Unstructured API MCP Server for Research Paper Data Processing
@HeetVekariya
About Unstructured API MCP Server for Research Paper Data Processing
GitHub repository for Unstructured MCP Hackathon.
Config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTools
18Lists 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.
Overview
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.
Frequently asked questions
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`.
Basic information
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