PageIndex Light MCP
Click on "Deploy 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., "@PageIndex Light MCP search 'AI ethics' in the latest whitepaper.pdf"
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.
PageIndex Light MCP
Agentic PDF Search via MCP — Inspired by PageIndex
Vectorless, reasoning-based document retrieval that thinks like a human
Overview
PageIndex Light MCP brings agentic search capabilities to your PDF documents through the Model Context Protocol. Instead of traditional vector similarity, it leverages LLM reasoning for intelligent, human-like document navigation.
Inspired by VectifyAI/PageIndex and pageindex-mcp.
Related MCP server: asset-aware-mcp
Features
Agentic Search — LLM-powered semantic search through document structure
MCP Sampling — Native MCP protocol sampling support
LLM Fallback — Auto-fallback to OpenAI-compatible APIs for non-sampling clients
OCR Fallback — Automatic OCR for scanned PDFs
Tools
Tool | Description |
| Get PDF index with semantic search support |
| Retrieve detailed content of a specific page |
How It Works
flowchart TB
subgraph Input
A[PDF File] --> B{Text Extraction}
end
subgraph TextExtraction["Text Extraction"]
B -->|Success| C[Raw Text]
B -->|Empty/Minimal| D{OCR Configured?}
D -->|Yes| E[Vision LLM OCR]
D -->|No| C
E --> C
end
subgraph Indexing
C --> F[LLM Summarization]
F -->|Per Page| G[Page Summaries]
G --> H[(Cached Index)]
end
subgraph Search["Agentic Search"]
I[User Query] --> J{Has Query?}
J -->|No| K[Return Full Index]
J -->|Yes| L[LLM Reasoning]
H --> L
L --> M[Ranked Results]
end
subgraph LLMProvider["LLM Provider"]
N{MCP Sampling?}
N -->|Supported| O[MCP Client LLM]
N -->|Not Supported| P[Fallback LLM API]
end
F -.-> N
L -.-> NQuick Start
Claude Desktop / Claude Code
Add to your MCP config:
{
"mcpServers": {
"pageindex": {
"command": "uv",
"args": ["run", "--directory", "/path/to/pageindex-light-mcp", "server.py"],
"env": {
"PAGEINDEX_LLM_BASE_URL": "https://api.openai.com/v1",
"PAGEINDEX_LLM_API_KEY": "sk-xxx",
"PAGEINDEX_LLM_MODEL": "gpt-4o-mini",
"PAGEINDEX_OCR_BASE_URL": "https://api.openai.com/v1",
"PAGEINDEX_OCR_API_KEY": "sk-xxx",
"PAGEINDEX_OCR_MODEL": "gpt-4o-mini"
}
}
}
}Environment Variables
Both configurations are optional and independent:
Variable | Purpose | Required |
| Fallback for non-Sampling MCP clients | Optional |
| Fallback for scanned PDFs (when text extraction fails) | Optional |
# LLM Config — Used when MCP client doesn't support Sampling
PAGEINDEX_LLM_BASE_URL=https://api.openai.com/v1
PAGEINDEX_LLM_API_KEY=sk-xxx
PAGEINDEX_LLM_MODEL=gpt-4o-mini
# OCR Config — Used when PDF text extraction returns empty/minimal content
PAGEINDEX_OCR_BASE_URL=https://api.openai.com/v1
PAGEINDEX_OCR_API_KEY=sk-xxx
PAGEINDEX_OCR_MODEL=gpt-4o-mini # Any vision-capable modelLicense
MIT
Available Tools
2 toolsget_detailA
获取 PDF 某一页的详细内容
Args: file_path: PDF 文件的完整路径 page: 页码(从 1 开始)
Inputs: page (required), file_path (required). Returns: object
get_indexA
获取 PDF 文件索引,支持语义搜索
Args: file_path: PDF 文件的完整路径 query: 搜索查询(可选)。如果提供,返回最相关的页面;否则返回全部索引 top_k: 返回结果数量,默认 5(仅在有 query 时生效)
Inputs: file_path (required), query, top_k. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one retrieves detailed content for a specific page, the other returns an index with optional semantic search. No overlap or ambiguity exists.
Both tools follow the same 'get_' prefix followed by a noun, perfectly consistent and predictable.
Only two tools exist, which is at the thin end of the range. While appropriate for a 'light' server, the scope is minimal and may feel incomplete for broader PDF workflows.
The core workflow of indexing and retrieving page details is covered. Minor gaps exist such as no explicit metadata retrieval or index update, but these can be worked around.
Maintenance
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- AlicenseNot gradedqualityDmaintenanceA local-first MCP server for building and querying PDF knowledge bases. It indexes PDFs into DuckDB and exposes evidence-grounded retrieval tools to separate corpus-backed answers from independent reasoning.16 npm3MIT