"Metallurgical Engineering and Steel Plant Processing Information" matching MCP servers:

Enables querying a local health knowledge base with hybrid RAG (vector + FTS5) and exploring backlinks between notes.

A unified MCP Gateway for enterprise knowledge, code, and memory tools, featuring admin management for documents, accounts, and MCP keys, with local network deployment support.

Enables agents to run hybrid retrieval, build token-budgeted attributed context packs, answer questions with citation and groundedness-gated release, explore temporal graph entities, evaluate claim support, and ingest screened, embedded documents through typed MCP tools.

A Model Context Protocol server that enables LLMs to automate web browsers using Puppeteer, allowing navigation, screenshots, form filling, and JavaScript execution in a real browser environment.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Processes emails from Outlook with date filtering, storing them in SQLite databases while generating vector embeddings for semantic search capabilities in MongoDB.

A development platform that helps AI developers optimize prompts through intelligent context management, providing tools for analysis, optimization, and template management to reduce costs and improve AI response quality.

A Model Context Protocol server that helps users create, validate, manage, and optimize prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).

Enables users to search curated motion recipes and prepare deterministic, customized prompts for coding agents to implement animations, product films, explainers, and procedural scenes in their projects. It returns instructions and requirements rather than calling a model API or rendering video.

Enables AI coding sessions to store consented facts, edges, and provenance in a local SQLite graph and recall only the relevant connected neighborhood needed to resume work. It preserves decisions, constraints, and failures across chats without replaying entire transcripts.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

Provides structured dealer brand data including inventory, promotions, reviews, and dealer profile via MCP tools, enabling LLMs to answer accurate brand-related queries.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Enables AI agents to search, fetch, and apply portable engineering standards over MCP, keyless and offline.

Enables interaction with Google's Gemini AI models including file uploads, conversation management, and batch API processing for large-scale tasks at reduced costs. Supports multiple Gemini models with advanced features like embeddings generation and automated workflow processing.

DevsContext is an MCP server that provides AI coding agents with synthesized engineering context—requirements, decisions, architecture, and standards—from tools like Jira and Slack. It fetches and synthesizes relevant information on demand to help agents work on tasks correctly.

A single MCP server that merges three context-engineering ideas into one toolset for AI agents

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

🔍 A Model Context Protocol (MCP) server providing unified access to multiple search engines (Tavily, Brave, Kagi), AI tools (Perplexity, FastGPT), and content processing services (Jina AI, Kagi). Combines search, AI responses, content processing, and enhancement features through a single interface.

Enables integration with Ahrefs SEO tools and services through MCP protocol. Provides automated exception handling, comprehensive logging, and parallel processing capabilities for SEO data analysis and operations.

Entroly is a high-performance context engineering engine that optimizes AI coding agent context windows through information-theoretic compression and reinforcement learning. It gives your AI 100% codebase visibility while reducing token consumption by an average of 78%.It's learns fully autonomously.

Provides database interaction through natural language, enabling query execution and content processing.

An MCP server that enables AI agents to interact with Extend's spend management APIs, allowing virtual card management, transaction tracking, and receipt processing through natural language.

Enables AI coding tools and developers to store, search, and supersede durable engineering discoveries with temporal validity, and to coordinate active work across sessions via MCP.

Enables KoboldCpp local models to index and semantically query private documents from multiple file formats via MCP, with privacy-first local processing and zero data leakage.