This release adds 3 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
Topics
+5 more
ReleasePort's take
Light signalv1.2.0 adds story summary APIs (get_event_summary, get_story_summary) and list_stories --include-summaries flag. LLM-generated summaries via DeepSeek V4 Flash during CI with three-tier fallback: LLM output, official one-liner, or chapter storyInfo.
Why it matters: Use get_event_summary and get_story_summary to display narrative context in event/story views. LLM-generated summaries deploy during CI with three-tier fallback ensuring availability. Integrate on next client update.
Summary
AI summaryAdded story chapter summaries with LLM-generated overviews and new APIs.
Changes in this release
| Type | Severity | Summary | CVE |
|---|---|---|---|
| Feature | Medium |
get_event_summary(event_id) provides narrative overview of every chapter in an event, including LLM event summaries when available. get_event_summary(event_id) provides narrative overview of every chapter in an event, including LLM event summaries when available. Source: llm_adapter@2026-05-21 Confidence: high |
— |
| Feature | Medium |
get_story_summary(story_key) returns single-chapter summary with three-tier fallback chain: LLM long summary, official one-liner, and chapter storyInfo field. get_story_summary(story_key) returns single-chapter summary with three-tier fallback chain: LLM long summary, official one-liner, and chapter storyInfo field. Source: llm_adapter@2026-05-21 Confidence: high |
— |
| Feature | Medium |
list_stories now accepts include_summaries (bool); when true, each chapter line includes an indented summary below it. list_stories now accepts include_summaries (bool); when true, each chapter line includes an indented summary below it. Source: llm_adapter@2026-05-21 Confidence: high |
— |
| Feature | Medium |
Story chapter summaries added with two new tools and one enhancement using zh_CN/storyinfo.json index. Story chapter summaries added with two new tools and one enhancement using zh_CN/storyinfo.json index. Source: llm_adapter@2026-05-21 Confidence: low |
— |
| Feature | Medium |
LLM summarization pipeline in 3aKHP/ArknightsStoryJson fork generates per-chapter and per-event summaries via DeepSeek V4 Flash API during CI release. LLM summarization pipeline in 3aKHP/ArknightsStoryJson fork generates per-chapter and per-event summaries via DeepSeek V4 Flash API during CI release. Source: llm_adapter@2026-05-21 Confidence: low |
— |
Full changelog
Added
- Story chapter summaries. Two new tools and one enhancement built on the
previously-unusedzh_CN/storyinfo.jsonindex (1,945 entries), plus an
LLM summarization pipeline in the data-source fork:get_event_summary(event_id)— narrative overview of every chapter in an
event, with chapter codes, tags, names, and summary text. When LLM event
summaries are available (zh_CN/event_summaries.json), a full-dialogue
V2 synopsis is prepended above the chapter listing.get_story_summary(story_key)— single-chapter summary with a three-tier
fallback chain: LLM long summary (zh_CN/summaries.json, 5~7:1 compression),
official one-liner (zh_CN/storyinfo.json), and chapterstoryInfofield.list_storiesnow acceptsinclude_summaries(bool). Whentrue, each
chapter line includes an indented summary below it.
- LLM summarization pipeline (
3aKHP/ArknightsStoryJsonfork):
scripts/summarize.pygenerates per-chapter (5~7:1) and per-event (10:1)
summaries via DeepSeek V4 Flash API during CI release, injecting them into
zh_CN.zipfor transparent consumption by the MCP server.
Install
pip install prts-mcp==1.2.0
Docker
docker pull ghcr.io/3akhp/prts-mcp:1.2.0
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About 3aKHP/prts-mcp
MCP Server for Arknights, querying the PRTS Wiki API and serving auto-synced operator archives and voice lines from game data. Designed for fan-creation (同人創作) AI agents. Python (stdio/Docker) and TypeScript (Streamable HTTP) implementations.
Related context
Related tools
Earlier breaking changes
- vts/v1.0.0 Public tool surface frozen; 9 MCP tool names, parameters, and response formats locked.
Beta — feedback welcome: [email protected]