datacharter/datacharter
by Various
Your data, explored locally — and your AI agents kept on a leash. A federated data explorer with governed agentic access.
MCP
datacharter/datacharter
Added 3 Sept 2026
Overview
A federated data explorer that runs locally and provides governed agentic access for AI agents. Built in Python, it lets you explore data across sources while keeping agent actions constrained.
Best for
Best for
Python developers who want local, governed data access for AI agents.
Use cases
- Explore data locally across federated sources
- Give AI agents governed, constrained access to datasets
- Keep sensitive data on-premises while enabling agent queries
How to use
Install
uvx datacharter serve # needsTested with
Claude Code, Cursor, Cline, ChatGPT
Notes
A federated data explorer that runs locally and provides governed agentic access for AI agents. Built in Python, it lets you explore data across sources while keeping agent actions constrained.
12 stars on GitHub. Last updated 2026-09-02. Licensed Apache-2.0.
Use cases
- Explore data locally across federated sources
- Give AI agents governed, constrained access to datasets
- Keep sensitive data on-premises while enabling agent queries
Pros
- Local execution keeps data on your own machine
- Federated design supports multiple data sources
- Governed access helps control what agents can do
Cons
- Very few stars, indicating limited adoption
- Python-only implementation may not fit all stacks
- Governance features are not described in detail
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Local execution keeps data on your own machine
- Federated design supports multiple data sources
- Governed access helps control what agents can do
Cons
- Very few stars, indicating limited adoption
- Python-only implementation may not fit all stacks
- Governance features are not described in detail
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