Guarded Data MCP

An MCP server project for controlled local data repair: an agent investigates a dataset and proposes changes, while a human approves an exact plan and the server enforces its permitted effects.

Status: M1 import/inspection slice implemented. Import CSV/Parquet through a human CLI, preserve the source and typed version in SQLite, then inspect it through two real MCP tools. Deterministic tests and a real stdio evaluation harness are included. Quality rules, repair/approval execution and real-agent evaluations remain later milestones.

The problem

Giving an agent a general SQL or Python executor makes its effective permissions much broader than a data-cleaning request. Two similar rows are not necessarily duplicates, a missing value does not establish its replacement, and a successful tool call does not establish that the data is correct.

This project chooses a bounded repair surface and explicit human authority. Its initial repair families are exact deduplication under an approved unique-key rule, normalization through approved mappings and quarantine with preserved content and reasons.

Related MCP server: peekmcp

Accepted design

• Local Python server using the official MCP SDK over stdio; human CLI for import, contract activation, plan approval and export.

• Registered, single-table CSV/Parquet datasets; preserve the original and repair a versioned working copy.

• SQLite owns data, quarantine, approvals and durable receipts; PyArrow handles interchange.

• Approval binds the exact plan and reviewed data/contract versions. Application is atomic and replay returns the original receipt.

• Structured operations rather than arbitrary SQL/Python; unresolved conflicts remain explicit.

• Deterministic protocol evaluations and a separate real-agent suite, with full-state outcome checks and published failures.

The stated trust boundary is an agent limited to the exposed MCP tools. It does not establish protection against an agent with unrestricted access to the host operating system.

Tool surface and implementation status

There is no agent-side import or approval tool. The M1 server registers only the two implemented read tools. Inspection explicitly returns quality_status: not_evaluated rather than treating a successful profile as proof of clean data.

Try the first slice

uv sync --locked
uv run python -m evals.runner

This self-contained evaluation imports synthetic data through the human CLI, starts the actual stdio server, checks output schemas and expected refusals, and compares the entire persisted dataset against an independent expected result. It preserves duplicates, leading-zero IDs, exact decimals and null versus empty text. It uses no model or network data.

Follow the M1 walkthrough to review/import a dataset and connect a client.

The recorded M1 results contain nine passing deterministic cases in each of two negotiated protocol modes: automatic (2026-07-28) and legacy (2025-11-25), using the pinned SDK. These results do not certify other clients or an agent's decisions.

Development

Python 3.13 and uv are required. Use the project-owned environment:

uv sync --locked
uv run python scripts/check_docs.py
uv run ruff check .
uv run ruff format --check .
uv run python -m pytest -q
uv run python -m evals.runner --mode legacy

CI runs documentation/lint/format checks and behavioral tests on Windows and Ubuntu. The protocol tests run the evaluation harness with the pinned SDK's automatic and legacy negotiation modes. They are deterministic protocol evaluations, not real-agent runs. Desktop host products are not yet individually certified.

Decisions and delivery

• Architecture decision index: accepted choices, alternatives and consequences.

• Technical specification: scope, invariants and remaining implementation details.

• Development plan: deliverables, dependencies and acceptance gates.

• Glossary: domain terminology.

• Discovery context: how the accepted scope emerged.

The public demonstration will use a pinned, minimized NYC 311 sample and clearly labeled synthetic defect variants. NYC data is not yet distributed; M1 fixtures are project-authored synthetic records. Source terms and provenance must accompany any future external data; the code license does not relicense that data.

License

MIT for the code and project-authored documentation. External datasets retain their source terms and attribution.