"A scheduling engine powered by AI" matching MCP servers:

Graph RAG based context management engine for coding agents

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Provides AI agents with 200 deterministic micro-services covering code auditing, 3D/robotics, media processing, cryptography, blockchain settlement, AI safety, networking, and math, with automatic x402 USDC micropayments.

Audits AI agent skills for safety using static, semantic, adversarial, and supply-chain analysis, providing scores and risk flags. Can be run via CLI, CI, or as an MCP tool from Claude Code, Cursor, and Codex.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

A local code-intelligence engine for AI agents that indexes repositories into a PostgreSQL-backed code graph and serves structured, token-budgeted context over MCP and HTTP, enabling targeted queries on symbols, dependencies, contracts, and impact analysis.

Enables AI agents to control Cheat Engine for memory reading/writing, scanning, debugging, and .NET/Mono game reverse engineering through 43 Chinese-language MCP tools, with thread-safe named-pipe communication.

A local-first, agent-agnostic MCP server that provides semantic search, persistent memory, and automated code review capabilities for development workflows. It leverages the Auggie SDK to offer advanced tools for codebase indexing, implementation planning, and deterministic static analysis.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

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.

Enables comprehensive project analysis, intelligent search with regex, multi-file editing with automatic backups, and dependency mapping, all via natural language.

Enables MCP clients to index code repositories and run AST-aware semantic and hybrid keyword search across files, commits and branches, returning relevant code snippets with expanded file context. It handles incremental re-indexing with local embeddings and Qdrant storage, and can serve either over stdio or as a bearer-token-protected HTTP endpoint for remote or containerized use.

Enables querying a TypeScript repository's structure (symbols, imports/exports, references) through direct queries, avoiding repetitive grep operations.

Provides safe, read-only access to memory analysis and debugging functionality through the Model Context Protocol, allowing users to examine computer memory for software development, security research, and educational purposes.

Enables AI coding assistants to ground on real D365 F&O AOT metadata locally and offline, preventing hallucinated X++ field names and Chain of Command signatures.

Provides AI agents with architecture decision records, project conventions, and real codebase usage data so they can understand why, when, and where to use design system components.

Regex AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

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.

Enables AI agents to build a structured model of a Python codebase and query it over stdio JSON-RPC: dependency-mapped component stats, ranked blast-radius predictions for a target file, natural-language change planning with suggested tests, and design decisions mined from git history.