booklib
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@booklibanalyze the current project for knowledge gaps"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The Problem
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 yours.
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."
The Fix
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(),
});
}Getting Started
Requires Node.js >= 18.
npm install -g @booklib/core
booklib initThe wizard detects your stack, configures MCP for your AI tools, and builds the knowledge index. Then see what your AI doesn't know:
booklib analyzeWebsite and skill browser at booklib-ai.github.io/booklib.
How It Works
Most context tools wait for your AI to ask. BookLib detects gaps before coding starts and injects corrections as code is written.
1. Detect Knowledge Gaps
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.
2. Resolve Automatically
For each gap, BookLib fetches current documentation:
Context7 — instant, version-specific library docs
GitHub — releases, wiki, and discussions
Manual — suggests the right
booklib connectcommand
3. Protect at Runtime
PreToolUse and PostToolUse hooks inject context as your AI writes code:
Runtime injection — 3-10 lines of relevant knowledge inserted before each edit, powered by a pre-computed context map
Import checking — flags unknown APIs not in the index (11 languages)
Contradiction detection — warns when code violates team decisions in real-time
4. Capture Team Knowledge
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/repoFeatures
Feature | Details |
Gap Detection | 10 package ecosystems, cross-referenced with source code |
Runtime Injection | Pre/PostToolUse hooks deliver context as AI writes code |
Context Map | Maps knowledge to code scopes via imports, terms, file patterns |
Auto-Resolution | Context7 + GitHub + web connectors fetch current docs |
Processing Modes | Fast (BM25), Local (Ollama), Cloud AI — choose in wizard |
Import Checking | Flags unknown APIs in JS/TS, Python, Go, Rust, Java, Kotlin, Ruby, PHP, C#, Swift, Dart |
Decision Checking | Detects when code contradicts captured team rules |
Knowledge Graph | Nodes, typed edges, auto-linking, BFS traversal |
Source Connectors | GitHub, Notion, Context7, local files, web docs, SDD specs (.specify, .planning, .kiro) |
Source Detection | Auto-detects 12 content types: OpenAPI, ADRs, Gherkin, project docs, and more |
Hybrid Search | BM25 + vector search + Reciprocal Rank Fusion + cross-encoder reranking |
23 Expert Skills | Distilled from Effective Java, Clean Code, DDD, and 20 more canonical books |
Works With
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.
CLI Reference
Setup
Command | Description |
| Guided setup — detects stack, configures MCP, builds index |
| Rebuild the search index |
| Health check for skills and config |
Daily use
Command | Description |
| Find post-training dependencies |
| Auto-fix gaps via Context7 and GitHub |
| Show affected files and post-training APIs |
| Search skills and knowledge |
Knowledge
Command | Description |
| Save a team decision or insight |
| Flag unknown APIs |
| Check code against team rules |
Sources
Command | Description |
| Index local documentation |
| Index GitHub changelogs |
| Index Notion pages |
| List connected sources |
Run booklib --help --all for the full list.
Architecture
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:
Layer | Tool | What it knows |
Documentation | Context7 | Current library APIs |
Code structure | lsp-mcp | Functions, types, call graphs |
Knowledge | BookLib | Post-training gaps, team decisions, expert principles |
Contributing
See CONTRIBUTING.md for the full guide.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/booklib-ai/booklib'
If you have feedback or need assistance with the MCP directory API, please join our Discord server