outputs/prompts/article-summary-prompt.txt`) that is completely independent from the daily digest prompt. It outputs a structured knowledge note in Chinese with sections: 核心结论、主要论点、关键方法 / 机制、重要细节、可复用启发、关键词、主题.
单篇总结使用独立 prompt：`outputs/prompts/article-summary-prompt.txt`。
它与日报 prompt 完全独立，输出的是中文结构化知识笔记，包含这些部分：
- 核心结论
- 主要论点
- 关键方法 / 机制
- 重要细节
- 可复用启发
- 关键词
- 主题