A Model Context Protocol server for safely inspecting and profiling local analytical datasets.
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# Data Science Workspace MCP Server     A local-first **Model Context Protocol (MCP)** server for data science workspaces. `ds-workspace-mcp` gives MCP-compatible assistants structured access to analytical data without handing them arbitrary filesystem or database control. It exposes a guarded local workspace where an assistant can discover datasets, inspect schemas, profile columns, run bounded read-only queries, evaluate baseline modeling readiness, and create reusable markdown modeling reports. The project is built as a practical reference implementation for AI-assisted data tooling: typed Python, bounded outputs, path traversal protection, read-only SQL guardrails, release metadata, Docker support, and CI-backed documentation. ## At a glance | Area | What this server provides | | --- | --- | | Dataset access | Safe reads for CSV, Parquet, JSON, Excel, SQLite, and DuckDB-backed CSV queries | | Exploration | Preview, profiling, missingness summaries, categorical summaries, correlations, and issue detection | | Modeling readiness | Target suggestions, feature review, leakage checks, time-series validation, forecast baselines, and dummy baselines | | Report workflow | Generate, save, search, preview, compare, copy, rename, delete, and extract markdown report sections | | MCP integration | Resources, tools, prompts, stdio transport, Streamable HTTP transport, and runnable client examples | | Safety controls | Configured data/report roots, path traversal rejection, size limits, row limits, query length limits, and SQL timeouts | ## Current scope Today the repository covers five practical workflows: - Safe local dataset access for CSV, Parquet, …
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