"Blender MCP: Possible Uses and Contexts" matching MCP servers:

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Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.

CodebaseAgent-MCP is an MCP server that uses a lightweight RAG pipeline to delegate repository analysis to a dedicated OpenAI-compatible LLM, reducing token consumption by having the primary assistant receive only the relevant findings instead of repeatedly scanning the entire codebase.

Scans source code for possible Korean compliance risks (PIPA, Network Act, Credit Information Act, E-Commerce Act) and maps findings to specific legal provisions. Runs locally, exposes MCP tools for scanning and law lookup, and does not constitute legal advice. That's hedged (matches the project's "possible risk / not legal advice" rule), keyword-rich for search, and names all four laws.

Audits local package folders to generate publish-readiness reports and previews of NPM tarball contents. It helps developers identify and resolve configuration issues like missing types or broken exports before releasing code.

Structured .aide spec files that give AI agents progressive disclosure into your codebase architecture via MCP.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

A TypeScript/JavaScript refactoring MCP server that uses the TypeScript compiler to perform safe, type-aware code transformations such as renaming, extracting functions, and organizing imports across your codebase.

A project-aware AI design and code review server that uses dual reviewers via OpenRouter to catch context-blind errors in AI-generated code, optionally integrating with Serena for long-term project memory.

Enables local npm supply-chain inspection through tools for repository scanning, SBOM generation, dependency audit, and evidence reporting. Uses deterministic mock data and does not query live APIs.

Enables AI coding assistants to retrieve exact, version-correct icon names and import statements from the icon packages a project actually uses, eliminating guesses and build failures.

AI-powered dependency vulnerability and breaking change analyzer that scans dependencies, identifies vulnerabilities via OSV.dev, and uses AI to assess real impact and suggest fixes.

An MCP server that uses the CFR decompiler to convert Java .class and .jar files back into readable source code. It supports single-file, batch, and recursive directory decompilation with automated CFR tool management.

An adversarial code verification system that uses a Verifier-Critic debate loop to systematically uncover security, correctness, and performance issues. It implements the Socratic method to provide deep semantic analysis and dialectical reasoning beyond traditional static linting.

Enables deterministic verification for AI assistants by executing Python code that uses symbolic engines like SymPy and Z3 for math, logic, and code analysis.

An MCP server that uses AI to analyze code diffs and transform vague commit messages into readable, conventional commit history. It helps maintain project documentation quality by challenging unclear messages and suggesting improvements based on actual code changes.

A read-only MCP evidence server for chip verification and RTL debugging that exposes structured design and waveform data from Synopsys Verdi databases via MCP tools and CLI. It provides auditable evidence like contexts, objects, connectivity, traces, waveforms, and source mappings without modifying RTL or design DBs.

BlueMouse is the "Prefrontal Cortex" for LLMs. It uses a 180k+ failure pattern database to validate code logic before execution, acting as a rigorous Quality Gate to prevent hallucinations and unsafe operations.

Allows agents to lookup code snippets in your project. Uses a remote MCP server with a collection of code snippets, with support for automatic project language detection.

Vet a package before your AI coding agent uses it — authoritative facts (CVEs, license, maintenance) via an MCP server + CLI. Local, no account.