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pageindex-mcp MCP Server

VectifyAI/pageindex-mcp

Score: 70.6 Rank #3923 Search & RAG
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MCP server for PageIndex. PageIndex is a vectorless reasoning-based RAG system which uses multi-step reasoning and tree search to retrieve information like a human expert would.

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Overview

VectifyAI/pageindex-mcp is a TypeScript MCP server licensed under MIT. MCP server for PageIndex. PageIndex is a vectorless reasoning-based RAG system which uses multi-step reasoning and tree search to retrieve information like a human expert would.

Ranked #3923 out of 24846 indexed tools.

Ecosystem

TypeScript MIT

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 400

400 stars → growing community

Freshness 25% 3mo ago

Last commit 3mo ago → stale

Issue Health 25% 100%

1/1 issues closed → responsive maintainer

Contributors 10% 4

4 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 52
Description Detailed
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Freshness high impact

Last commit was 95 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Matched Queries

"mcp server""mcp-server"

From the README

<div align="center">
  <a href="https://pageindex.ai/mcp">
    
  </a>
</div>

# PageIndex MCP

> If you find this repo useful, please also star our **[main PageIndex repo](https://github.com/VectifyAI/PageIndex)** ⭐

&nbsp;&nbsp;&nbsp;&nbsp;

📘 [**PageIndex**](https://github.com/VectifyAI/PageIndex) is a vectorless, reasoning-based RAG system that represents documents as hierarchical **tree structures**. It enables LLMs to navigate and retrieve information through structure and **reasoning**, not vector similarity — much like a human would retrieve information using a book's index.

🔌 [**PageIndex MCP**](https://pageindex.ai/mcp) exposes this **LLM-native, in-context tree index** directly to LLMs via MCP, allowing platforms like **Claude**, **Cursor**, and other MCP-compatible agents or LLMs to reason over document structure and retrieve the right information — without vector databases.

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Read full README on GitHub →

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