"author:mcp-tools" matching MCP servers:

Helps citizens and businesses navigate French bureaucracy with AI, covering taxes, social charges, benefits, invoices, administrative letters, collective agreements, and retirement.

Enables assessing database change risk, estimating impact, generating rollback plans, prechecking change windows, and orchestrating dry-run, canary, apply, and rollback with approval gates and auditable state transitions.

Enables cross-database and domestic-database migration analysis by checking type mappings, dialect gaps, NULL and charset/collation semantics, object parity, SQL translation, consistency verification with checksums and sampling, effort estimation, and migration planning/reporting.

Enables on-call database diagnostics: it classifies load pressure, traces root causes with hypothesis-evidence-confidence, detects blocking and lock chains, scores health across a dimension tree, and renders deliverable incident reports. Works from symptom text alone with no database connection or history required (Tier0), escalating to snapshot-based and trend-based analysis as observations accumulate.

Enables static, database-free SQL health checks that return rule-based findings and verdicts, propose executable fixes, and verify rewrites are equivalent while comparing execution plans and suggesting indexes. Supports batch/file/git-diff review for CI gating, all at zero-dependency Tier0.

Generates educational content like quizzes, lesson plans, flashcards, and grading rubrics from any topic via the Model Context Protocol.

Enables teams to structure metric definitions, compare semantic differences, detect JOIN fanout and drift, reconcile mismatched numbers layer by layer, and persist metric contracts as YAML.

Enables MCP clients to register and inspect database instance profiles, record observations and audits, compute scalar and quantile baselines, manage technical-debt findings and incident timelines, link related instances, and import/export sanitized knowledge packages.

Enables repair of malformed JSON strings into valid JSON for agent workflows by fixing common LLM formatting issues. Supports cleanup of code fences, smart quotes, unquoted keys, trailing commas, and missing brackets to ensure reliable data parsing.

Converts oversized JSON arrays into a compact toon-style textual representation to reduce token consumption and compress context for LLM agent pipelines.