Read and analyze local documents (PDF, Excel, CSV, Word, PowerPoint, images). No API keys needed.
An MCP (Model Context Protocol) server that lets AI assistants read and visually analyze local documents — PDFs, Excel spreadsheets, CSV files, Word documents, PowerPoint presentations, and images.
No API keys required. The host AI (GitHub Copilot, Claude, etc.) does all the reasoning directly.
| Format | Extensions | Read | Visual |
|---|---|---|---|
.pdf | ✅ | ✅ | |
| Excel | .xlsx, .xls | ✅ | ✅ |
| CSV / TSV | .csv, .tsv | ✅ | — |
| JSON | .json | ✅ | — |
| Word | .docx | ✅ | ✅ |
| PowerPoint | .pptx | ✅ | ✅ |
| Plain text | .txt, .md | ✅ | — |
| Images | .png, .jpg, .jpeg, .gif, .bmp, .tiff, .webp | — | ✅ |
| Tool | Description |
|---|---|
list_documents | List files under a directory, filtered by glob pattern |
document_info | Get metadata (size, modified date, sheets) for a file |
read_document | Extract text content from a document with pagination |
visual_evaluate_document | Return page images inline so the AI can analyze charts, tables, and diagrams |
Search for docalyze in the MCP server gallery (Extensions sidebar → MCP tab) and click Install.
pip install docalyze-mcp-server
npx docalyze-mcp-server
This requires uv or pipx installed — the npm wrapper calls uvx to run the Python package automatically.
Add to your VS Code mcp.json (or settings.json):
{
"servers": {
"docalyze": {
"type": "stdio",
"command": "python",
"args": ["-m", "docalyze_mcp_server"],
"env": {
"PYTHONIOENCODING": "utf-8"
}
}
}
}
Or, if you installed via pip and want to use the entry point:
{
"servers": {
"docalyze": {
"type": "stdio",
"command": "docalyze-mcp-server"
}
}
}
The base install handles PDF, Excel, CSV, JSON, and plain text. For additional formats:
# Word documents
pip install docalyze-mcp-server[docx]
# PowerPoint
pip install docalyze-mcp-server[pptx]
# OCR (requires Tesseract installed on your system)
pip install docalyze-mcp-server[ocr]
# Everything
pip install docalyze-mcp-server[all]
The server reads documents from a configurable root directory. Set the DOCUMENTS_ROOT environment variable to change it:
{
"servers": {
"docalyze": {
"type": "stdio",
"command": "docalyze-mcp-server",
"env": {
"DOCUMENTS_ROOT": "/path/to/your/documents"
}
}
}
}
If not set, it defaults to the directory containing the server script.
MIT
Source-derived launch command. Check the maintainer’s required arguments and credentials before running:
uvx docalyze-mcp-serverMerge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.
{
"mcpServers": {
"io-github-lunarperovskite-docalyze": {
"command": "uvx",
"args": [
"docalyze-mcp-server"
]
}
}
}Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.
Claude Desktop setup referencedocalyze-mcp-serverpypiio.github.LunarPerovskite/docalyze works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.
~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.~/.cursor/mcp.jsonRestart Cursor for changes to take effect..vscode/mcp.jsonReload VS Code window for changes to take effect.~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect..mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.