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Установка от издателя. Не эндпоинт PlugMCP.
Host a copy with our keys and logs.
В реестре пока нет пакетного релиза. Следуйте README репозитория.
Удалённый URL или команда на вашем компьютере? Чем различаются два способа установки
Запустить на 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.
ЗапускОт издателя
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.
Инструменты
Списка инструментов пока нет. У удалённых серверов он появляется после первой проверки состояния.
FAQ
Скопируйте на этой странице конфигурацию stdio для Cursor, Claude Desktop, Claude Code или VS Code. Она использует установку издателя, а не прокси PlugMCP.
В этой записи не указано обязательных переменных окружения.
Booklib работает локально через stdio.
Последняя проверка: 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.