arquero Connector for AI agents.
1 live capability
Perform high-speed data analysis and CSV aggregations on massive datasets.
Waiting for input…
Why people use arquero
DataFrame Aggregator Engine for High-Performance CSV Analysis
With this Connector, you just tell your agent what you want to see. It sends the request to the engine, which crunches the millions of rows on your hardware. You get the exact totals and averages you need in seconds. It is like having a data scientist who never makes a math error.
What Vinkius changes
Your AI handles the logic while a dedicated engine handles the math.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Summarizing regional sales
A sales lead wants a regional revenue summary from a 500k row export.
- Real-world use case 02
HR department salary analysis
An HR manager needs to find average salaries by department from a massive employee file.
- Real-world use case 03
Logistics shipment counts
A logistics coordinator wants to count active shipments per country from a 4 million row log.
Complete set · 1capability
The complete arquero capability set.
These are the exact actions your AI can choose when you ask it to work with arquero.
01
1 capability in this set.
Part of 1 available through arquero.
- 01 Capability
Aggregate dataframe
Perform fast, deterministic GroupBy, Pivot, and Aggregations on massive CSV strings. This lets your agent handle huge datasets that would normally crash a standard chat window.
Set up in minutes
One URL. Then ask arquero to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use arquero from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it arquero, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable arquero for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the arquero URL.
- Step 03
Save and start
Save the connection and enable arquero in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"dataframe-aggregator-engine": {
"url": "https://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using arquero
Open Agent mode in chat and ask: "Using arquero, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"dataframe-aggregator-engine": {
"url": "https://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using arquero
Ask Copilot: "Using arquero, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"dataframe-aggregator-engine": {
"url": "https://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using arquero
Open Cascade and ask: "Using arquero, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"dataframe-aggregator-engine": {
"url": "https://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using arquero
Ask Cline: "Using arquero, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add dataframe-aggregator-engine --transport http "https://edge.vinkius.com/vk_preview_bJtQDTXCWq4XDZooGgwkm8PaGBY9YZu2Ymvsdmvb/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using arquero
Ask Claude: "Using arquero, show me...". 1 tools are ready
Where the request belongs
Work arquero can move forward.
This is for the data professional who is tired of the AI crashing whenever they try to analyze a real world export. It is for anyone who needs 100% accurate math on large files.
Data Analyst
They use this to summarize massive CSV exports without having to manually open Excel and write formulas.
Operations Manager
They use it to get instant regional performance summaries from huge logs without hitting context limits.
Finance Professional
They use it to calculate precise totals on millions of transactions where a single hallucinated digit is not an option.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsStatistics Engine
A zero-latency statistical engine to process datasets. Instantly compute the exact mean, median, mode, standard deviation, and percentiles completely local.
Deterministic Array Operations
Equip your AI with high-performance array manipulation. Deterministically chunk large datasets, deep-deduplicate objects, and intersect arrays 100% local.
Outlier Detection Engine
Identify statistical anomalies in massive datasets local using deterministic Z-Score and IQR methods. Stop LLMs from guessing which rows are outliers.
Portfolio CSV Analyzer
Parse massive CSV exports from brokers like DEGIRO or XTB instantly. Streams financial data locally to prevent AI crashes, returning clean column schemas and sample data.
CSV Structural Scan and Deduplicator
Analyze CSV structure, detect delimiters, find empty cells, and remove duplicate rows.
CSV Structural Scan and Deduplicator Alternative
Analyze CSV structure, detect delimiters, find empty cells, and remove duplicate rows.
Bring your own AI
Change the model, client or framework. Keep arquero connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about arquero.
The practical details behind the request, access and result.
Can the DataFrame Aggregator Engine handle millions of rows?
Yes, it is designed specifically for massive files. It uses a high-performance engine to process data locally, so it won't crash your AI client or hit context limits.
Will the math be accurate?
Yes, it is deterministic. Instead of the AI guessing the numbers, the Connector uses a dedicated data engine to perform the actual calculations, ensuring perfect sums and averages.
Does this work with any CSV file?
As long as your data is in a CSV format, your AI client can use this Connector to perform GroupBy, Pivot, and Aggregation operations on it.
How much will I save on tokens?
You will save a lot. Because the AI only sees the final aggregated result rather than every single row of your raw data, you are not wasting tokens on unnecessary information.
Is my data private?
Yes, the data processing happens on your local machine. The Connector handles the heavy lifting locally, so your raw data does not need to be sent to the AI model for calculation.
Can I do multiple things at once?
You can. This Connector allows you to apply different types of aggregations to different columns in a single request, like getting a sum for one column and an average for another.
What is the maximum CSV size supported?
The engine runs locally via Node.js, meaning it can handle gigabytes of CSV data as long as your machine has sufficient RAM. There is no artificial size cap.
Which aggregation functions are supported?
Currently: sum, mean, count, min, and max. You can map different columns to different aggregations in a single call (e.g., sum Revenue and count Orders simultaneously).
Why use Arquero instead of sending the CSV to the AI?
LLMs charge per token. A large CSV can cost dollars per query and the math will be hallucinated. Arquero is free, local, and processes data with mathematically perfect deterministic precision.
One connection away
Give your agent a direct line to arquero.
Connect arquero once. Keep it beside 6,100+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available