# GetQueryly Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## GetQueryly

Upload spreadsheets and get AI analysis with charts, insights, and quality scores

- Record: https://musedirectory.ai/connector/getqueryly
- Category: Research & Data
- Developer: thesinbin12-jpg (https://getqueryly.com)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Having trouble, 285ms, checked 2026-10-29T11:45:54Z
- Endpoint: https://getqueryly.com/mcp/mcp
- Auth: Needs an access key; Pricing: unknown
- Screening: Screened, no issues found (2026-10-29T04:36:29Z)
- Source: Found in the official MCP Registry (io.github.thesinbin12-jpg/getqueryly) https://registry.modelcontextprotocol.io/v0/servers?search=io.github.thesinbin12-jpg%2Fgetqueryly

Connects to GetQueryly's AI data analysis platform. Upload CSV, Excel, JSON, PDF or images to get plain-English analysis, auto-generated charts, statistical tests, health checks, and executive dashboards. Supports knowledge graphs and pricing elasticity analysis.

Example request: "Upload my sales spreadsheet and show me the top trends and a chart of revenue by region"

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use GetQueryly to help me. It is a free service with an MCP server at https://getqueryly.com/mcp/mcp. It needs an API key from thesinbin12-jpg; ask me to enter it through your secure credential prompt. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- ai_chat: Chat with getqueryly AI. Ask questions, generate content, search the web. Costs 1 Make.
- ds_upload: Upload a data file for AI analysis. Supports CSV, Excel, JSON, PDF, images, SPSS, Parquet. Returns session_id. Free.
- ds_query: Ask a question about uploaded data. AI writes and runs Python code. Returns analysis with charts. Costs 1 Make.
- analyze_file: ONE-CALL deep analysis (v2): upload a file and get quality score, top insights, a summary and the headline chart — no session juggling. Without a question: full analysis (2 Makes). With a question: answers just that question (1 Make). Returns structured JSON.
- ds_health_check: Run a data health check. Detects missing values, outliers, type issues, duplicates. Costs 2 Makes.
- ds_insights: Get automatic insights from data: patterns, anomalies, trends, correlations. Costs 3 Makes.
- ds_swarm: Run analyst swarm: multiple AI personas analyze your data independently, then synthesize consensus. Depth: quick (5 Makes) or full (8 Makes).
- ds_report: Generate a PDF report from data analysis. Includes charts, stats, insights. Costs 3 Makes.
- smart_test: AI picks the right statistical test for your data and runs it. Costs 1 Make.
- chart: Generate a chart from data. Ask for any chart type: bar, line, scatter, heatmap, box plot, treemap, etc. Costs 2 Makes.
- generate_diagram: Generate a diagram from description. Types: flowchart, org_chart, process, mindmap, tree. Palettes: blue, green, purple, dark, warm. Costs 1 Make.
- generate_org_chart: Generate an org chart. names_json: JSON array of [name, role, parent_name_or_null]. Example: [["CEO John", "CEO", null], ["VP Sarah", "VP Engineering", "CEO John"]]. Costs 2 Makes.
- create_infographic: Create a professional infographic. stats_json/bars_json/progress_json: JSON arrays. Themes: corporate, vibrant, clean, nature, sunset. Costs 1 Make.
- create_comparison: Create a side-by-side comparison infographic. Costs 1 Make.
- kaggle_search: Search 200K+ Kaggle datasets by keyword. Returns dataset refs, titles, license info, download counts. Free.
- kaggle_load: Download a Kaggle dataset and create an analysis session. Returns session_id for queries. Free.
- kaggle_analyze: Search Kaggle, download dataset, and analyze it in one step. Costs 2 Makes for the query.
- get_usage: Check your getqueryly Makes balance and usage. Free.
- get_costs: Get the Makes cost table for all operations. Free.
- ds_graph: Build or query a knowledge graph for an uploaded dataset (GraphRAG). action='build' turns the data into a graph and returns a summary. action='query' answers a question over the graph (e.g. 'which products share the most suppliers?'). Costs 1 Make.
- ds_elasticity: Pricing sensitivity: if the dataset has discount + margin columns, compute per-segment margin sensitivity to discount (slope, R-squared, confidence, recommendation). Costs 1 Make.
- ds_dashboard: Auto-generate a Power BI-grade executive dashboard (KPIs, charts, pricing elasticity, impact/effort matrix, takeaways) from the uploaded dataset. Costs 2 Makes.

Screening checks:
- MCP handshake: pass (Answered in 441ms)
- Domain against threat feeds (Cloudflare security DNS): pass (getqueryly.com not flagged)
- Published packages against the OSV malicious-package database: n/a (No npm or PyPI package published)
- Hidden instructions or invisible characters in tool text: pass (22 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (Domain registered 70 days ago)
- AI review of purpose and tool behavior: pass (No concerns)