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Unstructured API MCP Server for Research Paper Data Processing

@HeetVekariya

リポジトリ

Unstructured API MCP Server for Research Paper Data Processing について

GitHub repository for Unstructured MCP Hackathon.

#mcp#unstructured#mcp-server

基本情報

カテゴリ

データと分析

ランタイム

jupyter notebook

トランスポート

stdio

公開者

HeetVekariya

設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

ツール

18

Lists available sources from the Unstructured API.

Get detailed information about a specific source connector.

Update an existing google source connector by params.

Delete a source connector by source id.

Lists available destinations from the Unstructured API.

Get detailed info about a specific destination connector. Currently, we have s3/weaviate/astra/neo4j/mongo DB (more to come!)

Create a mongodb destination connector by params.

Update an existing mongodb destination connector by destination id.

Delete a mongodb destination connector by destination id.

Lists workflows from the Unstructured API.

Get detailed information about a specific workflow.

Create a new workflow with source, destination id, etc.

Run a specific workflow with workflow id

Update an existing workflow by params.

Delete a specific workflow by id.

Lists jobs for a specific workflow from the Unstructured API.

Get detailed information about a specific job by job id.

Delete a specific job by id.

概要

What is Unstructured API MCP Server for Research Paper Data Processing?

The Unstructured API MCP Server for Research Paper Data Processing is an MCP server that uses the Unstructured API to extract meaningful information from research paper PDFs. It converts unstructured documents into structured JSON data, which can then be used to fine‑tune language models and reduce literature review time for researchers.

How to use Unstructured API MCP Server for Research Paper Data Processing?

Install dependencies (uv add "mcp[cli]", uv pip install --upgrade unstructured-client python-dotenv) and set up environment variables (UNSTRUCTURED_API_KEY, GOOGLEDRIVE_SERVICE_ACCOUNT_KEY, MONGO_DB_CONNECTION_STRING). The server provides 18 tools for managing sources, destinations, workflows, and jobs. Integrate with Claude Desktop by adding the server configuration to claude_desktop_config.json. For local development, run the server over SSE and a minimal client.

Key features of Unstructured API MCP Server for Research Paper Data Processing

  • Connects to Google Drive to fetch research paper PDFs.
  • Sends structured data to a MongoDB destination connector.
  • Offers 18 tools for source, destination, workflow, and job management.
  • Supports auto partitioning, chunking, NER enrichment, and embedding.
  • Integrates directly with Claude Desktop via MCP configuration.

Use cases of Unstructured API MCP Server for Research Paper Data Processing

  • Researchers automatically extracting key information from many PDFs.
  • Building a pipeline to feed structured research data into a fine‑tuning workflow.
  • Creating, running, and monitoring document processing workflows end‑to‑end.
  • Reducing manual literature review time by converting papers into analyzable JSON.

FAQ from Unstructured API MCP Server for Research Paper Data Processing

What environment variables are required?

UNSTRUCTURED_API_KEY, GOOGLEDRIVE_SERVICE_ACCOUNT_KEY, and MONGO_DB_CONNECTION_STRING. A .env.template file is provided.

How do I integrate this server with Claude Desktop?

Add the server configuration to claude_desktop_config.json under mcpServers.UNS_MCP, specifying the uv command and environment variables. Restart Claude Desktop afterward.

How can I test the server during development?

Use Anthropic’s MCP Inspector by running mcp dev uns_mcp/server.py. You can set environment variables and test all tools interactively.

Can I run the server locally without Claude Desktop?

Yes. Run the server with uv run python uns_mcp/server.py --host 127.0.0.1 --port 8080 and connect a minimal client via SSE at http://127.0.0.1:8080/sse.

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