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Colpali MCP Server - Python

@camillebrl

Repository

Basic information

Category

Other

License

MIT license

Transports

stdio

Author

camillebrl

About Colpali MCP Server - Python

mcp server for Colpali (text-rich images) retrieval

Colpali MCP Server - Python config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Colpali MCP Server - Python tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Colpali MCP Server - Python?

Colpali MCP Server - Python is an MCP server for semantic image retrieval using ColPali and Elasticsearch. It indexes images or PDF pages and enables natural language search queries against the indexed content.

How to use Colpali MCP Server - Python?

Install by cloning the repository and running make install. Configure the server in your MCP client's mcp.json with the path to start_server.sh and environment variables for Elasticsearch (ES_HOST, ES_USER, ES_PASSWORD). The server exposes four tools: search_images, index_image, get_index_stats, and clear_index.

Key features of Colpali MCP Server - Python

  • Semantic image search using natural language queries
  • Automatic indexing of images and PDFs
  • Multimodal embeddings combining vision and language (ColPali)
  • Scalable storage with Elasticsearch
  • Standard MCP API compatible with any MCP client

Use cases of Colpali MCP Server - Python

  • Search for diagrams or schemas in a document database
  • Index and retrieve screenshots by textual description
  • Enable multimodal search across PDF archives
  • Build AI‑powered image retrieval systems for RAG workflows

Frequently asked questions

1

What dependencies are required?

Python 3.10+, Elasticsearch 8.0+, and a GPU is recommended for ColPali (though it also works on CPU).

2

Where are the image embeddings stored?

Embeddings and metadata are stored in Elasticsearch, accessed via the configured `ES_HOST`, `ES_USER`, and `ES_PASSWORD`.

3

How are images indexed?

Use the `index_image` tool to index images one by one (batch size of 1). PDFs can be indexed page by page by calling the tool for each page.

4

Is the clear_index tool reversible?

No, `clear_index` is irreversible and permanently removes all indexed data.

5

How is authentication handled?

Authentication is via the `ES_USER` and `ES_PASSWORD` environment variables passed to the server at startup.

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