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DataSnipe gives your AI assistant a reliable way to work with PDFs, scans, and images. Ask it to find the fields you need, review the evidence, and deliver consistent, traceable results.

Claude.ai · DataSnipe

Let DataSnipe suggest a schema for https://arxiv.org/pdf/2601.14701

Used DataSnipe integration · 3 notes

I’ll fetch that public paper and ask DataSnipe to suggest an extraction schema.

Upload URL

The PDF is stored. Now I’ll ask DataSnipe to suggest a schema for it.

Suggest extraction schema

DataSnipe fetched the paper and suggested this extraction schema:

Field	Type	Description
`title`	string	The title of the research paper
`authors`	string	The authors of the paper
`publication_date`	date	The date of publication
`arxiv_id`	string	The unique arXiv identifier if available
`abstract_summary`	string	A brief summary of the paper’s main objectives or findings

Want me to run the extraction with this schema as-is, or refine it first?

Add a list field for findings, and proceed with the extraction. Use a cheap model.

Run extraction · 1 note

I’ll add a findings list field and run the extraction with gemini-3.1-flash-lite.

Run extraction

Extraction is running with the updated schema using gemini-3.1-flash-lite.

Review and download the results in DataSnipe

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Used DataSnipe integration · 1 note

Get job group

The job’s done. Pulling the results.

Get extraction results

Extraction complete. Here’s the report for arXiv 2601.14701:

Title: Regulatory Expectations for Bayesian Methods in Drug and Biologic Clinical Trials: A Practical Perspective on FDA’s 2026 Draft Guidance

Author: Yuan Ji, PhD
Published: January 2026
arXiv ID: 2601.14701v1

Abstract summary: A practical, regulatory-oriented synthesis of the FDA’s January 2026 draft guidance on Bayesian methodology in clinical trials, focusing on success criteria, prior specification, operating-characteristic evaluation, and computational transparency.

Key findings

Success criteria must be defined via posterior probability with a justified, meaningful threshold.
Prior specification requires a documented rationale, robustness methods, and a plan for prior-data conflict.
Operating characteristics must be evaluated through prospective simulation across multiple scenarios.
FDA guidance permits frequentist-calibrated and purely Bayesian designs when operating characteristics are agreed.

Review or download the full results in DataSnipe

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DataSnipe brings consistent, auditable, repeatable data tables to agents through the open MCP standard.

Why not just let the agent handle it?

Move from one-off answers to a repeatable workflow.

A chat upload can answer a question. DataSnipe is for work where the same fields need to hold across different documents, layouts, and assistants.

One schema across every file

Define named fields and types once. DataSnipe applies that declared shape across the batch instead of asking each conversation to invent its own output.

Confidence that guides review

Set a confidence cutoff for collated results, then use the review page to inspect model-reported confidence and source evidence behind each field.

Review at the source

Follow the review link to compare extractions with source snippets and page numbers, with visual highlights where available.

See the typed workflow and API contract

Getting started

Connect once. Then just share your files and ask for the data.

You do not need to learn the technical details: ask your assistant to connect to the DataSnipe MCP, and it will guide you from there.

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Start with a question

Give it your documents and describe the fields you want back.

A small safety step for local files. Some assistants ask you to allow network access to datasnipe.app before they can send a file from your computer or a chat attachment. This is a one-time safety setting controlled by the assistant, not your DataSnipe account.

Claude.ai setup

Add DataSnipe to Claude.ai. Get started in seconds.

Four steps

Connect DataSnipe

01
Open Connectors
In Claude.ai, open Settings and select Connectors.
02
Add a custom connector
Select Add, then Add custom connector.
03
Enter the DataSnipe server
Name it DataSnipe and enter https://datasnipe.app/mcp.
04
Authorize access
Follow the prompt to sign in to DataSnipe and approve the connection.

Claude.ai setup flow from Connectors to the custom connector form filled with the DataSnipe name and MCP server URL — Add a custom connector named **DataSnipe** using the DataSnipe MCP server URL.

DataSnipe connector screen leading to the authorization page where Claude requests access to the user's DataSnipe account — Select **Connect**, then approve Claude’s access to your DataSnipe account.