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"Using Laravel Helper Functions and Resolving MySQL Table Query Errors" matching MCP servers:

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    Behavioral verification intelligence for AI coding agents. Reads a TypeScript or JavaScript repo, clusters functions into 25 semantic workflows (Authentication, Payments, Webhooks, Caching, Queue, and more), and emits concrete adversarial probes per workflow. 17 MCP tools, local SQLite state, zero cloud.
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    MIT
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    Generates realistic, referentially-coherent test data (SQL INSERTs, JSON, or CSV) from your database schema, resolving foreign keys and respecting constraints. Paste CREATE TABLE DDL or a JSON schema and get ready-to-run seed data with valid relationships.
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    MIT
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    Fact-checks and fixes AI outputs by catching hallucinations, repairing broken JSON, and correcting errors before they reach users, with tools for verification, validation, and correction.
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    MIT
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    Enables LLMs to automatically diagnose coding errors through codebase search, test execution, and live debugger integration (DAP/V8 CDP). Provides a secure, policy-gated environment for investigating failures while preventing destructive operations.
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    Point your coding agent at a URL and get a real-browser QA audit: broken signup/login/checkout flows, JS console errors, missing analytics, consent + security headers, mobile tap targets, and accessibility — returned as machine-verified findings graded A-F.
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    Apache 2.0
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    A security audit server for Laravel projects that performs static code analysis, dependency CVE checks, and configuration audits. It enables developers to detect vulnerabilities like SQL injection and XSS while providing active attack simulations directly within MCP-compatible IDEs.
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    AGPL 3.0
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    Let AI agents inspect and drive running WPF (.NET desktop) apps: visual tree, dependency properties, data bindings and binding errors, DataContext, screenshots (popups included), plus click, select-item, text input, keyboard shortcuts, live property editing and wait-for — via runtime injection, with no changes to the target app.
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    MIT
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    AI-powered characterization test generator that reads Python functions or class methods, synthesizes inputs, captures behavior in a sandbox, and emits pytest files to lock legacy code behavior for safe refactoring.
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    Apache 2.0
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    MCP server for AI agents to compile, playtest, and exhaustively explore ink interactive fiction stories, catching compile errors, runtime errors, unreachable content, and verifying story structure.
    Last updated
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    MIT
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    Static analysis engine that detects schema mismatches between data producers (like MCP servers) and consumers (like client code), preventing runtime errors by validating contracts at development time.
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    11