"Search engine optimization information and resources" matching MCP servers:

Provides nine specialized production-ready solvers for advanced resource allocation, network flow, and multi-objective optimization with native Monte Carlo integration. It enables users to perform constraint-based decision-making and performance analysis directly through Claude Code.

Enables solving complex combinatorial optimization problems with logical and numerical constraints through multiple solvers (Z3, CVXPY, HiGHS, OR-Tools). Specializes in portfolio optimization, scheduling, resource allocation, and constraint satisfaction problems.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

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.

Enables AI assistants to search and retrieve detailed documentation for Tracktion Engine classes and APIs.

MCP server (stdio, official Python SDK) exposing a single tool: quantity_execute. Performs dimensional calculation and unit conversion with evidence. Solves silent unit errors in LLM agent chains: 20 m/s × 3 h returns 216 km.

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Provides CSS color utilities including parsing, conversion, gamut mapping, WCAG contrast, tint/shade ramp generation, and contrast-target solving.

An AI-native product development pipeline that guides users from idea to shipping with structured research, architecture, build, validation, and traceability using 52 tools across 4 domains.

MCP server for rendering, parsing, converting, and editing Office documents (Excel, PowerPoint, Word) from a structured JSON spec, using LibreOffice WASM for conversion and preview.

Enables AI agents to audit Core Web Vitals, transform HTML for speed, generate PWAs, and export performance configurations via the Model Context Protocol.

An MCP server that provides a multidimensional philosophical framework for prompt engineering by mapping intent across ten distinct branches of thought. It generates a principled construction basis, including active constructs and spectrum tensions, to guide the creation of structured and intentional prompts.

A Model Context Protocol (MCP) server that enables AI assistants to interact with Camunda Platform workflow engine. Provides 21 specialized tools for complete workflow automation and process management.

An MCP server that gives AI agents broad control over Unreal Engine 5.7, enabling actor/asset/level management, Blueprint and material creation, screenshots, automation, and arbitrary editor Python execution.

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.

An MCP server that lets an AI agent drive Unreal Engine 5: create projects, import assets, build levels and Blueprints, configure replication, compile C++, run Play In Editor and package the game.

Enables AI agents to control Unreal Engine 5 through natural language commands, using the Model Context Protocol and Remote Control API to build levels, change assets, and trigger blueprints.

Persistent memory for AI coding assistants.