Power your agent
การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
Your AI writes code using knowledge from its training data. Your project uses libraries released after that cutoff. The result: hallucinated APIs, deprecated patterns, and code that doesn't compile.
Your team's decisions — use PaymentIntents, not Charges; always wrap API responses; never useEffect for data fetching — exist nowhere in the AI's training data. The code it generates is idiomatic React or idiomatic Node — just not idiomatic .
Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.
When we asked Claude to write code for botid (published 2026-03-03, post-training):
"I can't verify the botid package's actual API. I won't output code for a package whose API I can't verify. Guessing function names and signatures would likely give you broken code."
BookLib detects every post-training API in your codebase and resolves the gaps automatically. Real output: booklib analyze on vercel/ai-chatbot — 82 dependencies, 274 post-training APIs across 158 files.
Without BookLib — AI uses AI SDK v5 patterns from training data:
import { OpenAIStream, StreamingTextResponse } from 'ai'; // removed in v6
import OpenAI from 'openai';
export async function POST(req: Request) {
const response = await new OpenAI().chat.completions.create({
model: 'gpt-4', stream: true, messages,
});
return new StreamingTextResponse(OpenAIStream(response));
}
With BookLib — AI gets v6 docs injected at runtime:
import { streamText, convertToModelMessages,
createUIMessageStreamResponse } from 'ai';
import { openai } from '@ai-sdk/openai';
export async function POST(req: Request) {
const result = streamText({
model: openai('gpt-4o'),
messages: convertToModelMessages(messages),
});
return createUIMessageStreamResponse({
stream: result.toUIMessageStream(),
});
}
Requires Node.js >= 18.
npm install -g @booklib/core
booklib init
The wizard detects your stack, configures MCP for your AI tools, and builds the knowledge index. Then see what your AI doesn't know:
booklib analyze
Website and skill browser at booklib-ai.github.io/booklib.
Most context tools wait for your AI to ask. BookLib detects gaps before coding starts and injects corrections as code is written.
Scans your dependencies across npm, PyPI, Maven, Crates.io, RubyGems, Go modules, Packagist, Pub, Swift, and NuGet. Checks publish dates against the model's training cutoff, then cross-references with your source code to find the exact files and APIs affected.
For each gap, BookLib fetches current documentation:
booklib connect commandPreToolUse and PostToolUse hooks inject context as your AI writes code:
Your team's decisions live nowhere in public docs. BookLib auto-detects project documentation — specs, ADRs, architecture docs — and indexes them alongside your team decisions.
booklib capture --title "use PaymentIntents not Charges" --type decision
booklib connect notion database <db-id>
booklib connect github discussions org/repo
booklib init detects your AI tools and configures MCP automatically.
10 tools via MCP, 14 total with instruction-file support. See AGENTS.md for per-tool setup.
Setup
Daily use
Knowledge
Sources
Run booklib --help --all for the full list.
Everything runs locally by default. Embeddings via HuggingFace Transformers (CoreML on macOS, CPU elsewhere), vector search via Vectra, lexical search via BM25, all persisted in .booklib/. Optional cloud modes (Ollama, Anthropic, OpenAI) for AI-powered reasoning.
BookLib complements code context tools:
See CONTRIBUTING.md for the full guide.
Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Booklib ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.