MCP Analytics
MCP Analytics lets you upload data, discover and run statistical/ML analysis tools, and generate shareable reports, all through an AI chat interface.
Get platform info, pricing, usage stats, and documentation
Discover analysis tools matching your data or question
Inspect tool schemas and detailed tool information
Run analysis tools to produce interactive HTML reports
Upload, list, read, download, and update datasets
List and query live data connectors (e.g., GA4, Google Search Console)
List, search, and view analysis reports and report cards
Get conversational guidance via agent_advisor
Check billing status, usage, or open the billing portal
Request custom analysis modules for specific use cases
Allows for statistical analysis and reporting on eBay data to transform business questions into actionable insights.
Provides native connectors to pull live Google Analytics 4 data for advanced modeling and seasonal analysis.
Connects to Google Search Console to retrieve and analyze search performance data for causal and trend analysis.
Enables analysis of Shopify order and store data to perform statistical modeling, sales forecasting, and customer segmentation.
Integrates with Stripe data to perform financial analytics, including revenue forecasting, churn prediction, and regression modeling.
Supports analyzing WooCommerce data to provide business insights, customer lifetime value (LTV) analysis, and seasonal trend detection.
This server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
MCP Analytics
A statistical analyst in your AI chat. Bring a dataset and a question, get back a citable, re-runnable report with its method named.
MCP Analytics is a Model Context Protocol server that answers business and statistical questions about your data, as deep as each question needs:
Answer: a quick question about the data, read from all of it, shown with how we got it, and saved.
Analytics: a tested statistical method runs on the data, and we interpret it with charts and a written report.
Custom Analytics: a question only your business asks, built for your data from proven models, yours to re-run on next month's file.
Intelligent routing picks the depth: you describe the question, and the server says which path answers it and why. Analytics is repeatable: same data, same numbers. Every report names its method and the code that ran it, and runs again on new data.
It is for business and statistical questions about data, not a general-purpose assistant.
Website: mcpanalytics.ai · Free tools you can try in the browser: mcpanalytics.ai/free
Connect
Endpoint: https://api.mcpanalytics.ai/mcp/api-key (Streamable HTTP, JSON-RPC)
Authentication: an API key in the X-API-Key header. Create an account at mcpanalytics.ai and copy your key from your account settings.
{
"mcpServers": {
"mcpanalytics": {
"url": "https://api.mcpanalytics.ai/mcp/api-key",
"headers": { "X-API-Key": "mcp_your_key_here" }
}
}
}Claude Code:
claude mcp add --transport http mcpanalytics https://api.mcpanalytics.ai/mcp/api-key --header "X-API-Key: mcp_your_key_here"Browse before you sign up. Without a key the server answers in discovery mode: you can connect and list the tools, but not run anything.
curl -X POST https://api.mcpanalytics.ai/mcp/api-key \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'Related MCP server: MCP Tabular Data Analysis Server
Start
Ask your AI assistant a question about your data, for example:
"Upload sales.csv. What changed in revenue from May to June, and which channel drove it?"
"Which customers are most likely to churn?"
"Forecast next quarter's sales from this file."
The first call is the resolver: it reads your question and your data, executes nothing and charges nothing, and names the path that answers it (an answer, a library analysis, or a custom build) with the reason. Then it runs that path.
What you get
A report with charts, tables and a written interpretation, with its method named and the code that ran it.
Re-runs on new data: run the same analysis on next month's file.
A library of your reports you can search and ask questions across.
Data and privacy
Your uploaded data is used only to answer your questions. See the privacy policy for what we keep and for how long. Security issues: see SECURITY.md.
Support
Website: mcpanalytics.ai
Email: support@mcpanalytics.ai
License
See LICENSE.
Available Tools
19 toolsagent_advisorCInspect
Conversational AI that guides analysis and interprets results.
Inputs: message (required).
connectors_listAInspect
List available data connectors — GA4, Google Search Console, and more.
Inputs: no named parameters.
connectors_queryBInspect
Pull live data from a connected source using connector:// URIs.
Inputs: uri (required).
datasets_downloadAInspect
Generate a single-use download token for securely downloading datasets.
Inputs: uuid (required).
datasets_listAInspect
List and search uploaded datasets with fuzzy matching.
Inputs: search, limit.
datasets_readBInspect
Read dataset contents — preview rows, columns, and types.
Inputs: uuid (required), secret, rows.
datasets_updateCInspect
Update dataset metadata — name, description, tags, visibility.
Inputs: uuid (required).
datasets_uploadAInspect
Generate a secure upload token for CSV files. Returns UUID + curl command for the user.
Inputs: expires_in.
discover_toolsAInspect
Find analysis tools matching your data or question. Semantic search across 50+ statistical and ML tools.
Inputs: query, dataset.
module_requestBInspect
Request a custom analysis module to be built for your use case.
Inputs: description (required).
report_cardsCInspect
Get individual card data from a report for rendering.
Inputs: processing_id (required).
reports_listCInspect
List analysis reports with metadata.
Inputs: limit.
reports_searchBInspect
Search reports by job ID, tool name, or keyword.
Inputs: query, job_ids.
reports_viewBInspect
View a specific report by processing ID.
Inputs: processing_id (required).
tools_infoAInspect
Get detailed information about a specific analysis tool — use cases, assumptions, data requirements.
Inputs: tool_name (required).
tools_runCInspect
Execute an analysis tool. Returns a shareable interactive HTML report URL.
Inputs: tool_name (required), taskList (required).
tools_schemaBInspect
Get JSON schema for a tool — column_mapping and module_parameters required before tools_run.
Inputs: tool_name (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 19 tools
Most tools are grouped by resource and action, but there is some overlap: reports_view vs reports_list vs report_cards could be confused, and datasets_read vs datasets_download may seem similar at first glance. tools_info, discover_tools, and agent_advisor also all occupy a 'help me use the system' space that requires careful reading.
The naming has a recognizable pattern for the main clusters (datasets_*, reports_*, connectors_*, tools_*), but it is not consistently applied: report_cards and module_request are noun-only, while billing and about are bare nouns. The pattern is predictable within each domain but not uniform across the server.
19 tools is on the heavy side, but the platform covers datasets, connectors, analysis tools, reports, billing, and system info, so the breadth is somewhat justified. Still, some clusters could be consolidated (e.g., reports_list vs reports_search) to reduce cognitive load.
The main workflow — upload/read datasets, query connectors, discover and run tools, and view reports — is well covered. The primary gap is the lack of delete/removal operations for datasets and reports, plus no obvious connector setup or management tools, but most core analysis workflows are supported.
Maintenance
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