"A server for finding stories" matching MCP servers:

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

An MCP server that scans local codebases for quantum-vulnerable cryptography (secp256k1, Ed25519, RSA, etc.) and CI signing commands, classifying each finding as quantum-broken, post-quantum, or neither. It runs entirely locally with no network calls, providing a deterministic inventory for AI agents.

PCI DSS v4.0.1 compliance scanner for Go payment services, delivered as an MCP server. Every finding maps to a specific PCI DSS requirement ID. Taint-aware cardholder data flow analysis with PCI SSC FAQ semantics, designed to complement broad SAST tools like Semgrep and CodeQL - not replace them.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

Vet ClawHub skills before installing them; detects prompt-injection, exfiltration, and other security issues, outputting a risk score with per-finding evidence.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Connects MCP clients to pre-submission review that argues against a draft bug-bounty report or smart-contract finding the way a triager would, tying every claim to a supplied file and line and returning a submit, rewrite-then-submit, prove-first, hold-duplicate or drop verdict. Exposes tools to list review profiles, prepare reviews for the agent's own model, build hash-verified review packets, and — with a connection token — run hosted profiles on your own provider key.

An MCP server for interacting with the Rell Language Server Protocol interface. It enables LLMs to query LSP Hover and Completion providers for Rell projects.

An MCP server that empowers AI coding agents to work effectively with Minecraft mod development, providing static analysis of decompiled source code and runtime interaction with a running Minecraft instance.

Enables AI-assisted X++ development for Dynamics 365 Finance and Operations by pre-indexing the entire codebase and providing 54 specialized tools for metadata lookup, code generation, and best practice validation.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

SkillTotal is a local stdio MCP server that lets an agent check a component before installing it. It statically analyzes MCP servers, agent skills, npm/PyPI packages and repos for supply-chain risks, dangerous capabilities (shell, filesystem, network, credentials), prompt-injection surfaces like tool poisoning, and exfiltration paths. Each finding cites file, line and snippet.

A code review tool server based on Model Context Protocol (MCP) providing multi-dimensional code review and scoring.

locidx

Enables users to scan a website, project folder or HTML file for generic AI-generated copy, template design and unfinished details, scoring each finding with file-and-line evidence and a per-page breakdown. It turns the results into phased, step-by-step repair plans that Claude, Cursor and other AI tools can apply, then re-measures the score after each change.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.

Provides AI assistants with frontend development tools including component scaffolding, bundle analysis, accessibility checks, and responsive design guidance. Enables automated generation of React components with tests and stories, bundle optimization recommendations, and WCAG compliance fixes.

Enables AI coding assistants to perform local semantic code search using AST parsing and embeddings, finding code by meaning with exact file and line locations, plus AST-based usage lookup.