precis-mcp
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- AlicenseNot gradedqualityAmaintenanceLocal-first MCP server for data quality that finds suspicious data, explains findings with evidence, tracks drift, and supports human-approved, reversible repair workflows. Deterministic by default, with AI optional.108 PyPI2Apache 2.0
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- AlicenseAqualityCmaintenanceEnables a language model to run four deterministic data checks it cannot argue with — column-contract validation with an explicit quarantine list, arithmetic reconciliation of opening/movements/closing, distinct entity counting against row counts, and duplicate detection returned for human review — each answering with structured findings and row/column locators. Failures come back as results with reasons, so the model reports exactly which rows and columns are broken rather than asserting the data is clean.4MIT
TDQS
Scored across 4 tools
Each tool has a largely distinct purpose: validate_data checks data, check_config inspects a project's config files, describe_constraints serves as generic reference documentation, and infer_schema produces draft schemas. The main overlap is between validate_data and check_config, since both are used for diagnosing failures, but the descriptions explicitly frame check_config as the follow-up to a validate_data failure, which mitigates most confusion.
All four tools follow a clean verb_noun snake_case convention: validate_data, check_config, describe_constraints, infer_schema. There are no deviations or mixed conventions.
Four tools is a good fit for a focused validation/diagnostics server; each tool serves a clear purpose (validate, diagnose config, reference docs, infer schema). It is on the lighter side, so slightly under-scoped but not thin enough to be a problem.
The read-only validation lifecycle is well covered: validate, diagnose, reference constraint types, and draft schemas. Minor gaps exist, such as no tool to inspect an existing saved schema or enumerate a project's declared constraints, but these are workarounds rather than blockers.