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    Runs a regular expression on real engines — JavaScript (V8), Python (CPython re), PCRE2 and RE2 — reporting each engine's own compile errors with positions, every match with its groups and named groups, replacement results, and where the engines disagree. It also detects catastrophic backtracking (ReDoS), naming the at-risk part of the pattern, generating an attack string, and timing how long each engine actually takes on it.
    2
    1
    MIT
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    Scores any remote MCP server for readiness across the Claude Directory, ChatGPT Apps, Gemini Connected Apps, and MCP Registry, returning a 0–100 score, A–F grade, per-store status, failing checks, and one-line fixes. Also provides report links, badges, and explanations of each check.
    MIT
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    Enables agents to verify that a deploy actually worked by checking pages, links, and comparing environments, catching blank 200 responses, broken links, stray noindex, and staging that never updated.
    3
    MIT
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    Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.
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    MIT
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    Enables agents and users to validate Kubernetes manifests (YAML/JSON, multi-document) against the API of a chosen Kubernetes version without needing a cluster. It reports removed or deprecated APIs with replacements, unknown or misspelt fields, misconfigurations the API server would reject, Pod Security Standard violations, and common risks, each with file, line, and fix.
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    1
    MIT
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    Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.
    11 npm
    MIT
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    Serves the paper fact-checking checks over the Model Context Protocol, enabling an agent to recompute reported statistics, percentages and changes, compare text against tables, and verify references against Crossref, OpenAlex and arXiv. Each finding quotes the paper, shows the arithmetic and suggests a fix, with HTML, Markdown and JSON reports.
    MIT
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    Enables AI agents to submit npm, PyPI, Go, or Maven packages and manifests to the free OSV.dev database and receive per-package and per-advisory results with CVE mappings, CVSS scores, severities, and the versions that fix each issue. It also supports filtering by minimum severity, de-duplicating alias advisories, and machine-readable error/warning rows for CI gates and upgrade recommendations.
    3
    MIT
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    Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.
    5
    23 npm
    MIT
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    Enables agents to turn a plain-language description of a device into a complete, validated build package — parts list, 3D-printable parts, wiring, firmware code, picture assembly guide, and tests — that anyone can assemble without electronics experience.
    1
    MIT
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    Free JSON Schema validation, JSON to TypeScript, CSV to JSON, JSON repair and JSON to CSV for AI agents. Hosted MCP plus local STDIO. Connect once with a scoped key; enforce tool allowlists and quotas, inspect request IDs. Runnable repair → validate → CSV workflow. Bounded OpenAPI and webhook checks. No LLM calls or payments. Setup: invokeport.com/agents.
    3
    94 npm
    MIT
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    Enables MCP clients such as Claude Desktop and Claude Code to read café rules, rota, and staff data, check proposed rota changes against the owner's rules, and evaluate customer chat replies before sending. Every tool is read-only, with code checks applied first and AI judging used only when needed.
    MIT
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    Enables an AI assistant to run pre-ingest QA on utility power-grid GIS exports: score readiness, list issues with coordinates, reconcile registers against survey/LiDAR detections, map codes to library types, and draft a data request. It exposes the same checks, reconciliation and reporting operations as callable tools so a deployment engineer can work conversationally in any MCP client.
    MIT
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    Programmatic email deliverability testing for AI agents. Create inbox placement tests across Gmail, Outlook, Yahoo, Mail.ru, Yandex — get per-provider placement (Inbox/Spam/Promotions), SPF/DKIM/DMARC auth, Rspamd & SpamAssassin verdicts, DNS health (MX, PTR, DNSBL), and live SSE results.
    5
    72 npm
    2
    MIT